From f86c2f6c5a361b466e1a5018e28d69ce8422003c Mon Sep 17 00:00:00 2001 From: Jae-Seung Yeom Date: Sun, 4 Oct 2026 21:51:36 -0700 Subject: [PATCH 1/3] Add Lassen trace conversion and data-driven synthetic job generation - convert Lassen CSM pbatch records to Unix epoch timestamps - resolve Daylight Saving Time ordering anomalies - generate simulation traces with duration in place of begin/end times - add configurable filtering and empirical sampling for synthetic jobs - document input schemas, outputs, sampling logic, and usage --- experimental/synthesize/lassen/README.md | 211 +++++++++++++++++ .../lassen/convert_job_stream_to_epoch.py | 223 ++++++++++++++++++ .../lassen/generate_synthetic_job_stream.py | 215 +++++++++++++++++ 3 files changed, 649 insertions(+) create mode 100644 experimental/synthesize/lassen/README.md create mode 100755 experimental/synthesize/lassen/convert_job_stream_to_epoch.py create mode 100755 experimental/synthesize/lassen/generate_synthetic_job_stream.py diff --git a/experimental/synthesize/lassen/README.md b/experimental/synthesize/lassen/README.md new file mode 100644 index 0000000..735a5e1 --- /dev/null +++ b/experimental/synthesize/lassen/README.md @@ -0,0 +1,211 @@ +# Job trace conversion and synthetic trace generation + +This directory contains two scripts: + +1. `convert_job_stream_to_epoch.py` extracts Lassen `pbatch` jobs from the raw + CSM allocation history, converts local timestamps to Unix epoch seconds, + and can calculate job duration. +2. `generate_synthetic_job_stream.py` accepts a standardized historical job + trace and generates a synthetic trace by sampling arrival times, job + size/runtime pairs, and time limits separately. + +Both scripts use Python's standard library and require Python 3.9 or newer. + +> **Scope of the two scripts:** `convert_job_stream_to_epoch.py` is a +> Lassen-specific adapter. It depends on Lassen CSM column names, selects the +> `pbatch` queue, interprets local timestamps in `America/Los_Angeles`, and +> uses the trace's approximate begin-time ordering to resolve the repeated +> Daylight Saving Time (DST) hour. The physical order of CSV columns does not +> matter because columns are +> read by header name. In contrast, `generate_synthetic_job_stream.py` is the +> general-purpose component: it can process any job trace that provides the +> standardized columns documented below. + +## 1. Convert the Lassen CSM allocation history + +### Expected input + +`convert_job_stream_to_epoch.py` is the Lassen-specific input adapter. Its +input is a CSV file such as `final_csm_allocation_history_hashed.csv`. Column +order does not matter, and additional columns are allowed, but these named +columns must exist: + +| Column | Meaning | +| --- | --- | +| `job_submit_time` | Local job submission timestamp | +| `num_nodes` | Number of requested/allocated nodes | +| `begin_time` | Local execution start timestamp | +| `end_time` | Local execution end timestamp | +| `time_limit` | Job time limit in seconds | +| `exit_status` | Job exit status; zero represents success | +| `queue` | Queue name used to select `pbatch` jobs | + +The timestamp fields are expected to resemble +`2020-11-01 01:07:31.70215` and must not contain a UTC offset. By default they +are interpreted in `America/Los_Angeles`. + +The input should be approximately ordered by `begin_time`. This allows the +script to distinguish the first and second occurrence of times such as 01:30 +when daylight saving time ends. Small out-of-order differences are tolerated. + +### Processing logic + +The script performs the CSV-aware equivalent of the original `awk` command: + +- Select rows whose `queue` is exactly `pbatch`. +- Select and rename `job_submit_time` to `submit_time`. +- Retain `num_nodes`, `begin_time`, `end_time`, `time_limit`, and + `exit_status`. +- Parse CSV quoting instead of deleting quote characters blindly. +- Convert local timestamps to Unix epoch seconds. +- Resolve the repeated fall-back hour so that + `submit_time < begin_time < end_time`. +- Preserve fractional seconds without floating-point rounding. + +### Outputs + +The required output contains: + +```text +submit_time,num_nodes,begin_time,end_time,time_limit,exit_status +``` + +With `--simulation-output`, a second file is produced containing: + +```text +submit_time,num_nodes,duration,time_limit,exit_status +``` + +Here, `duration = end_time - begin_time`, in seconds. Timestamps are Unix +epoch seconds, and values may include a fractional part. + +### Generate the standardized historical traces + +```bash +./convert_job_stream_to_epoch.py \ + final_csm_allocation_history_hashed.csv \ + lassen_pbatch_job_stream_epoch.csv \ + --simulation-output lassen_pbatch_scheduling_simulation.csv +``` + +Use a different source timezone if necessary: + +```bash +./convert_job_stream_to_epoch.py input.csv output.csv \ + --timezone America/Los_Angeles \ + --simulation-output simulation_input.csv +``` + +## 2. Generate a synthetic job trace + +> **Data-driven sampling:** This approach resamples directly from observed job +> records instead of assuming an arbitrary parametric distribution (for +> example, normal, exponential, or log-normal). As a result, the generated +> values follow the empirical behavior present in the historical data, +> including irregular shapes, heavy tails, and repeated values. The method +> preserves historical arrival patterns, keeps `num_nodes` paired with +> `duration`, and samples `time_limit` conditionally on the duration bucket. +> These independently sampled components intentionally do not preserve every +> possible correlation from the original trace. + +### Expected input: a general historical job trace + +`generate_synthetic_job_stream.py` is not tied to Lassen. It accepts a general +CSV job trace with the following named columns. Column order does not matter, +and extra columns are ignored. + +| Column | Requirement | +| --- | --- | +| `submit_time` | Numeric timestamp, normally Unix epoch seconds | +| `num_nodes` | Number of nodes associated with the job | +| `duration` | Positive runtime in seconds; fractions are supported | +| `time_limit` | Time-limit value to sample | +| `exit_status` | Required only when `--successful-only` is used; zero means success | + +The generated `lassen_pbatch_scheduling_simulation.csv` is directly usable as +this input. For another trace format, rename or derive its columns to match the +table above. If it contains start/end timestamps rather than duration, compute +`duration = end_time - begin_time` first. Use consistent units: duration and +time limit should both be in seconds. + +The script sorts eligible input jobs by numeric `submit_time`; the original +CSV does not need to be pre-sorted. + +### Eligibility filters + +- `--min-duration SECONDS` removes jobs whose duration is less than the given + value. A job exactly equal to the threshold remains eligible. +- `--successful-only` removes jobs whose `exit_status` is not `0`. +- Without these options, all positive-duration jobs are eligible and the + `exit_status` column is optional. + +The same eligible population is used by all three sampling stages. + +### Synthetic sampling logic + +For a requested count of `N` jobs, the script does the following: + +1. **Submission times:** Uniformly select a valid starting index, take `N` + consecutive eligible jobs in submit-time order, and retain only their + `submit_time` values. This preserves a real historical arrival pattern and + its interarrival gaps. +2. **Node count and duration:** Independently sample `N` eligible jobs + uniformly from the whole eligible trace. Keep each selected job's + `(num_nodes, duration)` pair together, preserving the historical + relationship between job size and runtime. Sampling is without replacement + by default; `--with-replacement` enables bootstrap sampling. +3. **Time limit:** Assign every eligible historical job to a one-second bucket + using `ceil(duration)`. Thus `(0, 1]` is bucket 1, `(1, 2]` is bucket 2, + and so on. For each synthetic job, uniformly select a `time_limit` from the + historical records in the same duration bucket. Repeated historical values + remain repeated in the bucket and retain their empirical frequency. + +The three stages are independent except that `num_nodes` and `duration` remain +paired and the time-limit bucket is selected from the sampled duration. + +### Expected output + +```text +submit_time,num_nodes,time_limit,duration +``` + +The output has one header row followed by exactly `N` synthetic jobs. + +### Generate one trace + +The following creates 100,000 jobs using successful historical jobs that ran +for at least 60 seconds: + +```bash +./generate_synthetic_job_stream.py \ + lassen_pbatch_scheduling_simulation.csv \ + synthetic_jobs_100000_min60s_successful.csv \ + 100000 \ + --min-duration 60 \ + --successful-only +``` + +For reproducible results, provide a seed: + +```bash +./generate_synthetic_job_stream.py historical.csv synthetic.csv 100000 \ + --min-duration 60 --successful-only --seed 2026 +``` + +Running the same command with the same input and seed produces identical +output. Without `--seed`, Python uses system randomness. + +### Generate ten independent traces + +```bash +for i in $(seq -w 1 10); do + ./generate_synthetic_job_stream.py \ + lassen_pbatch_scheduling_simulation.csv \ + "synthetic_jobs_100000_min60s_successful_${i}.csv" \ + 100000 \ + --min-duration 60 \ + --successful-only +done +``` + +Because no seed is supplied, each invocation uses fresh system randomness. diff --git a/experimental/synthesize/lassen/convert_job_stream_to_epoch.py b/experimental/synthesize/lassen/convert_job_stream_to_epoch.py new file mode 100755 index 0000000..6873b63 --- /dev/null +++ b/experimental/synthesize/lassen/convert_job_stream_to_epoch.py @@ -0,0 +1,223 @@ +#!/usr/bin/env python3 +"""Extract pbatch jobs from CSM allocation history and use Unix timestamps. + +The input is expected to be in approximately increasing begin_time order, as +final_csm_allocation_history_hashed.csv is. That ordering lets this script +distinguish the two occurrences of 01:xx during the autumn DST transition. +""" + +import argparse +import csv +import sys +from contextlib import ExitStack +from datetime import datetime, timezone +from itertools import product +from pathlib import Path +from zoneinfo import ZoneInfo, ZoneInfoNotFoundError + + +OUTPUT_COLUMNS = ( + "submit_time", + "num_nodes", + "begin_time", + "end_time", + "time_limit", + "exit_status", +) +SIMULATION_COLUMNS = ( + "submit_time", + "num_nodes", + "duration", + "time_limit", + "exit_status", +) +SOURCE_COLUMNS = ( + "job_submit_time", + "num_nodes", + "begin_time", + "end_time", + "time_limit", + "exit_status", + "queue", +) +EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc) + + +def epoch_us(aware_datetime): + delta = aware_datetime.astimezone(timezone.utc) - EPOCH + return ( + delta.days * 86_400_000_000 + + delta.seconds * 1_000_000 + + delta.microseconds + ) + + +def local_candidates(value, zone): + """Return valid epoch-microsecond interpretations of a local timestamp.""" + naive = datetime.fromisoformat(value) + if naive.tzinfo is not None: + raise ValueError("timestamps must not contain a UTC offset") + + candidates = set() + for fold in (0, 1): + aware = naive.replace(tzinfo=zone, fold=fold) + # The round trip rejects nonexistent times in the spring DST gap. + if aware.astimezone(timezone.utc).astimezone(zone).replace(tzinfo=None) == naive: + candidates.add(epoch_us(aware)) + if not candidates: + raise ValueError(f"nonexistent local time: {value}") + return sorted(candidates) + + +def format_epoch(microseconds): + seconds, fraction = divmod(microseconds, 1_000_000) + return str(seconds) if fraction == 0 else f"{seconds}.