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[Puzzle 24] Fix benchmark timing #102

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Oct 7, 2025
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[Puzzle 24] Fix benchmark timing #102

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62 changes: 30 additions & 32 deletions book/src/puzzle_24/warp_sum.md
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Original file line number Diff line number Diff line change
Expand Up @@ -136,11 +136,13 @@ total = warp_sum(partial_product)

```mojo
if lane_id() == 0:
output[0] = total
output[global_i // WARP_SIZE] = total
```

**Why only lane 0?** All lanes have the same `total` value after `warp_sum()`, but we only want to write once to avoid race conditions.

**Why not write to `output[0]`?** Flexibility, function can be used in cases where there is more than one warp. i.e. The result from each warp is written to the unique location `global_i // WARP_SIZE`.

**`lane_id()`:** Returns 0-31 (NVIDIA) or 0-63 (AMD) - identifies which lane within the warp.

</div>
Expand Down Expand Up @@ -280,10 +282,10 @@ else:
total = warp_sum(partial_product)

if lane_id() == 0:
output.store[1](0, 0, total)
output.store[1](idx // WARP_SIZE, 0, total)
```

**Storage pattern:** `output.store[1](0, 0, total)` stores 1 element at position (0, 0) in the output tensor.
**Storage pattern:** `output.store[1](idx // WARP_SIZE, 0, total)` stores 1 element at position `(idx // WARP_SIZE, 0)` in the output tensor.

**Same warp logic:** `warp_sum()` and lane 0 writing work identically in functional approach.

Expand Down Expand Up @@ -444,42 +446,38 @@ Testing SIZE=65536 x WARP_SIZE, BLOCKS=65536 (Massive Scale)
Running traditional_65536x
Running simple_warp_65536x
Running functional_warp_65536x
| name | met (ms) | iters |
| ---------------------- | ------------------ | ----- |
| traditional_1x | 1.0263419180000002 | 1000 |
| simple_warp_1x | 1.025756103 | 1000 |
| functional_warp_1x | 1.027618774 | 1000 |
| traditional_4x | 1.026372558 | 1000 |
| simple_warp_4x | 1.0274108880000001 | 1000 |
| functional_warp_4x | 1.0272440180000002 | 1000 |
| traditional_32x | 1.029869628 | 1000 |
| simple_warp_32x | 1.029203002 | 1000 |
| functional_warp_32x | 1.0293903800000002 | 1000 |
| traditional_256x | 1.055470581 | 1000 |
| simple_warp_256x | 1.0549002680000001 | 1000 |
| functional_warp_256x | 1.054106567 | 1000 |
| traditional_2048x | 1.170297851 | 1000 |
| simple_warp_2048x | 1.1691909169999999 | 1000 |
| functional_warp_2048x | 1.