{fraction:06d}".rstrip("0") + + +def resolve_row(row, zone, previous_begin, order_slack_us): + submit_options = local_candidates(row["submit_time"], zone) + begin_options = local_candidates(row["begin_time"], zone) + end_options = local_candidates(row["end_time"], zone) + + # During a repeated hour, prefer the earliest begin-time interpretation + # that does not substantially move backward in this approximately ordered + # stream. A reset such as 01:59 -> 01:05 therefore selects fold=1. + if previous_begin is not None: + ordered = [b for b in begin_options if b >= previous_begin - order_slack_us] + begin = min(ordered) if ordered else min(begin_options) + else: + begin = min(begin_options) + + # Pick interpretations adjacent to begin. This minimizes queue/runtime + # duration while enforcing the required strict ordering. + choices = [ + (s, begin, e) + for s, e in product(submit_options, end_options) + if s < begin < e + ] + if not choices: + raise ValueError( + "cannot resolve submit_time < begin_time < end_time: " + f"{row['submit_time']}, {row['begin_time']}, {row['end_time']}" + ) + submit, begin, end = min( + choices, key=lambda values: (values[1] - values[0], values[2] - values[1]) + ) + return submit, begin, end + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "input_csv", + help="CSM allocation history CSV, e.g. final_csm_allocation_history_hashed.csv", + ) + parser.add_argument("output_csv", help="six-column pbatch job-stream CSV") + parser.add_argument( + "--simulation-output", + metavar="CSV", + help="also write a scheduling-simulation CSV with duration instead of begin/end", + ) + parser.add_argument("--timezone", default="America/Los_Angeles") + parser.add_argument( + "--order-slack-seconds", + type=float, + default=60.0, + help="small begin-time regressions tolerated before detecting a DST reset (default: 60)", + ) + args = parser.parse_args() + + try: + zone = ZoneInfo(args.timezone) + except ZoneInfoNotFoundError as exc: + parser.error(str(exc)) + + previous_begin = None + rows_written = 0 + input_path = Path(args.input_csv).resolve() + output_path = Path(args.output_csv).resolve() + simulation_path = ( + Path(args.simulation_output).resolve() if args.simulation_output else None + ) + if output_path == input_path or simulation_path == input_path: + parser.error("output files must not overwrite the input CSV") + if simulation_path == output_path: + parser.error("output_csv and --simulation-output must be different files") + + with ExitStack() as stack: + source = stack.enter_context( + open(args.input_csv, newline="", encoding="utf-8") + ) + destination = stack.enter_context( + open(args.output_csv, "w", newline="", encoding="utf-8") + ) + simulation_destination = None + if args.simulation_output: + simulation_destination = stack.enter_context( + open(args.simulation_output, "w", newline="", encoding="utf-8") + ) + + reader = csv.DictReader(source) + if reader.fieldnames is None: + parser.error("input CSV has no header") + missing = set(SOURCE_COLUMNS) - set(reader.fieldnames) + if missing: + parser.error("missing columns: " + ", ".join(sorted(missing))) + + writer = csv.DictWriter(destination, fieldnames=OUTPUT_COLUMNS) + writer.writeheader() + simulation_writer = None + if simulation_destination is not None: + simulation_writer = csv.DictWriter( + simulation_destination, fieldnames=SIMULATION_COLUMNS + ) + simulation_writer.writeheader() + for line_number, source_row in enumerate(reader, 2): + # This is the CSV-aware equivalent of the original awk filter and + # field selection. csv.DictReader also removes surrounding quotes. + if source_row["queue"] != "pbatch": + continue + row = { + "submit_time": source_row["job_submit_time"], + "num_nodes": source_row["num_nodes"], + "begin_time": source_row["begin_time"], + "end_time": source_row["end_time"], + "time_limit": source_row["time_limit"], + "exit_status": source_row["exit_status"], + } + try: + submit, begin, end = resolve_row( + row, + zone, + previous_begin, + int(args.order_slack_seconds * 1_000_000), + ) + except (KeyError, ValueError) as exc: + raise SystemExit(f"{args.input_csv}:{line_number}: {exc}") from exc + + row["submit_time"] = format_epoch(submit) + row["begin_time"] = format_epoch(begin) + row["end_time"] = format_epoch(end) + writer.writerow(row) + if simulation_writer is not None: + simulation_writer.writerow( + { + "submit_time": row["submit_time"], + "num_nodes": row["num_nodes"], + "duration": format_epoch(end - begin), + "time_limit": row["time_limit"], + "exit_status": row["exit_status"], + } + ) + previous_begin = begin + rows_written += 1 + + print(f"wrote {rows_written} rows to {args.output_csv}", file=sys.stderr) + if args.simulation_output: + print( + f"wrote {rows_written} rows to {args.simulation_output}", file=sys.stderr + ) + + +if __name__ == "__main__": + main() diff --git a/experimental/synthesize/lassen/generate_synthetic_job_stream.py b/experimental/synthesize/lassen/generate_synthetic_job_stream.py new file mode 100755 index 0000000..3765e69 --- /dev/null +++ b/experimental/synthesize/lassen/generate_synthetic_job_stream.py @@ -0,0 +1,215 @@ +#!/usr/bin/env python3 +"""Generate a synthetic scheduling trace from a historical job trace. + +The input must contain these columns: + + submit_time,num_nodes,duration,time_limit,exit_status + +The output contains: + + submit_time,num_nodes,time_limit,duration + +The four output values are assembled in three deliberately separate sampling +steps. See ``generate_jobs`` below for the detailed process and rationale. +""" + +import argparse +import csv +import random +import sys +from collections import defaultdict +from decimal import Decimal, InvalidOperation, ROUND_CEILING +from pathlib import Path + + +REQUIRED_COLUMNS = { + "submit_time", + "num_nodes", + "duration", + "time_limit", + "exit_status", +} +OUTPUT_COLUMNS = ("submit_time", "num_nodes", "time_limit", "duration") + + +def decimal_value(text, field, line_number): + """Parse a finite numeric field without losing fractional precision.""" + try: + value = Decimal(text) + except InvalidOperation as exc: + raise ValueError(f"line {line_number}: invalid {field}: {text!r}") from exc + if not value.is_finite(): + raise ValueError(f"line {line_number}: non-finite {field}: {text!r}") + return value + + +def duration_bucket(duration): + """Map (0, 1] to bucket 1, (1, 2] to bucket 2, and so forth.""" + if duration <= 0: + raise ValueError(f"duration must be greater than zero, got {duration}") + return int(duration.to_integral_value(rounding=ROUND_CEILING)) + + +def read_eligible_jobs(path, minimum_duration, successful_only): + """Read the trace in source order and apply both optional filters.""" + eligible = [] + with open(path, newline="", encoding="utf-8") as source: + reader = csv.DictReader(source) + if reader.fieldnames is None: + raise ValueError("input CSV has no header") + missing = REQUIRED_COLUMNS - set(reader.fieldnames) + if missing: + raise ValueError("missing columns: " + ", ".join(sorted(missing))) + + for line_number, row in enumerate(reader, 2): + duration = decimal_value(row["duration"], "duration", line_number) + if duration <= 0: + raise ValueError( + f"line {line_number}: duration must be greater than zero" + ) + # "Ignore jobs shorter than X" means a job of exactly X seconds + # remains eligible. + if duration < minimum_duration: + continue + if successful_only and row["exit_status"].strip() != "0": + continue + + eligible.append( + { + "submit_time": row["submit_time"], + "num_nodes": row["num_nodes"], + "duration": row["duration"], + "duration_decimal": duration, + "time_limit": row["time_limit"], + } + ) + return eligible + + +def generate_jobs(eligible, count, rng, with_replacement=False): + """Construct synthetic jobs using the requested independent sampling. + + 1. Choose one uniformly random starting index and copy ``count`` + consecutive submit times from the eligible trace. Keeping a contiguous + window preserves the historical arrival pattern and interarrival gaps. + + 2. Independently sample ``count`` historical jobs uniformly, and retain + each selected job's (num_nodes, duration) pair. The pair stays intact + so the observed relationship between job size and runtime is preserved. + + 3. Put every eligible historical job into ceil(duration) one-second + buckets: (0,1] is bucket 1, (1,2] is bucket 2, etc. For each sampled + duration, independently choose a time_limit uniformly from historical + jobs in the same bucket. Sampling records rather than distinct limit + values preserves the empirical frequency of repeated time limits. + """ + if count <= 0: + raise ValueError("number of jobs must be greater than zero") + if count > len(eligible): + raise ValueError( + f"requested {count} jobs, but only {len(eligible)} are eligible" + ) + + # Stage 1: the window contains consecutive *eligible* jobs. If filtering + # removes rows, the remaining submit times retain their original order. + start = rng.randrange(len(eligible) - count + 1) + submit_times = [job["submit_time"] for job in eligible[start : start + count]] + + # Stage 2: sampling is without replacement by default because the request + # is to pick N eligible historical jobs. --with-replacement enables + # bootstrap-style resampling when repeated pairs are desirable. + if with_replacement: + pair_samples = [rng.choice(eligible) for _ in range(count)] + else: + pair_samples = rng.sample(eligible, count) + + # Build buckets from all eligible jobs, not merely from Stage 2's sample. + limits_by_bucket = defaultdict(list) + for job in eligible: + bucket = duration_bucket(job["duration_decimal"]) + limits_by_bucket[bucket].append(job["time_limit"]) + + synthetic = [] + for submit_time, pair in zip(submit_times, pair_samples): + bucket = duration_bucket(pair["duration_decimal"]) + synthetic.append( + { + "submit_time": submit_time, + "num_nodes": pair["num_nodes"], + "time_limit": rng.choice(limits_by_bucket[bucket]), + "duration": pair["duration"], + } + ) + return synthetic, start + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("input_csv", help="historical scheduling trace CSV") + parser.add_argument("output_csv", help="synthetic trace to create") + parser.add_argument("num_jobs", type=int, help="number of synthetic jobs") + parser.add_argument( + "--min-duration", + default="0", + metavar="SECONDS", + help="ignore jobs shorter than this duration (default: 0)", + ) + parser.add_argument( + "--successful-only", + action="store_true", + help="use only jobs whose exit_status is 0", + ) + parser.add_argument( + "--seed", + type=int, + help="random seed for exactly reproducible output", + ) + parser.add_argument( + "--with-replacement", + action="store_true", + help="allow repeated (num_nodes, duration) samples", + ) + args = parser.parse_args() + + try: + minimum_duration = Decimal(args.min_duration) + except InvalidOperation as exc: + parser.error(f"invalid --min-duration: {args.min_duration!r}") + if not minimum_duration.is_finite() or minimum_duration < 0: + parser.error("--min-duration must be a finite, nonnegative number") + if args.num_jobs <= 0: + parser.error("num_jobs must be greater than zero") + + input_path = Path(args.input_csv).resolve() + output_path = Path(args.output_csv).resolve() + if input_path == output_path: + parser.error("output_csv must not overwrite input_csv") + + try: + eligible = read_eligible_jobs( + args.input_csv, minimum_duration, args.successful_only + ) + synthetic, window_start = generate_jobs( + eligible, + args.num_jobs, + random.Random(args.seed), + args.with_replacement, + ) + except (OSError, ValueError) as exc: + raise SystemExit(f"error: {exc}") from exc + + with open(args.output_csv, "w", newline="", encoding="utf-8") as destination: + writer = csv.DictWriter(destination, fieldnames=OUTPUT_COLUMNS) + writer.writeheader() + writer.writerows(synthetic) + + seed_text = "system randomness" if args.seed is None else str(args.seed) + print( + f"wrote {len(synthetic)} jobs to {args.output_csv}; " + f"eligible={len(eligible)}, submit-window-start={window_start}, seed={seed_text}", + file=sys.stderr, + ) + + +if __name__ == "__main__": + main() From 3a0ca7ebc0da618f3efbf7b639ee8b04e1352c16 Mon Sep 17 00:00:00 2001 From: Jae-Seung Yeom Date: Mon, 5 Oct 2026 00:25:33 -0700 Subject: [PATCH 2/3] Add wall-time-aware multi-cluster prediction study - treat the application requirements table as a workload allowlist - exclude applications absent from apps.csv during table generation and dispatch - retain all compatible measured modes in ground-truth performance tables - allow prediction tables to have different mode coverage from ground truth - generate application-average predictions for measured modes - generate RAJAPerf predictions for every system mode - filter systems whose actual runtime exceeds the maximum wall-time limit - prefer any feasible system over infeasible systems regardless of queue wait - double underestimated predicted time limits until they cover actual runtime - cap submitted limits at the configured platform maximum - drop and report jobs when no system can complete them within the maximum - record submitted limits, doubling counts, and dropped-job totals - document the optimistic assumption that failed limit attempts consume no resources - add a three-policy prediction-study runner with validated completion markers - summarize ideal, application-average, and RAJAPerf results across ten traces - relocate production tables and configuration under experimental/multi-cluster - move dispatcher test fixtures into a dedicated testdata directory - source generated traces from experimental/synthesize/lassen/synthetic_traces - ignore generated traces and prediction-study results - emit generated CSV traces with Unix line endings - add Python and MPI regression coverage for allowlists, wall-time correction, infeasible placement, dropped jobs, and prediction coverage --- .gitignore | 2 + experimental/multi-cluster/README.md | 114 ++-- experimental/multi-cluster/apps.csv | 7 + .../multi-cluster/build_performance_tables.py | 79 +-- .../build_prediction_baselines.py | 9 +- experimental/multi-cluster/ground_truth.csv | 643 +++++++++++++++++- .../grpc_performance_dispatch.py | 115 +++- .../mpi_performance_dispatch.cpp | 125 +++- .../multi-cluster/prediction.app_avg.csv | 638 +++++++++++++++++ experimental/multi-cluster/prediction.csv | 643 +++++++++++++++++- .../multi-cluster/prediction.rajaperf.csv | 638 +++++++++++++++++ .../multi-cluster/run_prediction_study.py | 318 +++++++++ .../test_performance_dispatch.py | 167 ++++- .../{ => testdata}/applications.csv | 0 .../multi-cluster/testdata/ground_truth.csv | 7 + .../{ => testdata}/job_stream.csv | 0 .../multi-cluster/testdata/machines.csv | 6 + .../multi-cluster/testdata/prediction.csv | 7 + .../lassen/generate_synthetic_job_stream.py | 4 +- tests/test_mpi_performance_dispatch.py | 41 +- 20 files changed, 3383 insertions(+), 180 deletions(-) create mode 100644 experimental/multi-cluster/apps.csv create mode 100644 experimental/multi-cluster/prediction.app_avg.csv create mode 100644 experimental/multi-cluster/prediction.rajaperf.csv create mode 100644 experimental/multi-cluster/run_prediction_study.py rename experimental/multi-cluster/{ => testdata}/applications.csv (100%) create mode 100644 experimental/multi-cluster/testdata/ground_truth.csv rename experimental/multi-cluster/{ => testdata}/job_stream.csv (100%) create mode 100644 experimental/multi-cluster/testdata/machines.csv create mode 100644 experimental/multi-cluster/testdata/prediction.csv diff --git a/.gitignore b/.gitignore index 05e9c5d..dfc2f17 100644 --- a/.gitignore +++ b/.gitignore @@ -81,3 +81,5 @@ python/dr_evt_market/lc_traces/ # Local multi-cluster experiment inputs and generated results /multi-cluster/ +/experimental/synthesize/lassen/synthetic_traces/ +/experimental/multi-cluster/prediction-study-results/ diff --git a/experimental/multi-cluster/README.md b/experimental/multi-cluster/README.md index 060a6c6..9c635ba 100644 --- a/experimental/multi-cluster/README.md +++ b/experimental/multi-cluster/README.md @@ -52,7 +52,7 @@ testdfft,CPU-only The accepted values are `CPU-only`, `GPU-only`, and `GPU-portable`. Every application in the performance tables must occur exactly once. The requirement is -application-specific, not job-stream-specific. `multi-cluster/apps.csv` is a +application-specific, not job-stream-specific. `experimental/multi-cluster/apps.csv` is a runtime input; the `#` prefix on its first header is accepted. ### Systems @@ -66,7 +66,7 @@ tuolumne,256,GPU-enabled matrix,26,GPU-enabled ``` -`multi-cluster/machines.csv` is a runtime input. Its columns are machine name, +`experimental/multi-cluster/machines.csv` is a runtime input. Its columns are machine name, physical node count, and machine type (`CPU-only` or `GPU-enabled`); the `#` prefix on the first header is accepted. The size is the worker simulation's schedulable capacity. Because background jobs are not modeled, this represents @@ -97,12 +97,15 @@ amg,-problem1-p442-n12812864,32,0.9821,1.0446,1.0943,,1.2496,,2.2508, `--ground-truth` supplies measured speedups and `--prediction` supplies values available to the dispatch policy. Values are positive, dimensionless speedups relative to the job stream's `duration`: a speedup of `2.0` means half that -duration. The two files must have identical `(App, Args, Ranks)` identities and -missing-value patterns. They need no named Quartz column, but must use the same -duration reference. +duration. The two files must have identical `(App, Args, Ranks)` identities, +but their missing-value patterns may differ. A mode is dispatchable only when +both its ground-truth and prediction cells are present. They need no named +Quartz column, but must use the same duration reference. Rows without a usable measurement for any configured compatible system are reported and omitted. Duplicate `(app, args, ranks)` rows are rejected. +Applications absent from the application-to-requirement table are omitted, +allowing that table to act as an application sampling allowlist. Generate the production table by joining the prediction matrix to measured target runtimes: @@ -112,40 +115,40 @@ source docs/venv/bin/activate python3 experimental/multi-cluster/build_performance_tables.py \ --predictions multi-cluster/relative_runtime_matrix_quartz_new.csv \ --measurements multi-cluster/merged.txt \ - --applications multi-cluster/apps.csv \ - --systems multi-cluster/machines.csv \ - --ground-truth-output multi-cluster/ground_truth.csv \ - --prediction-output multi-cluster/prediction.csv + --applications experimental/multi-cluster/apps.csv \ + --systems experimental/multi-cluster/machines.csv \ + --ground-truth-output experimental/multi-cluster/ground_truth.csv \ + --prediction-output experimental/multi-cluster/prediction.csv ``` The join key is `(app, args, ranks)` after lowercasing the application and removing whitespace and case differences from `args`; commas inside quoted -argument fields remain part of the argument. For each workload, the converter -uses a prediction row containing all available target modes and prefers the row -with the most measured reference systems. It normalizes measured runtimes and -predictions to the first measured non-dispatch reference available in table -order (normally Borax), falling back to a compatible target mode only when -needed. Quartz data is not required. In `merged.txt`, the unsuffixed `matrix`, -`tioga`, and `tuolumne` measurements represent their GPU modes; their `-cpu` -rows represent CPU modes. +argument fields remain part of the argument. Ground truth retains every +compatible target-mode measurement and normalizes it to the first measured +non-dispatch reference in table order (normally Borax), falling back to a +compatible target mode only when needed. Model predictions are emitted only +when they can use that same reference, so missing model coverage never removes +ground-truth measurements. Quartz data is not required. In `merged.txt`, the +unsuffixed `matrix`, `tioga`, and `tuolumne` measurements represent their GPU +modes; their `-cpu` rows represent CPU modes. Generate two interchangeable prediction baselines: ```bash source docs/venv/bin/activate python3 experimental/multi-cluster/build_prediction_baselines.py \ - --ground-truth multi-cluster/ground_truth.csv \ + --ground-truth experimental/multi-cluster/ground_truth.csv \ --machine-rep multi-cluster/machine_rep.txt \ - --rajaperf-output multi-cluster/prediction.rajaperf.csv \ - --app-avg-output multi-cluster/prediction.app_avg.csv + --rajaperf-output experimental/multi-cluster/prediction.rajaperf.csv \ + --app-avg-output experimental/multi-cluster/prediction.app_avg.csv ``` The RAJAPerf baseline computes `borax_time / machine_time` for every shared kernel position, averages those ratios per execution mode, and assigns that -machine-level value to every workload where the mode is available. Repeated -kernel names remain distinct positional samples. The application-average -baseline assigns the arithmetic mean ground-truth speedup for each -`(App, Ranks, execution mode)` group. +machine-level value to every workload and system mode. Repeated kernel names +remain distinct positional samples. The application-average baseline assigns +the arithmetic mean ground-truth speedup for each `(App, Ranks, execution +mode)` group and retains the workload's measured-mode coverage. #### Optional plotting @@ -158,9 +161,9 @@ does not refer to the simulator's `run_time_mode` option: ```bash source docs/venv/bin/activate python3 experimental/multi-cluster/plot_relative_performance.py \ - --ground-truth multi-cluster/ground_truth.csv \ - --prediction multi-cluster/prediction.csv \ - --output multi-cluster/relative-performance.png + --ground-truth experimental/multi-cluster/ground_truth.csv \ + --prediction experimental/multi-cluster/prediction.csv \ + --output experimental/multi-cluster/relative-performance.png ``` The default logarithmic axes make both sub-unit and large speedups visible. @@ -207,13 +210,27 @@ actual_time_limit = time_limit / ground_truth_relative_performance predicted_turnaround = estimated_wait + estimated_duration ``` -The smallest predicted turnaround wins; ties use estimated wait and then -systems-table order. The wait estimate uses the predicted time limit. Only the -selected worker receives the job, with its ground-truth-scaled time limit and -actual duration. Thus prediction error affects placement, while the resulting -queue state reflects the workload's realized performance. The next arrival -observes all earlier decisions, so dispatch remains online even though inputs -are validated before the run. +The experiment assumes that the submitting user can correct an underestimated +wall-time request using the known ground-truth runtime before the successful +submission. Starting from `predicted_time_limit`, it doubles the request until +it is at least `actual_duration`, without simulating or charging resources for +the failed attempts. `--max-time-limit` caps the corrected request; the +prediction study defaults to Lassen's 43,200-second maximum. If +`actual_duration` exceeds that maximum, the experiment writes a `dropped:` +record to standard error and does not submit the job. Dropped jobs are excluded +from simulation statistics. This intentionally optimistic assumption isolates +placement quality from the cost of discovering a sufficient wall-time limit. + +Candidate systems whose ground-truth runtime exceeds `--max-time-limit` are +discarded before placement, regardless of their waiting time. The smallest +predicted turnaround among the remaining systems wins; ties use estimated wait +and then systems-table order. If no measured, capacity-compatible system can +finish within the maximum, the job is dropped. The wait estimate uses the +corrected submitted time limit. Only the selected worker receives the job, with +that limit and the actual duration. Thus prediction error affects placement, +while the resulting queue state reflects the workload's realized performance. +The next arrival observes all earlier decisions, so dispatch remains online +even though inputs are validated before the run. ## Build and run @@ -223,11 +240,12 @@ cmake --build build --target mpi_performance_dispatch-bin mpirun -np 6 build/mpi_performance_dispatch \ --jobs multi-cluster/job_stream.csv \ - --ground-truth multi-cluster/ground_truth.csv \ - --prediction multi-cluster/prediction.csv \ - --applications multi-cluster/apps.csv \ - --systems multi-cluster/machines.csv \ + --ground-truth experimental/multi-cluster/ground_truth.csv \ + --prediction experimental/multi-cluster/prediction.csv \ + --applications experimental/multi-cluster/apps.csv \ + --systems experimental/multi-cluster/machines.csv \ --seed 7 \ + --max-time-limit 43200 \ --output dispatch-decisions.csv ``` @@ -236,7 +254,8 @@ allocation. MPI and Ser20 are required; gRPC and Python are not. ## Output -Rank 0 writes one CSV row per job-stream row: +Rank 0 writes one CSV row per successfully dispatched job; dropped jobs are +recorded in the log instead: | Column | Meaning | |---|---| @@ -253,7 +272,9 @@ Rank 0 writes one CSV row per job-stream row: | `estimated_duration` | Duration predicted for the dispatch decision. | | `actual_duration` | Ground-truth duration submitted to DR_EVT. | | `predicted_time_limit` | Wall-time limit used to estimate the dispatch candidate. | -| `actual_time_limit` | Ground-truth-scaled wall-time limit submitted to DR_EVT. | +| `actual_time_limit` | Ground-truth-scaled wall-time limit retained for comparison. | +| `submitted_time_limit` | Predicted limit after doubling and applying the configured maximum; this is submitted to DR_EVT. | +| `time_limit_doublings` | Number of pre-submission doublings needed to cover the known actual runtime. | | `predicted_turnaround` | `estimated_wait + estimated_duration`. | | `job_idx` | Worker-local DR_EVT job identifier. | @@ -271,7 +292,9 @@ these evaluation metrics: Turnaround and bounded slowdown are weighted by each system's completed-job count when combined. A mismatch between the completed and dispatched counts is reported as an error rather than producing partial metrics. The Python/gRPC -implementation prints the same summary schema. +implementation prints the same summary schema. The summary reports +`dropped_jobs` separately, and standard error contains one detailed record for +each dropped job. ## Python/gRPC implementation @@ -284,11 +307,12 @@ python3 python/grpc_mpi_launcher.py --mpi-ranks 6 \ --server-binary build/dr_evt_server \ --client-script experimental/multi-cluster/grpc_performance_dispatch.py -- \ --jobs multi-cluster/job_stream.csv \ - --ground-truth multi-cluster/ground_truth.csv \ - --prediction multi-cluster/prediction.csv \ - --applications multi-cluster/apps.csv \ - --systems multi-cluster/machines.csv \ + --ground-truth experimental/multi-cluster/ground_truth.csv \ + --prediction experimental/multi-cluster/prediction.csv \ + --applications experimental/multi-cluster/apps.csv \ + --systems experimental/multi-cluster/machines.csv \ --seed 7 \ + --max-time-limit 43200 \ --output grpc-dispatch-decisions.csv ``` diff --git a/experimental/multi-cluster/apps.csv b/experimental/multi-cluster/apps.csv new file mode 100644 index 0000000..c22543d --- /dev/null +++ b/experimental/multi-cluster/apps.csv @@ -0,0 +1,7 @@ +#app,sys_requirement +amg,CPU-only +xsbench,GPU-portable +laghos,GPU-portable +minife.x,GPU-portable +minivite,CPU-only +testdfft,CPU-only diff --git a/experimental/multi-cluster/build_performance_tables.py b/experimental/multi-cluster/build_performance_tables.py index 8af2047..1e914c2 100644 --- a/experimental/multi-cluster/build_performance_tables.py +++ b/experimental/multi-cluster/build_performance_tables.py @@ -140,68 +140,55 @@ def build_rows( for identity, candidates in prediction_groups.items(): app = identity[0] if app not in requirements: - raise ValueError(f"no sys_requirement for app {app}") - predicted_modes = { - mode for mode in mode_order if any(row[mode].strip() for row in candidates) - } - complete = [ - row - for row in candidates - if all(row[mode].strip() for mode in predicted_modes) - ] - if not complete: - raise ValueError( - f"no single complete prediction row for workload {identity}" - ) - prediction = max( - complete, - key=lambda row: sum( - bool(row[column].strip()) and column in measurements.get(identity, {}) - for column in reference_order - ), - ) - - paired = [ - mode - for mode in mode_order - if mode in predicted_modes and mode in measurements.get(identity, {}) - ] - usable = [ + continue + measured = measurements.get(identity, {}) + actual_modes = [ mode for mode in compatible_modes(requirements[app], mode_order) - if mode in paired + if mode in measured ] - if not usable: + if not actual_modes: skipped += 1 continue external_references = [ column for column in reference_order - if column not in mode_order - and prediction[column].strip() - and column in measurements.get(identity, {}) + if column not in mode_order and column in measured ] - reference = external_references[0] if external_references else usable[0] - reference_runtime = measurements[identity][reference] - reference_prediction = float(prediction[reference]) + reference = external_references[0] if external_references else actual_modes[0] + reference_runtime = measured[reference] + prediction_candidates = [row for row in candidates if row[reference].strip()] + prediction = ( + max( + prediction_candidates, + key=lambda row: sum( + bool(row[mode].strip()) and mode in measured + for mode in mode_order + ), + ) + if prediction_candidates + else None + ) identity_values = { - "App": prediction["app"], - "Args": prediction["args"], - "Ranks": prediction["ranks"], + "App": candidates[0]["app"], + "Args": candidates[0]["args"], + "Ranks": candidates[0]["ranks"], } ground_truth = dict(identity_values) predicted = dict(identity_values) for mode in mode_order: - if mode not in paired: - ground_truth[mode] = "" - predicted[mode] = "" - continue - ground_truth[mode] = format( - reference_runtime / measurements[identity][mode], ".17g" + ground_truth[mode] = ( + format(reference_runtime / measured[mode], ".17g") + if mode in actual_modes + else "" ) - predicted[mode] = format( - float(prediction[mode]) / reference_prediction, ".17g" + predicted[mode] = ( + format(float(prediction[mode]) / float(prediction[reference]), ".17g") + if prediction is not None + and mode in actual_modes + and prediction[mode].strip() + else "" ) ground_truth_rows.append(ground_truth) prediction_rows.append(predicted) diff --git a/experimental/multi-cluster/build_prediction_baselines.py b/experimental/multi-cluster/build_prediction_baselines.py index e3efa57..3c5ee96 100644 --- a/experimental/multi-cluster/build_prediction_baselines.py +++ b/experimental/multi-cluster/build_prediction_baselines.py @@ -66,16 +66,11 @@ def rajaperf_speedups(path, modes, reference="borax"): def rajaperf_rows(ground_truth, modes, speedups): - """Assign one machine-level RAJAPerf prediction to every available mode.""" + """Assign one machine-level RAJAPerf prediction to every system mode.""" rows = [] for actual in ground_truth: row = {name: actual[name] for name in IDENTITY} - row.update( - { - mode: format(speedups[mode], ".17g") if actual[mode].strip() else "" - for mode in modes - } - ) + row.update({mode: format(speedups[mode], ".17g") for mode in modes}) rows.append(row) return rows diff --git a/experimental/multi-cluster/ground_truth.csv b/experimental/multi-cluster/ground_truth.csv index 7eabf93..3e7e3a2 100644 --- a/experimental/multi-cluster/ground_truth.csv +++ b/experimental/multi-cluster/ground_truth.csv @@ -1,7 +1,638 @@ App,Args,Ranks,dane,mammoth,matrix-cpu,matrix-gpu,tioga-cpu,tioga-gpu,tuolumne-cpu,tuolumne-gpu -cpu-solver,--mesh-small,32,1.0,1.2,0.9,,1.0,,1.1, -cpu-solver,--mesh-large,128,0.8,1.0,0.7,,0.8,,0.9, -gpu-trainer,--batch-64,64,,,,1.0,,1.2,,1.4 -portable-md,--size-small,32,0.8,0.9,0.7,1.3,0.8,1.2,0.9,1.1 -portable-md,--size-medium,64,0.9,1.0,0.8,1.1,0.9,1.3,1.0,1.5 -portable-md,--size-large,128,0.7,0.8,0.6,0.9,0.8,1.0,0.9,1.2 +amg,-problem1-p442-n12812864,32,1.0085286291247524,1.0726441577462058,1.1236568150299973,,1.283209560850828,,2.3112229763573646, +amg,-problem1-p442-n16168,32,0.92236071745918025,0.81262364238342288,0.93734741661154219,,0.92046091800398155,,0.71970150944361411, +amg,-problem1-p442-n323216,32,1.009144819723377,0.8873426089342622,0.96196836246427297,,1.3803944205810805,,0.92178770497312645, +amg,-problem1-p442-n646432,32,1.0238450890378283,1.0418827042345884,1.0770104068117312,,1.3985292845155404,,1.7339653196340177, +amg,-problem1-p442-n884,32,0.89509023166601454,0.80970861345883793,0.91111788704362917,,1.1483083541121131,,0.73898202404654023, +amg,-problem2-p111-n128128128,1,1.4863316252705803,1.3141484959053396,1.3825543170341497,,1.5269414639355876,,1.5112492574646645, +amg,-problem2-p111-n161616,1,0.97168581605925586,0.74557509250828358,1.2100298358136958,,1.1874899811645894,,1.311923736786762, +amg,-problem2-p111-n256256256,1,1.3344006490838645,1.2831250212968726,1.4031147680693563,,1.4808464241204089,,1.2985742890986485, 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+minivite,-l-p4-n64,1,0.70793521617872879,0.51458895104200619,0.90975503629798704,,0.98947648086878193,,0.99837481465347533, +minivite,-w-l-p9-n64,1,0.70893256443450625,0.51639727219343246,0.96699133547638827,,1.0054119463395297,,1.0046322355227004, diff --git a/experimental/multi-cluster/grpc_performance_dispatch.py b/experimental/multi-cluster/grpc_performance_dispatch.py index 7bc4c34..51bebe4 100644 --- a/experimental/multi-cluster/grpc_performance_dispatch.py +++ b/experimental/multi-cluster/grpc_performance_dispatch.py @@ -179,7 +179,7 @@ def read_performance_table(path, columns): def read_workloads(ground_truth_path, prediction_path, systems, requirements): - """Read, validate, and group unique runnable workloads by application.""" + """Read runnable workloads whose applications are in the requirements map.""" columns = [] for system in systems: columns.append(system["cpu_column"]) @@ -187,6 +187,16 @@ def read_workloads(ground_truth_path, prediction_path, systems, requirements): columns.append(system["gpu_column"]) ground_truth = read_performance_table(ground_truth_path, columns) prediction = read_performance_table(prediction_path, columns) + ground_truth = { + identity: values + for identity, values in ground_truth.items() + if identity[0] in requirements + } + prediction = { + identity: values + for identity, values in prediction.items() + if identity[0] in requirements + } if ground_truth.keys() != prediction.keys(): missing_prediction = ground_truth.keys() - prediction.keys() missing_truth = prediction.keys() - ground_truth.keys() @@ -199,10 +209,6 @@ def read_workloads(ground_truth_path, prediction_path, systems, requirements): unavailable_rows = 0 for identity, truth_values in ground_truth.items(): app, workload_args, ranks = identity - if app not in requirements: - raise ValueError( - f"{ground_truth_path}: no sys_requirement for app {app}" - ) workload = { "app": app, "args": workload_args, @@ -222,13 +228,9 @@ def read_workloads(ground_truth_path, prediction_path, systems, requirements): continue actual = truth_values[column] predicted = prediction[identity][column] - if (actual is None) != (predicted is None): - raise ValueError( - f"incomplete {column} pair for workload {identity}" - ) performance[mode] = ( None - if actual is None + if actual is None or predicted is None else {"ground_truth": actual, "predicted": predicted} ) if workload["sys_requirement"] == "CPU-only": @@ -289,8 +291,10 @@ def estimate_wait(window, required_nodes, runtime, prediction_horizon): return window.shadow_time - window.current_time + prediction_horizon -def execution_performance(workload, system, index): - """Return the compatible execution mode and performance pair.""" +def execution_performance( + workload, system, index, duration=0.0, max_time_limit=math.inf +): + """Return the fastest predicted compatible mode that meets the runtime cap.""" performance = workload["performance"][index] requirement = workload["sys_requirement"] if requirement == "CPU-only": @@ -306,11 +310,24 @@ def execution_performance(workload, system, index): (mode, performance[mode]) for mode in ("CPU", "GPU") if performance[mode] is not None + and duration / performance[mode]["ground_truth"] <= max_time_limit ] return max(candidates, key=lambda item: item[1]["predicted"], default=None) -def choose_system(job, workload, systems, windows, horizons): +def adjusted_time_limit(predicted_limit, actual_duration, maximum_limit): + """Double a predicted limit until it admits the known runtime or hits the cap.""" + limit = min(predicted_limit, maximum_limit) + doublings = 0 + while limit < actual_duration and limit < maximum_limit: + limit = min(limit * 2, maximum_limit) + doublings += 1 + return limit, doublings + + +def choose_system( + job, workload, systems, windows, horizons, max_time_limit=math.inf +): """Choose the compatible system with minimum predicted turnaround.""" candidates = [] for index, (system, window, horizon) in enumerate( @@ -318,7 +335,9 @@ def choose_system(job, workload, systems, windows, horizons): ): if job["num_nodes"] > system["capacity"]: continue - execution = execution_performance(workload, system, index) + execution = execution_performance( + workload, system, index, job["duration"], max_time_limit + ) if execution is None: continue mode, performance = execution @@ -326,9 +345,14 @@ def choose_system(job, workload, systems, windows, horizons): ground_truth_speedup = performance["ground_truth"] predicted_duration = job["duration"] / predicted_speedup actual_duration = job["duration"] / ground_truth_speedup + if actual_duration > max_time_limit: + continue predicted_limit = job["limit_time"] / predicted_speedup actual_limit = job["limit_time"] / ground_truth_speedup - wait = estimate_wait(window, job["num_nodes"], predicted_limit, horizon) + submitted_limit, doublings = adjusted_time_limit( + predicted_limit, actual_duration, max_time_limit + ) + wait = estimate_wait(window, job["num_nodes"], submitted_limit, horizon) if math.isfinite(wait): candidates.append( { @@ -342,14 +366,11 @@ def choose_system(job, workload, systems, windows, horizons): "actual_duration": actual_duration, "predicted_time_limit": predicted_limit, "actual_time_limit": actual_limit, + "submitted_time_limit": submitted_limit, + "time_limit_doublings": doublings, "predicted_turnaround": wait + predicted_duration, } ) - if not candidates: - raise ValueError( - f"job {job['job_id']} sampled workload {workload['app']} " - "but no compatible system can run it" - ) return min( candidates, key=lambda item: ( @@ -357,6 +378,7 @@ def choose_system(job, workload, systems, windows, horizons): item["estimated_wait"], item["index"], ), + default=None, ) @@ -381,6 +403,7 @@ def run_experiment(args, grpc, pb, service): largest_system = max(system["capacity"] for system in systems) sessions = [ServerSession(address, grpc, pb, service) for address in args.server] decisions = [] + dropped_jobs = [] try: with ThreadPoolExecutor(max_workers=len(sessions)) as executor: init_futures = [] @@ -436,14 +459,31 @@ def run_experiment(args, grpc, pb, service): for response in horizon_responses ] workload = sample_workload(workloads, generator) - choice = choose_system(job, workload, systems, windows, horizons) + choice = choose_system( + job, + workload, + systems, + windows, + horizons, + args.max_time_limit, + ) + if choice is None: + dropped_jobs.append( + { + "job_id": original_job["job_id"], + "app": workload["app"], + "reason": "no_system_within_max_time_limit", + "max_time_limit": args.max_time_limit, + } + ) + continue append = pb.AppendJobsRequest( requests=[ pb.JobAppendData( submit_time=job["submit_time"], num_nodes=job["num_nodes"], queue=job["queue"], - limit_time=choice["actual_time_limit"], + limit_time=choice["submitted_time_limit"], actual_run_time=choice["actual_duration"], ) ] @@ -480,6 +520,8 @@ def run_experiment(args, grpc, pb, service): "actual_duration", "predicted_time_limit", "actual_time_limit", + "submitted_time_limit", + "time_limit_doublings", "predicted_turnaround", ) }, @@ -496,7 +538,7 @@ def run_experiment(args, grpc, pb, service): ) return decisions, [ response.finish_simulation.statistics for response in finish_responses - ], [system["system_id"] for system in systems] + ], [system["system_id"] for system in systems], dropped_jobs finally: for session in sessions: session.close() @@ -524,6 +566,8 @@ def write_results(stream, decisions): "actual_duration", "predicted_time_limit", "actual_time_limit", + "submitted_time_limit", + "time_limit_doublings", "predicted_turnaround", "job_idx", ) @@ -571,7 +615,7 @@ def evaluation_metrics(decisions, statistics): } -def write_summary(stream, statistics, system_ids, decisions): +def write_summary(stream, statistics, system_ids, decisions, dropped_jobs=()): """Print per-system completion data and overall evaluation metrics.""" for system_id, stats in zip(system_ids, statistics): print( @@ -579,9 +623,16 @@ def write_summary(stream, statistics, system_ids, decisions): f"completed={stats.jobs_completed} makespan={stats.makespan:.6g}", file=stream, ) + for job in dropped_jobs: + print( + f"dropped: job_id={job['job_id']} app={job['app']} " + f"reason={job['reason']} " + f"max_time_limit={job['max_time_limit']:.8g}", + file=stream, + ) metrics = evaluation_metrics(decisions, statistics) print( - f"overall: jobs={len(decisions)} " + f"overall: jobs={len(decisions)} dropped_jobs={len(dropped_jobs)} " f"average_turnaround_time={metrics['average_turnaround_time']:.8g} " f"average_bounded_slowdown={metrics['average_bounded_slowdown']:.8g} " f"average_run_time={metrics['average_run_time']:.8g} " @@ -612,10 +663,18 @@ def main(): default=1.0, help="usable-capacity factor for queue horizon (0..1)", ) + parser.add_argument( + "--max-time-limit", + type=float, + default=math.inf, + help="maximum submitted wall time (default: unlimited)", + ) parser.add_argument("--session-name", default="performance-dispatch") args = parser.parse_args() if not 0.0 <= args.prediction_utilization <= 1.0: parser.error("--prediction-utilization must be in [0, 1]") + if math.isnan(args.max_time_limit) or args.max_time_limit <= 0: + parser.error("--max-time-limit must be positive") if args.backfill_policy.lower() != "easy": parser.error("performance dispatch requires --backfill-policy easy") if args.priority_policy.lower() not in {"fcfs", "fcfs_alt"}: @@ -625,13 +684,15 @@ def main(): repo_root = pathlib.Path(__file__).resolve().parents[2] grpc, pb, service, generated_dir = load_stubs(repo_root) try: - decisions, statistics, system_ids = run_experiment(args, grpc, pb, service) + decisions, statistics, system_ids, dropped_jobs = run_experiment( + args, grpc, pb, service + ) if args.output: with args.output.open("w", newline="") as stream: write_results(stream, decisions) else: write_results(sys.stdout, decisions) - write_summary(sys.stderr, statistics, system_ids, decisions) + write_summary(sys.stderr, statistics, system_ids, decisions, dropped_jobs) finally: generated_dir.cleanup() diff --git a/experimental/multi-cluster/mpi_performance_dispatch.cpp b/experimental/multi-cluster/mpi_performance_dispatch.cpp index c31ba2f..773f774 100644 --- a/experimental/multi-cluster/mpi_performance_dispatch.cpp +++ b/experimental/multi-cluster/mpi_performance_dispatch.cpp @@ -164,6 +164,7 @@ struct Options { std::optional output; std::uint64_t seed = 0; double prediction_utilization = 1.0; + double max_time_limit = std::numeric_limits::infinity(); }; [[noreturn]] void usage(const char *program, const std::string &error = {}) { @@ -177,6 +178,8 @@ struct Options { << " --systems PATH\n" << " --seed INTEGER sampling seed (default: 0)\n" << " --prediction-utilization U value in [0,1] (default: 1)\n" + << " --max-time-limit SECONDS maximum submitted wall time " + "(default: unlimited)\n" << " --output PATH decision CSV (default: stdout)\n"; throw std::invalid_argument(error.empty() ? "help requested" : error); } @@ -207,6 +210,8 @@ Options parse_options(int argc, char **argv) { else if (arg == "--prediction-utilization") options.prediction_utilization = std::stod(option_value(i, argc, argv, arg)); + else if (arg == "--max-time-limit") + options.max_time_limit = std::stod(option_value(i, argc, argv, arg)); else if (arg == "--output") options.output = option_value(i, argc, argv, arg); else if (arg == "--help" || arg == "-h") @@ -223,6 +228,8 @@ Options parse_options(int argc, char **argv) { options.prediction_utilization < 0.0 || options.prediction_utilization > 1.0) usage(argv[0], "--prediction-utilization must be in [0,1]"); + if (std::isnan(options.max_time_limit) || options.max_time_limit <= 0.0) + usage(argv[0], "--max-time-limit must be positive"); return options; } @@ -507,10 +514,7 @@ read_performance_pair(const std::vector &row, const auto ground_truth = read_performance(row, header, column, context); const auto predicted = read_performance(prediction_row, prediction_header, column, context); - if (ground_truth.has_value() != predicted.has_value()) - throw std::runtime_error(context + ": incomplete " + column + - " ground-truth/prediction pair"); - if (!ground_truth) + if (!ground_truth || !predicted) return std::nullopt; return PerformancePair{*ground_truth, *predicted}; } @@ -519,7 +523,8 @@ read_performance_pair(const std::vector &row, * @param[in] ground_truth_path Measured-speedup CSV. * @param[in] prediction_path Predicted-speedup CSV. * @param[in] systems Configured systems and their execution-mode columns. - * @param[in] requirements Application compatibility requirements. + * @param[in] requirements Application allowlist and compatibility requirements; + * performance rows for absent applications are ignored. * @return Valid runnable workloads grouped by application. * @throws std::runtime_error if either table is malformed or identities differ. */ @@ -575,6 +580,8 @@ WorkloadCatalog read_workloads( field(row, prediction_header, predicted_identity[1]), static_cast( std::stoul(field(row, prediction_header, predicted_identity[2])))}; + if (!requirements.contains(std::get<0>(key))) + continue; if (!predictions.emplace(std::move(key), std::move(row)).second) throw std::runtime_error(prediction_path + ": duplicate workload identity"); @@ -594,6 +601,9 @@ WorkloadCatalog read_workloads( const Identity workload_identity{ app, field(row, header, identity[1]), static_cast(std::stoul(field(row, header, identity[2])))}; + const auto requirement = requirements.find(app); + if (requirement == requirements.end()) + continue; if (!identities.insert(workload_identity).second) throw std::runtime_error(ground_truth_path + ": duplicate workload at row " + @@ -603,10 +613,6 @@ WorkloadCatalog read_workloads( throw std::runtime_error(prediction_path + ": missing workload found in ground truth"); const auto &prediction_row = prediction->second; - const auto requirement = requirements.find(app); - if (requirement == requirements.end()) - throw std::runtime_error(ground_truth_path + - ": no sys_requirement for App " + app); Workload workload{ app, field(row, header, identity[1]), static_cast(std::stoul(field(row, header, identity[2]))), @@ -709,12 +715,46 @@ struct Choice { double actual_duration; double predicted_time_limit; double actual_time_limit; + double submitted_time_limit; + std::uint32_t time_limit_doublings; double predicted_turnaround; }; -Choice choose_system(const Job &job, const Workload &workload, - const std::vector &systems, - const std::vector &snapshots) { +/** Increase a predicted wall-time limit enough to admit the known runtime. + * + * This models a user correcting the request before the successful submission; + * failed attempts consume no simulated resources. + * + * @param[in] predicted_limit Initial performance-scaled limit. + * @param[in] actual_duration Ground-truth runtime on the candidate system. + * @param[in] maximum_limit Largest permitted wall-time request. + * @return Final capped limit and the number of doublings performed. + */ +std::pair +adjust_time_limit(double predicted_limit, double actual_duration, + double maximum_limit) { + double limit = std::min(predicted_limit, maximum_limit); + std::uint32_t doublings = 0; + while (limit < actual_duration && limit < maximum_limit) { + limit = std::min(limit * 2.0, maximum_limit); + ++doublings; + } + return {limit, doublings}; +} + +/** Select the system with the smallest predicted turnaround. + * @param[in] job Job request with its effective node count. + * @param[in] workload Sampled application workload. + * @param[in] systems Configured execution systems. + * @param[in] snapshots Current scheduler state for each system. + * @param[in] max_time_limit Maximum permitted submitted wall-time limit. + * @return Best compatible system that can finish within the maximum, or no + * choice if none can do so. + */ +std::optional +choose_system(const Job &job, const Workload &workload, + const std::vector &systems, + const std::vector &snapshots, double max_time_limit) { std::optional best; for (std::size_t i = 0; i < systems.size(); ++i) { if (job.num_nodes > systems[i].physical_nodes) @@ -731,8 +771,13 @@ Choice choose_system(const Job &job, const Workload &workload, } else if (!systems[i].gpu_enabled) { performance = workload.performance[i].cpu; } else { - performance = workload.performance[i].cpu; + if (workload.performance[i].cpu && + job.duration / workload.performance[i].cpu->ground_truth <= + max_time_limit) + performance = workload.performance[i].cpu; if (workload.performance[i].gpu && + job.duration / workload.performance[i].gpu->ground_truth <= + max_time_limit && (!performance || workload.performance[i].gpu->predicted > performance->predicted)) { performance = workload.performance[i].gpu; @@ -743,10 +788,14 @@ Choice choose_system(const Job &job, const Workload &workload, continue; const double predicted_duration = job.duration / performance->predicted; const double actual_duration = job.duration / performance->ground_truth; + if (actual_duration > max_time_limit) + continue; const double predicted_limit = job.limit_time / performance->predicted; const double actual_limit = job.limit_time / performance->ground_truth; + const auto [submitted_limit, doublings] = adjust_time_limit( + predicted_limit, actual_duration, max_time_limit); const double wait = - estimate_wait(snapshots[i].window, job.num_nodes, predicted_limit, + estimate_wait(snapshots[i].window, job.num_nodes, submitted_limit, snapshots[i].prediction_horizon); if (!std::isfinite(wait)) continue; @@ -759,6 +808,8 @@ Choice choose_system(const Job &job, const Workload &workload, actual_duration, predicted_limit, actual_limit, + submitted_limit, + doublings, wait + predicted_duration}; if (!best || std::tie(choice.predicted_turnaround, choice.estimated_wait, @@ -766,11 +817,7 @@ Choice choose_system(const Job &job, const Workload &workload, best->estimated_wait, best->index)) best = choice; } - if (!best) - throw std::runtime_error("job " + job.id + " sampled workload " + - workload.app + - " but no compatible system can run it"); - return *best; + return best; } Response handle_request(dr_evt::Simulation &simulation, @@ -886,12 +933,15 @@ void controller(const Options &options, const std::vector &systems, "ground_truth_relative_performance," "predicted_relative_performance," "estimated_wait,estimated_duration,actual_duration," - "predicted_time_limit,actual_time_limit," + "predicted_time_limit,actual_time_limit,submitted_time_limit," + "time_limit_doublings," "predicted_turnaround,job_idx\n"; *output << std::setprecision(17); double total_run_time = 0.0; double total_speedup = 0.0; + std::uint64_t dispatched_jobs = 0; + std::uint64_t dropped_jobs = 0; for (const auto &job : jobs) { Job dispatch_job = job; @@ -903,15 +953,25 @@ void controller(const Options &options, const std::vector &systems, snapshot.prediction_utilization = options.prediction_utilization; const auto snapshots = call_all(snapshot, worker_count); const auto &workload = sample_workload(catalog, generator); - const Choice choice = - choose_system(dispatch_job, workload, systems, snapshots); + const auto selected = + choose_system(dispatch_job, workload, systems, snapshots, + options.max_time_limit); + if (!selected) { + ++dropped_jobs; + std::cerr << "dropped: job_id=" << job.id << " app=" << workload.app + << " reason=no_system_within_max_time_limit" + << " max_time_limit=" << options.max_time_limit << '\n'; + continue; + } + const Choice &choice = *selected; + ++dispatched_jobs; total_run_time += choice.actual_duration; total_speedup += choice.ground_truth_relative_performance; Request append; append.operation = Operation::Append; append.job = {job.submit_time, dispatch_job.num_nodes, job.queue, - choice.actual_duration, choice.actual_time_limit}; + choice.actual_duration, choice.submitted_time_limit}; send_serialized(append, static_cast(choice.index) + 1); const Response response = receive_serialized(static_cast(choice.index) + 1); @@ -930,6 +990,8 @@ void controller(const Options &options, const std::vector &systems, << choice.estimated_wait << ',' << choice.estimated_duration << ',' << choice.actual_duration << ',' << choice.predicted_time_limit << ',' << choice.actual_time_limit << ',' + << choice.submitted_time_limit << ',' + << choice.time_limit_doublings << ',' << choice.predicted_turnaround << ',' << response.job_idx << '\n'; } @@ -949,19 +1011,22 @@ void controller(const Options &options, const std::vector &systems, << " completed=" << stats.jobs_completed << " makespan=" << stats.makespan << '\n'; } - if (completed_jobs != jobs.size()) + if (completed_jobs != dispatched_jobs) throw std::runtime_error("completed job count does not match dispatched " "job count"); - const double denominator = static_cast(jobs.size()); - std::cerr << std::setprecision(8) << "overall: jobs=" << jobs.size() + const double denominator = static_cast(dispatched_jobs); + std::cerr << std::setprecision(8) << "overall: jobs=" << dispatched_jobs + << " dropped_jobs=" << dropped_jobs << " average_turnaround_time=" - << (jobs.empty() ? 0.0 : total_turnaround / denominator) + << (dispatched_jobs == 0 ? 0.0 : total_turnaround / denominator) << " average_bounded_slowdown=" - << (jobs.empty() ? 0.0 : total_bounded_slowdown / denominator) + << (dispatched_jobs == 0 ? 0.0 + : total_bounded_slowdown / denominator) << " average_run_time=" - << (jobs.empty() ? 0.0 : total_run_time / denominator) + << (dispatched_jobs == 0 ? 0.0 : total_run_time / denominator) << " average_speedup=" - << (jobs.empty() ? 0.0 : total_speedup / denominator) << '\n'; + << (dispatched_jobs == 0 ? 0.0 : total_speedup / denominator) + << '\n'; } } // namespace diff --git a/experimental/multi-cluster/prediction.app_avg.csv b/experimental/multi-cluster/prediction.app_avg.csv new file mode 100644 index 0000000..1ef09ed --- /dev/null +++ b/experimental/multi-cluster/prediction.app_avg.csv @@ 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-portable-md,--size-small,32,0.9,1.0,0.8,1.5,0.9,1.1,1.0,1.2 -portable-md,--size-medium,64,0.8,1.1,0.9,1.2,1.0,1.4,1.1,1.3 -portable-md,--size-large,128,0.8,0.9,0.7,1.0,0.9,1.1,1.0,1.3 +amg,-problem1-p442-n12812864,32,1.0085233107414253,1.0727048675292667,1.1237420414869583,,1.2832203737933867,,2.3113575682891763, +amg,-problem1-p442-n16168,32,0.92249047013977115,0.81257941550190604,0.93710292249047,,0.92058449809402787,,0.71982210927573054, +amg,-problem1-p442-n323216,32,1.0088647412067486,0.88733199885616243,0.96196740062911057,,1.3803259937088934,,0.92164712610809252, +amg,-problem1-p442-n646432,32,1.0238264076017587,1.0419798610126225,1.0770103531413984,,1.3986668557651398,,1.7340802723018012, +amg,-problem1-p442-n884,32,0.89520317751298506,0.8096547509929729,0.91109074243813026,,1.1481820959364499,,0.7390772991139628, +amg,-problem2-p111-n128128128,1,1.4855842185128982,1.314112291350531,1.3823975720789075,,1.526555386949924,,1.5106221547799696, 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+minivite,-l-p4-n64,1,1.7377746022772464,1.3488386608852763,1.3266053338108152,197.98609520110355,1.3840844383352937,66.106285880545187,1.5523977280240795,130.44264661919249 +minivite,-w-l-p9-n64,1,1.7377746022772464,1.3488386608852763,1.3266053338108152,197.98609520110355,1.3840844383352937,66.106285880545187,1.5523977280240795,130.44264661919249 diff --git a/experimental/multi-cluster/run_prediction_study.py b/experimental/multi-cluster/run_prediction_study.py new file mode 100644 index 0000000..2acaff7 --- /dev/null +++ b/experimental/multi-cluster/run_prediction_study.py @@ -0,0 +1,318 @@ +#!/usr/bin/env python3 +"""Run and summarize the multi-cluster prediction-baseline study. + +The study uses ten Lassen synthetic job streams and compares ideal, +application-average, and RAJAPerf predictions. A model-based case can be +added later by passing ``--model-prediction``. +""" + +import argparse +import csv +import math +import re +import statistics +import subprocess +import sys +from pathlib import Path + + +METRICS = ( + "average_turnaround_time", + "average_bounded_slowdown", + "average_run_time", + "average_speedup", +) +OVERALL_RE = re.compile( + r"^overall: jobs=(?P\d+) dropped_jobs=(?P\d+) " + + r" ".join(rf"{name}=(?P<{name}>[-+0-9.eE]+)" for name in METRICS) + + r"$", + re.MULTILINE, +) + + +def parse_overall(text): + """Extract the overall metric record from one dispatcher log.""" + matches = list(OVERALL_RE.finditer(text)) + if len(matches) != 1: + raise ValueError(f"expected one overall line, found {len(matches)}") + match = matches[0] + return { + "jobs": int(match.group("jobs")), + "dropped_jobs": int(match.group("dropped_jobs")), + **{name: float(match.group(name)) for name in METRICS}, + } + + +def prediction_cases(root, model_prediction): + """Return ordered (case, prediction table) pairs for this study.""" + experiment = root / "experimental/multi-cluster" + cases = [ + ("ideal", experiment / "ground_truth.csv"), + ("app_avg", experiment / "prediction.app_avg.csv"), + ("rajaperf", experiment / "prediction.rajaperf.csv"), + ] + if model_prediction is not None: + cases.insert(1, ("model", model_prediction.resolve())) + return cases + + +def validate_inputs(root, executable, cases): + """Validate all static inputs before consuming an allocation.""" + required = [ + executable, + root / "experimental/multi-cluster/apps.csv", + root / "experimental/multi-cluster/machines.csv", + *(prediction for _, prediction in cases), + ] + traces = sorted( + (root / "experimental/synthesize/lassen/synthetic_traces").glob( + "synthetic_jobs_100000_min60s_successful_*.csv" + ) + ) + missing = [str(path) for path in required if not path.is_file()] + if missing: + raise FileNotFoundError("missing required files: " + ", ".join(missing)) + if len(traces) != 10: + raise ValueError(f"expected exactly 10 job traces, found {len(traces)}") + for trace in traces: + with trace.open(encoding="utf-8") as stream: + rows = sum(1 for _ in stream) - 1 + if rows != 100_000: + raise ValueError(f"{trace} contains {rows} jobs, expected 100000") + return traces + + +def run_case(args, root, case, prediction, trace, run_number): + """Run one case unless its validated completion marker already exists.""" + stem = f"{case}.run_{run_number:02d}" + dispatch = args.output_dir / f"{stem}.dispatch.csv" + log = args.output_dir / f"{stem}.log" + marker = args.output_dir / f"{stem}.complete" + + if marker.is_file() and dispatch.is_file() and log.is_file(): + try: + record = parse_overall(log.read_text(encoding="utf-8")) + with dispatch.open(encoding="utf-8") as stream: + rows = sum(1 for _ in stream) - 1 + if ( + rows == record["jobs"] + and rows + record["dropped_jobs"] == 100_000 + ): + print(f"skip validated {stem}", flush=True) + return record + except (OSError, ValueError): + pass + + marker.unlink(missing_ok=True) + command = [ + *args.launcher, + "-n", + str(args.ranks), + str(args.executable), + "--jobs", + str(trace), + "--ground-truth", + str(root / "experimental/multi-cluster/ground_truth.csv"), + "--prediction", + str(prediction), + "--applications", + str(root / "experimental/multi-cluster/apps.csv"), + "--systems", + str(root / "experimental/multi-cluster/machines.csv"), + "--seed", + str(args.seed), + "--max-time-limit", + str(args.max_time_limit), + "--output", + str(dispatch), + ] + print(f"run {stem}: {' '.join(command)}", flush=True) + result = subprocess.run(command, cwd=root, text=True, capture_output=True) + combined = result.stdout + result.stderr + log.write_text(combined, encoding="utf-8") + if result.returncode: + raise RuntimeError(f"{stem} failed with status {result.returncode}; see {log}") + + record = parse_overall(combined) + with dispatch.open(encoding="utf-8") as stream: + rows = sum(1 for _ in stream) - 1 + if rows != record["jobs"] or rows + record["dropped_jobs"] != 100_000: + raise RuntimeError( + f"{stem} is incomplete: dispatch rows={rows}, " + f"reported jobs={record['jobs']}, dropped={record['dropped_jobs']}" + ) + marker.write_text("complete\n", encoding="utf-8") + return record + + +def write_results(output_dir, records): + """Write per-run data and ten-run aggregate tables.""" + per_run = output_dir / "metrics_per_run.csv" + with per_run.open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter( + stream, + fieldnames=("case", "run", "jobs", "dropped_jobs", *METRICS), + ) + writer.writeheader() + writer.writerows(records) + + grouped = {} + for record in records: + grouped.setdefault(record["case"], []).append(record) + + summary_rows = [] + for case, case_records in grouped.items(): + if len(case_records) != 10: + raise ValueError(f"{case} has {len(case_records)} complete runs, expected 10") + row = {"case": case, "runs": len(case_records)} + dropped = [record["dropped_jobs"] for record in case_records] + row["dropped_jobs_mean"] = statistics.fmean(dropped) + row["dropped_jobs_stddev"] = statistics.pstdev(dropped) + for metric in METRICS: + values = [record[metric] for record in case_records] + row[f"{metric}_mean"] = statistics.fmean(values) + row[f"{metric}_stddev"] = statistics.pstdev(values) + summary_rows.append(row) + + summary_csv = output_dir / "summary.csv" + fields = ["case", "runs", "dropped_jobs_mean", "dropped_jobs_stddev"] + [ + column for metric in METRICS for column in (f"{metric}_mean", f"{metric}_stddev") + ] + with summary_csv.open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(summary_rows) + + summary_md = output_dir / "summary.md" + labels = { + "ideal": "Ideal (100% accurate)", + "model": "Model-based", + "app_avg": "Application average", + "rajaperf": "RAJAPerf", + } + lines = [ + "# Multi-cluster prediction study", + "", + "Values are the mean ± population standard deviation over 10 job traces.", + "", + "| Prediction | Dropped jobs | Turnaround time | Bounded slowdown | Run time | Speedup |", + "|---|---:|---:|---:|---:|---:|", + ] + for row in summary_rows: + cells = [] + for metric in METRICS: + cells.append(f"{row[f'{metric}_mean']:.6g} ± {row[f'{metric}_stddev']:.6g}") + dropped = ( + f"{row['dropped_jobs_mean']:.6g} ± " + f"{row['dropped_jobs_stddev']:.6g}" + ) + lines.append( + f"| {labels[row['case']]} | {dropped} | " + + " | ".join(cells) + + " |" + ) + if "model" not in grouped: + lines.extend( + ["", "Model-based prediction was not run because no model prediction table was supplied."] + ) + summary_md.write_text("\n".join(lines) + "\n", encoding="utf-8") + return summary_rows + + +def plot_results(output_dir, summary_rows): + """Create a four-panel mean-metric plot with run-to-run error bars.""" + try: + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + except ImportError as error: + raise RuntimeError( + "matplotlib is required for the plot; tables were still generated" + ) from error + + labels = [row["case"].replace("_", " ") for row in summary_rows] + titles = { + "average_turnaround_time": "Average turnaround time", + "average_bounded_slowdown": "Average bounded slowdown", + "average_run_time": "Average run time", + "average_speedup": "Average selected speedup", + } + fig, axes = plt.subplots(2, 2, figsize=(11, 8)) + colors = ["#4C78A8", "#F58518", "#54A24B", "#E45756"] + for axis, metric in zip(axes.flat, METRICS): + means = [row[f"{metric}_mean"] for row in summary_rows] + errors = [row[f"{metric}_stddev"] for row in summary_rows] + axis.bar(labels, means, yerr=errors, capsize=4, color=colors[: len(labels)]) + axis.set_title(titles[metric]) + axis.grid(axis="y", alpha=0.25) + axis.tick_params(axis="x", rotation=15) + fig.suptitle("Multi-cluster prediction policies (mean ± SD, 10 traces)") + fig.tight_layout() + fig.savefig(output_dir / "summary.png", dpi=180) + plt.close(fig) + + +def load_completed(output_dir, cases): + """Load metrics for aggregate-only mode from validated run artifacts.""" + records = [] + for case, _ in cases: + for run_number in range(1, 11): + stem = f"{case}.run_{run_number:02d}" + marker = output_dir / f"{stem}.complete" + log = output_dir / f"{stem}.log" + if not marker.is_file() or not log.is_file(): + raise FileNotFoundError(f"missing completed run: {stem}") + records.append({"case": case, "run": run_number, **parse_overall(log.read_text())}) + return records + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--executable", type=Path, help="installed mpi_performance_dispatch") + parser.add_argument( + "--output-dir", + type=Path, + default=Path("experimental/multi-cluster/prediction-study-results"), + ) + parser.add_argument( + "--launcher", nargs="+", default=["srun"], help="MPI launcher prefix (default: srun)" + ) + parser.add_argument("--ranks", type=int, default=6) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument("--max-time-limit", type=float, default=43200.0) + parser.add_argument("--model-prediction", type=Path) + parser.add_argument("--aggregate-only", action="store_true") + args = parser.parse_args() + if not math.isfinite(args.max_time_limit) or args.max_time_limit <= 0: + parser.error("--max-time-limit must be finite and positive") + + root = Path(__file__).resolve().parents[2] + args.output_dir = args.output_dir.resolve() + args.output_dir.mkdir(parents=True, exist_ok=True) + cases = prediction_cases(root, args.model_prediction) + + if args.aggregate_only: + records = load_completed(args.output_dir, cases) + else: + if args.executable is None: + parser.error("--executable is required unless --aggregate-only is used") + args.executable = args.executable.resolve() + traces = validate_inputs(root, args.executable, cases) + records = [] + for case, prediction in cases: + for run_number, trace in enumerate(traces, start=1): + record = run_case(args, root, case, prediction, trace, run_number) + records.append({"case": case, "run": run_number, **record}) + + summary_rows = write_results(args.output_dir, records) + plot_results(args.output_dir, summary_rows) + print(f"wrote results to {args.output_dir}") + + +if __name__ == "__main__": + try: + main() + except (FileNotFoundError, RuntimeError, ValueError) as error: + print(f"error: {error}", file=sys.stderr) + raise SystemExit(1) diff --git a/experimental/multi-cluster/test_performance_dispatch.py b/experimental/multi-cluster/test_performance_dispatch.py index b311929..1a785ef 100644 --- a/experimental/multi-cluster/test_performance_dispatch.py +++ b/experimental/multi-cluster/test_performance_dispatch.py @@ -14,6 +14,7 @@ import build_prediction_baselines as baselines import plot_relative_performance as performance_plot from grpc_performance_dispatch import ( + adjusted_time_limit, choose_system, evaluation_metrics, estimate_release_wait, @@ -26,7 +27,7 @@ ) -_FIXTURES = pathlib.Path(__file__).resolve().parent +_FIXTURES = pathlib.Path(__file__).resolve().parent / "testdata" def window(now, available, releases=(), shadow=-1): @@ -154,17 +155,37 @@ def test_sampling_is_uniform_by_application_not_row_count(self): counts[sample_workload(self.workloads, generator)["app"]] += 1 self.assertTrue(all(1800 < count < 2200 for count in counts.values())) - def test_workload_requires_ground_truth_prediction_pairs(self): + def test_applications_table_is_sampling_allowlist(self): + requirements = read_applications(_FIXTURES / "applications.csv") + requirements.pop("gpu-trainer") + prediction = (_FIXTURES / "prediction.csv").read_text(encoding="utf-8") + with tempfile.TemporaryDirectory() as directory: + path = pathlib.Path(directory) / "prediction.csv" + path.write_text( + "\n".join( + line + for line in prediction.splitlines() + if not line.startswith("gpu-trainer,") + ) + + "\n", + encoding="utf-8", + ) + workloads = read_workloads( + _FIXTURES / "ground_truth.csv", path, self.systems, requirements + ) + self.assertEqual(set(workloads), {"cpu-solver", "portable-md"}) + + def test_workload_ignores_modes_without_a_prediction(self): requirements = read_applications(_FIXTURES / "applications.csv") contents = (_FIXTURES / "prediction.csv").read_text(encoding="utf-8") contents = contents.replace("0.9,1.3", ",1.3", 1) with tempfile.TemporaryDirectory() as directory: path = pathlib.Path(directory) / "prediction.csv" path.write_text(contents, encoding="utf-8") - with self.assertRaisesRegex(ValueError, "incomplete dane pair"): - read_workloads( - _FIXTURES / "ground_truth.csv", path, self.systems, requirements - ) + workloads = read_workloads( + _FIXTURES / "ground_truth.csv", path, self.systems, requirements + ) + self.assertIsNone(workloads["cpu-solver"][0]["performance"][0]["CPU"]) def test_workload_builder_normalizes_quoted_comma_arguments(self): identity = build_table.normalized_identity( @@ -209,6 +230,46 @@ def test_workload_builder_uses_one_reference_for_actual_and_prediction(self): self.assertEqual(float(ground_truth[0]["mammoth"]), 2.0) self.assertEqual(float(predicted[0]["mammoth"]), 4.0) + def test_workload_builder_keeps_measured_modes_without_model_predictions(self): + identity = ("solver", "--size8", 4) + predictions = { + identity: [ + { + "app": "solver", + "args": "--size8", + "ranks": "4", + "borax": "1", + "dane": "", + "mammoth": "4", + } + ] + } + measurements = { + identity: {"borax": 50.0, "dane": 100.0, "mammoth": 25.0} + } + ground_truth, predicted, skipped = build_table.build_rows( + predictions, + measurements, + {"solver": "CPU-only"}, + ["dane", "mammoth"], + ["borax", "dane", "mammoth"], + ) + self.assertEqual(skipped, 0) + self.assertEqual(float(ground_truth[0]["dane"]), 0.5) + self.assertEqual(float(ground_truth[0]["mammoth"]), 2.0) + self.assertEqual(predicted[0]["dane"], "") + self.assertEqual(float(predicted[0]["mammoth"]), 4.0) + + def test_workload_builder_uses_requirements_as_allowlist(self): + excluded = ("excluded", "--case", 1) + ground_truth, predicted, skipped = build_table.build_rows( + {excluded: [{"dane": "1"}]}, + {excluded: {"dane": 1.0}}, + {}, + ["dane"], + ) + self.assertEqual((ground_truth, predicted, skipped), ([], [], 0)) + def test_plot_system_alias_and_point_loading(self): with tempfile.TemporaryDirectory() as directory: actual = pathlib.Path(directory) / "actual.csv" @@ -244,6 +305,11 @@ def test_rajaperf_baseline_preserves_duplicate_kernel_positions(self): values = baselines.rajaperf_speedups(path, ["dane"]) self.assertEqual(values["dane"], 3.0) + def test_rajaperf_baseline_predicts_modes_without_measurements(self): + rows = [{"App": "a", "Args": "x", "Ranks": "1", "dane": ""}] + predicted = baselines.rajaperf_rows(rows, ["dane"], {"dane": 2.0}) + self.assertEqual(float(predicted[0]["dane"]), 2.0) + def test_wait_uses_cumulative_releases(self): snapshot = window(10, 2, ((15, 3), (21, 4))) self.assertEqual(estimate_release_wait(snapshot, 5), 5) @@ -333,6 +399,32 @@ def test_gpu_portable_uses_faster_mode_on_gpu_system(self): self.assertAlmostEqual(choice["predicted_time_limit"], 120 / 1.5) self.assertAlmostEqual(choice["actual_time_limit"], 120 / 1.3) + def test_gpu_portable_uses_feasible_mode_when_faster_mode_exceeds_limit(self): + workload = { + **self.workloads["portable-md"][0], + "performance": [ + { + "CPU": {"ground_truth": 1.0, "predicted": 1.0}, + "GPU": {"ground_truth": 0.5, "predicted": 4.0}, + } + ], + } + system = self.systems[-1] + choice = choose_system( + { + "job_id": "mode-cap", + "num_nodes": 1, + "duration": 80, + "limit_time": 40, + }, + workload, + [system], + [window(0, system["capacity"])], + [0], + max_time_limit=100, + ) + self.assertEqual(choice["execution_mode"], "CPU") + def test_prediction_drives_choice_but_ground_truth_drives_runtime(self): systems = self.systems[:2] workload = { @@ -358,6 +450,48 @@ def test_prediction_drives_choice_but_ground_truth_drives_runtime(self): self.assertEqual(choice["estimated_duration"], 20) self.assertEqual(choice["actual_duration"], 160) + def test_predicted_time_limit_is_doubled_and_capped(self): + self.assertEqual(adjusted_time_limit(10, 35, 100), (40, 2)) + self.assertEqual(adjusted_time_limit(10, 120, 100), (100, 4)) + self.assertEqual(adjusted_time_limit(40, 40, 100), (40, 0)) + + def test_runtime_limit_filters_systems_before_wait_comparison(self): + systems = self.systems[:2] + workload = { + **self.workloads["cpu-solver"][0], + "performance": [ + {"CPU": {"ground_truth": 0.5, "predicted": 4.0}, "GPU": None}, + {"CPU": {"ground_truth": 1.0, "predicted": 1.0}, "GPU": None}, + ], + } + job = { + "job_id": "runtime-limit", + "num_nodes": 8, + "duration": 80, + "limit_time": 20, + } + choice = choose_system( + job, + workload, + systems, + [window(0, system["capacity"]) for system in systems], + [0, 0], + max_time_limit=100, + ) + self.assertEqual(choice["system_id"], "mammoth") + self.assertEqual(choice["submitted_time_limit"], 80) + self.assertEqual(choice["time_limit_doublings"], 2) + self.assertIsNone( + choose_system( + job, + workload, + systems, + [window(0, system["capacity"]) for system in systems], + [0, 0], + max_time_limit=50, + ) + ) + def test_controller_uses_same_inputs_and_submits_both_scaled_times(self): args = SimpleNamespace( server=[f"server-{index}" for index in range(5)], @@ -372,15 +506,17 @@ def test_controller_uses_same_inputs_and_submits_both_scaled_times(self): priority_policy="fcfs", queue_impl="circular", prediction_utilization=1.0, + max_time_limit=1000.0, session_name="test", ) FakeSession.instances = [] with patch.object(dispatch, "ServerSession", FakeSession): - decisions, statistics, system_ids = dispatch.run_experiment( + decisions, statistics, system_ids, dropped_jobs = dispatch.run_experiment( args, None, FakeMessages, None ) self.assertEqual(len(decisions), 4) + self.assertEqual(dropped_jobs, []) self.assertEqual(sum(stat.jobs_submitted for stat in statistics), 4) self.assertEqual( system_ids, [system["system_id"] for system in self.systems] @@ -401,7 +537,7 @@ def test_controller_uses_same_inputs_and_submits_both_scaled_times(self): ) self.assertCountEqual( [job.limit_time for job in submitted], - [decision["actual_time_limit"] for decision in decisions], + [decision["submitted_time_limit"] for decision in decisions], ) for decision in decisions: self.assertAlmostEqual( @@ -425,6 +561,21 @@ def test_controller_uses_same_inputs_and_submits_both_scaled_times(self): all(session.run_time_mode == "actual" for session in FakeSession.instances) ) + args.max_time_limit = 1.0 + FakeSession.instances = [] + with patch.object(dispatch, "ServerSession", FakeSession): + dropped_decisions, dropped_statistics, _, dropped_jobs = ( + dispatch.run_experiment(args, None, FakeMessages, None) + ) + self.assertEqual(dropped_decisions, []) + self.assertEqual(len(dropped_jobs), 4) + self.assertEqual( + sum(stat.jobs_submitted for stat in dropped_statistics), 0 + ) + self.assertTrue( + all(not session.submitted for session in FakeSession.instances) + ) + def test_evaluation_metrics_are_job_weighted(self): decisions = [ {"actual_duration": 5.0, "ground_truth_relative_performance": 1.0}, diff --git a/experimental/multi-cluster/applications.csv b/experimental/multi-cluster/testdata/applications.csv similarity index 100% rename from experimental/multi-cluster/applications.csv rename to experimental/multi-cluster/testdata/applications.csv diff --git a/experimental/multi-cluster/testdata/ground_truth.csv b/experimental/multi-cluster/testdata/ground_truth.csv new file mode 100644 index 0000000..7eabf93 --- /dev/null +++ b/experimental/multi-cluster/testdata/ground_truth.csv @@ -0,0 +1,7 @@ +App,Args,Ranks,dane,mammoth,matrix-cpu,matrix-gpu,tioga-cpu,tioga-gpu,tuolumne-cpu,tuolumne-gpu +cpu-solver,--mesh-small,32,1.0,1.2,0.9,,1.0,,1.1, +cpu-solver,--mesh-large,128,0.8,1.0,0.7,,0.8,,0.9, +gpu-trainer,--batch-64,64,,,,1.0,,1.2,,1.4 +portable-md,--size-small,32,0.8,0.9,0.7,1.3,0.8,1.2,0.9,1.1 +portable-md,--size-medium,64,0.9,1.0,0.8,1.1,0.9,1.3,1.0,1.5 +portable-md,--size-large,128,0.7,0.8,0.6,0.9,0.8,1.0,0.9,1.2 diff --git a/experimental/multi-cluster/job_stream.csv b/experimental/multi-cluster/testdata/job_stream.csv similarity index 100% rename from experimental/multi-cluster/job_stream.csv rename to experimental/multi-cluster/testdata/job_stream.csv diff --git a/experimental/multi-cluster/testdata/machines.csv b/experimental/multi-cluster/testdata/machines.csv new file mode 100644 index 0000000..70ccef9 --- /dev/null +++ b/experimental/multi-cluster/testdata/machines.csv @@ -0,0 +1,6 @@ +#machine,size,GPU +dane,256,CPU-only +mammoth,64,CPU-only +tioga,30,GPU-enabled +tuolumne,256,GPU-enabled +matrix,26,GPU-enabled diff --git a/experimental/multi-cluster/testdata/prediction.csv b/experimental/multi-cluster/testdata/prediction.csv new file mode 100644 index 0000000..e0901ed --- /dev/null +++ b/experimental/multi-cluster/testdata/prediction.csv @@ -0,0 +1,7 @@ +App,Args,Ranks,dane,mammoth,matrix-cpu,matrix-gpu,tioga-cpu,tioga-gpu,tuolumne-cpu,tuolumne-gpu +cpu-solver,--mesh-small,32,0.9,1.3,0.8,,1.1,,1.0, +cpu-solver,--mesh-large,128,0.9,1.1,0.8,,0.7,,1.0, +gpu-trainer,--batch-64,64,,,,0.9,,1.1,,1.3 +portable-md,--size-small,32,0.9,1.0,0.8,1.5,0.9,1.1,1.0,1.2 +portable-md,--size-medium,64,0.8,1.1,0.9,1.2,1.0,1.4,1.1,1.3 +portable-md,--size-large,128,0.8,0.9,0.7,1.0,0.9,1.1,1.0,1.3 diff --git a/experimental/synthesize/lassen/generate_synthetic_job_stream.py b/experimental/synthesize/lassen/generate_synthetic_job_stream.py index 3765e69..4abdbc9 100755 --- a/experimental/synthesize/lassen/generate_synthetic_job_stream.py +++ b/experimental/synthesize/lassen/generate_synthetic_job_stream.py @@ -199,7 +199,9 @@ def main(): raise SystemExit(f"error: {exc}") from exc with open(args.output_csv, "w", newline="", encoding="utf-8") as destination: - writer = csv.DictWriter(destination, fieldnames=OUTPUT_COLUMNS) + writer = csv.DictWriter( + destination, fieldnames=OUTPUT_COLUMNS, lineterminator="\n" + ) writer.writeheader() writer.writerows(synthetic) diff --git a/tests/test_mpi_performance_dispatch.py b/tests/test_mpi_performance_dispatch.py index 0764600..5bd512c 100644 --- a/tests/test_mpi_performance_dispatch.py +++ b/tests/test_mpi_performance_dispatch.py @@ -11,7 +11,7 @@ from pathlib import Path -def run_dispatch(command, output, expected_jobs): +def run_dispatch(command, output, expected_jobs, expected_dropped=0): result = subprocess.run( command + ["--output", str(output)], check=False, @@ -46,8 +46,10 @@ def run_dispatch(command, output, expected_jobs): for name, value in re.findall(r"([a-z_]+)=([0-9.eE+-]+)", overall_lines[0]) } assert int(metrics["jobs"]) == expected_jobs - assert metrics["average_turnaround_time"] >= metrics["average_run_time"] - assert metrics["average_bounded_slowdown"] >= 1.0 + assert int(metrics["dropped_jobs"]) == expected_dropped + if expected_jobs: + assert metrics["average_turnaround_time"] >= metrics["average_run_time"] + assert metrics["average_bounded_slowdown"] >= 1.0 return output.read_bytes(), metrics @@ -62,7 +64,7 @@ def main(): if Path(mpiexec).name == "srun" and "SLURM_JOB_ID" not in os.environ: print("SKIP: srun requires an active Slurm allocation") return 77 - fixture_dir = Path(source_dir) / "experimental" / "multi-cluster" + fixture_dir = Path(source_dir) / "experimental" / "multi-cluster" / "testdata" requirements = { "cpu-solver": "CPU-only", "gpu-trainer": "GPU-only", @@ -159,14 +161,45 @@ def main(): actual = float(row["actual_duration"]) predicted_limit = float(row["predicted_time_limit"]) actual_limit = float(row["actual_time_limit"]) + submitted_limit = float(row["submitted_time_limit"]) turnaround = float(row["predicted_turnaround"]) wait = float(row["estimated_wait"]) assert abs(estimated - duration / predicted) < 1e-9 assert abs(actual - duration / ground_truth) < 1e-9 assert abs(predicted_limit - limit / predicted) < 1e-9 assert abs(actual_limit - limit / ground_truth) < 1e-9 + assert submitted_limit >= actual + assert int(row["time_limit_doublings"]) >= 0 assert abs(turnaround - (wait + estimated)) < 1e-9 + allowed_applications = temp_dir / "allowed-applications.csv" + with allowed_applications.open("w", newline="") as stream: + writer = csv.writer(stream) + writer.writerow(("app", "sys_requirement")) + for app, requirement in requirements.items(): + if app != "gpu-trainer": + writer.writerow((app, requirement)) + allowlist_command = list(command) + allowlist_command[allowlist_command.index("--applications") + 1] = str( + allowed_applications + ) + allowlist_output = temp_dir / "allowlist.csv" + run_dispatch(allowlist_command, allowlist_output, expected_jobs) + with allowlist_output.open(newline="") as stream: + allowlist_rows = list(csv.DictReader(stream)) + assert {row["App"] for row in allowlist_rows} == { + "cpu-solver", + "portable-md", + } + + dropped_output = temp_dir / "dropped.csv" + dropped_command = command + ["--max-time-limit", "1"] + dropped_bytes, dropped_metrics = run_dispatch( + dropped_command, dropped_output, 0, expected_jobs + ) + assert dropped_bytes.count(b"\n") == 1 + assert dropped_metrics["average_run_time"] == 0 + if __name__ == "__main__": raise SystemExit(main()) From 1d9d1391b77057c2a523c2bc7b35d23f8608b876 Mon Sep 17 00:00:00 2001 From: Jae-Seung Yeom Date: Mon, 5 Oct 2026 00:28:16 -0700 Subject: [PATCH 3/3] Normalize Lassen synthetic traces for time-limit constraints MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - make exit_status optional unless --successful-only is requested - validate that historical time limits are positive - sort eligible jobs by submission time before sampling arrival windows - add --max-time-limit to enforce a target platform wall-time cap - normalize runtimes that exceed their effective time limits - apply minimum-duration filtering after runtime normalization - sample time limits conditionally from the selected runtime bucket - cap sampled runtimes when fractional bucket matching selects a shorter limit - guarantee duration does not exceed time_limit in generated jobs - emit generated CSV files with Unix line endings - document time-limit units, normalization, sampling, and Lassen’s 12-hour cap - add coverage for default limits, custom caps, invalid limits, and line endings --- experimental/synthesize/lassen/README.md | 45 +++++-- .../lassen/generate_synthetic_job_stream.py | 89 ++++++++++--- .../test_generate_synthetic_job_stream.py | 118 ++++++++++++++++++ 3 files changed, 225 insertions(+), 27 deletions(-) create mode 100644 experimental/synthesize/lassen/test_generate_synthetic_job_stream.py diff --git a/experimental/synthesize/lassen/README.md b/experimental/synthesize/lassen/README.md index 735a5e1..d854672 100644 --- a/experimental/synthesize/lassen/README.md +++ b/experimental/synthesize/lassen/README.md @@ -6,8 +6,9 @@ This directory contains two scripts: CSM allocation history, converts local timestamps to Unix epoch seconds, and can calculate job duration. 2. `generate_synthetic_job_stream.py` accepts a standardized historical job - trace and generates a synthetic trace by sampling arrival times, job - size/runtime pairs, and time limits separately. + trace and generates a synthetic trace by sampling arrival times and job + size/runtime pairs separately, then sampling each time limit conditionally + on the selected runtime. Both scripts use Python's standard library and require Python 3.9 or newer. @@ -119,7 +120,7 @@ and extra columns are ignored. | `submit_time` | Numeric timestamp, normally Unix epoch seconds | | `num_nodes` | Number of nodes associated with the job | | `duration` | Positive runtime in seconds; fractions are supported | -| `time_limit` | Time-limit value to sample | +| `time_limit` | Positive time-limit value in seconds to sample | | `exit_status` | Required only when `--successful-only` is used; zero means success | The generated `lassen_pbatch_scheduling_simulation.csv` is directly usable as @@ -131,10 +132,19 @@ time limit should both be in seconds. The script sorts eligible input jobs by numeric `submit_time`; the original CSV does not need to be pre-sorted. -### Eligibility filters - -- `--min-duration SECONDS` removes jobs whose duration is less than the given - value. A job exactly equal to the threshold remains eligible. +### Time-limit handling, eligibility filters, and normalization + +- `time_limit` is expressed in seconds, like `duration`; it is not a runtime + multiplier or a value in minutes. +- `--max-time-limit SECONDS` applies the target platform's maximum time limit. + A larger historical `time_limit` is set to this cap. The job remains + eligible; if its `duration` exceeds the resulting limit, its duration is + set to that limit. No platform maximum is applied when the option is omitted. +- After applying the optional platform maximum, an input job whose `duration` + exceeds its effective `time_limit` is normalized by setting `duration` to + `time_limit`. +- `--min-duration SECONDS` removes jobs whose normalized duration is less than + the given value. A job exactly equal to the threshold remains eligible. - `--successful-only` removes jobs whose `exit_status` is not `0`. - Without these options, all positive-duration jobs are eligible and the `exit_status` column is optional. @@ -156,9 +166,14 @@ For a requested count of `N` jobs, the script does the following: by default; `--with-replacement` enables bootstrap sampling. 3. **Time limit:** Assign every eligible historical job to a one-second bucket using `ceil(duration)`. Thus `(0, 1]` is bucket 1, `(1, 2]` is bucket 2, - and so on. For each synthetic job, uniformly select a `time_limit` from the - historical records in the same duration bucket. Repeated historical values - remain repeated in the bucket and retain their empirical frequency. + and so on. For each synthetic job, uniformly select a `time_limit` from all + eligible historical records in the sampled runtime's bucket. This is a + conditional sample, not an independent sample from the trace-wide + `time_limit` distribution. Repeated historical values remain repeated in + the bucket and retain their empirical frequency. If the selected limit is + slightly shorter than the sampled duration (possible when two fractional + durations share a bucket), the output duration is capped at that selected + limit. The three stages are independent except that `num_nodes` and `duration` remain paired and the time-limit bucket is selected from the sampled duration. @@ -170,11 +185,15 @@ submit_time,num_nodes,time_limit,duration ``` The output has one header row followed by exactly `N` synthetic jobs. +Every newly generated row satisfies `duration <= time_limit`. Existing CSVs +are not rewritten automatically when the generator changes; regenerate older +outputs to apply this invariant. ### Generate one trace The following creates 100,000 jobs using successful historical jobs that ran -for at least 60 seconds: +for at least 60 seconds. Lassen's platform time-limit cap is 43,200 seconds +(12 hours): ```bash ./generate_synthetic_job_stream.py \ @@ -182,6 +201,7 @@ for at least 60 seconds: synthetic_jobs_100000_min60s_successful.csv \ 100000 \ --min-duration 60 \ + --max-time-limit 43200 \ --successful-only ``` @@ -189,7 +209,7 @@ For reproducible results, provide a seed: ```bash ./generate_synthetic_job_stream.py historical.csv synthetic.csv 100000 \ - --min-duration 60 --successful-only --seed 2026 + --min-duration 60 --max-time-limit 43200 --successful-only --seed 2026 ``` Running the same command with the same input and seed produces identical @@ -204,6 +224,7 @@ for i in $(seq -w 1 10); do "synthetic_jobs_100000_min60s_successful_${i}.csv" \ 100000 \ --min-duration 60 \ + --max-time-limit 43200 \ --successful-only done ``` diff --git a/experimental/synthesize/lassen/generate_synthetic_job_stream.py b/experimental/synthesize/lassen/generate_synthetic_job_stream.py index 4abdbc9..80ac93f 100755 --- a/experimental/synthesize/lassen/generate_synthetic_job_stream.py +++ b/experimental/synthesize/lassen/generate_synthetic_job_stream.py @@ -27,7 +27,6 @@ "num_nodes", "duration", "time_limit", - "exit_status", } OUTPUT_COLUMNS = ("submit_time", "num_nodes", "time_limit", "duration") @@ -50,14 +49,18 @@ def duration_bucket(duration): return int(duration.to_integral_value(rounding=ROUND_CEILING)) -def read_eligible_jobs(path, minimum_duration, successful_only): - """Read the trace in source order and apply both optional filters.""" +def read_eligible_jobs( + path, minimum_duration, successful_only, maximum_time_limit=None +): + """Read, normalize/filter jobs, and sort them by submit time.""" eligible = [] with open(path, newline="", encoding="utf-8") as source: reader = csv.DictReader(source) if reader.fieldnames is None: raise ValueError("input CSV has no header") missing = REQUIRED_COLUMNS - set(reader.fieldnames) + if successful_only and "exit_status" not in reader.fieldnames: + missing.add("exit_status") if missing: raise ValueError("missing columns: " + ", ".join(sorted(missing))) @@ -67,22 +70,52 @@ def read_eligible_jobs(path, minimum_duration, successful_only): raise ValueError( f"line {line_number}: duration must be greater than zero" ) + if successful_only and row["exit_status"].strip() != "0": + continue + + time_limit = decimal_value( + row["time_limit"], "time_limit", line_number + ) + if time_limit <= 0: + raise ValueError( + f"line {line_number}: time_limit must be greater than zero" + ) + time_limit_text = row["time_limit"] + if maximum_time_limit is not None and time_limit > maximum_time_limit: + time_limit = maximum_time_limit + time_limit_text = str(maximum_time_limit) + + # A synthetic job cannot run beyond its effective time limit. + # Normalize the duration before applying the duration filter and + # before assigning the job to a duration bucket. + if duration > time_limit: + duration = time_limit + duration_text = time_limit_text + else: + duration_text = row["duration"] + # "Ignore jobs shorter than X" means a job of exactly X seconds - # remains eligible. + # remains eligible. Use the normalized duration here so every + # emitted job still satisfies the requested minimum. if duration < minimum_duration: continue - if successful_only and row["exit_status"].strip() != "0": - continue eligible.append( { "submit_time": row["submit_time"], + "submit_time_decimal": decimal_value( + row["submit_time"], "submit_time", line_number + ), "num_nodes": row["num_nodes"], - "duration": row["duration"], + "duration": duration_text, "duration_decimal": duration, - "time_limit": row["time_limit"], + "time_limit": time_limit_text, } ) + # A general trace need not already be ordered. Sorting makes a consecutive + # slice below represent a real interval of the historical arrival stream. + # Python's stable sort retains source order for simultaneous submissions. + eligible.sort(key=lambda job: job["submit_time_decimal"]) return eligible @@ -90,8 +123,9 @@ def generate_jobs(eligible, count, rng, with_replacement=False): """Construct synthetic jobs using the requested independent sampling. 1. Choose one uniformly random starting index and copy ``count`` - consecutive submit times from the eligible trace. Keeping a contiguous - window preserves the historical arrival pattern and interarrival gaps. + consecutive submit times from the time-sorted eligible trace. Keeping + a contiguous window preserves the historical arrival pattern and + interarrival gaps. 2. Independently sample ``count`` historical jobs uniformly, and retain each selected job's (num_nodes, duration) pair. The pair stays intact @@ -110,8 +144,8 @@ def generate_jobs(eligible, count, rng, with_replacement=False): f"requested {count} jobs, but only {len(eligible)} are eligible" ) - # Stage 1: the window contains consecutive *eligible* jobs. If filtering - # removes rows, the remaining submit times retain their original order. + # Stage 1: the window contains consecutive *eligible* jobs in submit-time + # order. If filtering removes rows, those gaps are simply skipped. start = rng.randrange(len(eligible) - count + 1) submit_times = [job["submit_time"] for job in eligible[start : start + count]] @@ -132,12 +166,18 @@ def generate_jobs(eligible, count, rng, with_replacement=False): synthetic = [] for submit_time, pair in zip(submit_times, pair_samples): bucket = duration_bucket(pair["duration_decimal"]) + time_limit_text = rng.choice(limits_by_bucket[bucket]) + time_limit = Decimal(time_limit_text) + if pair["duration_decimal"] > time_limit: + duration_text = time_limit_text + else: + duration_text = pair["duration"] synthetic.append( { "submit_time": submit_time, "num_nodes": pair["num_nodes"], - "time_limit": rng.choice(limits_by_bucket[bucket]), - "duration": pair["duration"], + "time_limit": time_limit_text, + "duration": duration_text, } ) return synthetic, start @@ -154,6 +194,14 @@ def main(): metavar="SECONDS", help="ignore jobs shorter than this duration (default: 0)", ) + parser.add_argument( + "--max-time-limit", + metavar="SECONDS", + help=( + "platform maximum time limit; cap larger limits and any longer " + "durations to this value (default: disabled)" + ), + ) parser.add_argument( "--successful-only", action="store_true", @@ -177,6 +225,14 @@ def main(): parser.error(f"invalid --min-duration: {args.min_duration!r}") if not minimum_duration.is_finite() or minimum_duration < 0: parser.error("--min-duration must be a finite, nonnegative number") + maximum_time_limit = None + if args.max_time_limit is not None: + try: + maximum_time_limit = Decimal(args.max_time_limit) + except InvalidOperation: + parser.error(f"invalid --max-time-limit: {args.max_time_limit!r}") + if not maximum_time_limit.is_finite() or maximum_time_limit <= 0: + parser.error("--max-time-limit must be a finite, positive number") if args.num_jobs <= 0: parser.error("num_jobs must be greater than zero") @@ -187,7 +243,10 @@ def main(): try: eligible = read_eligible_jobs( - args.input_csv, minimum_duration, args.successful_only + args.input_csv, + minimum_duration, + args.successful_only, + maximum_time_limit, ) synthetic, window_start = generate_jobs( eligible, diff --git a/experimental/synthesize/lassen/test_generate_synthetic_job_stream.py b/experimental/synthesize/lassen/test_generate_synthetic_job_stream.py new file mode 100644 index 0000000..9b0c74c --- /dev/null +++ b/experimental/synthesize/lassen/test_generate_synthetic_job_stream.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python3 +"""Tests for configurable synthetic-trace eligibility rules.""" + +import csv +import subprocess +import sys +import tempfile +import unittest +from decimal import Decimal +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from generate_synthetic_job_stream import read_eligible_jobs # noqa: E402 + + +class MaximumTimeLimitTests(unittest.TestCase): + def write_trace(self, directory): + path = Path(directory) / "input.csv" + with path.open("w", newline="", encoding="utf-8") as destination: + writer = csv.DictWriter( + destination, + fieldnames=( + "submit_time", + "num_nodes", + "duration", + "time_limit", + ), + ) + writer.writeheader() + writer.writerows( + ( + { + "submit_time": "2", + "num_nodes": "1", + "duration": "99", + "time_limit": "150", + }, + { + "submit_time": "1", + "num_nodes": "2", + "duration": "100", + "time_limit": "150", + }, + ) + ) + return path + + def test_limit_is_disabled_by_default(self): + with tempfile.TemporaryDirectory() as directory: + jobs = read_eligible_jobs( + self.write_trace(directory), Decimal(0), successful_only=False + ) + + self.assertEqual([job["time_limit"] for job in jobs], ["150", "150"]) + + def test_custom_limit_caps_short_jobs_and_removes_long_jobs(self): + with tempfile.TemporaryDirectory() as directory: + jobs = read_eligible_jobs( + self.write_trace(directory), + Decimal(0), + successful_only=False, + maximum_time_limit=Decimal(100), + ) + + self.assertEqual(len(jobs), 1) + self.assertEqual(jobs[0]["time_limit"], "100") + self.assertEqual(jobs[0]["duration"], "99") + + def test_cli_rejects_nonpositive_limit(self): + script = Path(__file__).resolve().with_name( + "generate_synthetic_job_stream.py" + ) + result = subprocess.run( + [ + sys.executable, + str(script), + "input.csv", + "output.csv", + "1", + "--max-time-limit", + "0", + ], + check=False, + capture_output=True, + text=True, + ) + + self.assertEqual(result.returncode, 2) + self.assertIn("must be a finite, positive number", result.stderr) + + def test_cli_writes_unix_line_endings(self): + script = Path(__file__).resolve().with_name( + "generate_synthetic_job_stream.py" + ) + with tempfile.TemporaryDirectory() as directory: + output = Path(directory) / "output.csv" + result = subprocess.run( + [ + sys.executable, + str(script), + str(self.write_trace(directory)), + str(output), + "2", + "--seed", + "7", + ], + check=False, + capture_output=True, + text=True, + ) + + self.assertEqual(result.returncode, 0, result.stderr) + self.assertNotIn(b"\r\n", output.read_bytes()) + + +if __name__ == "__main__": + unittest.main()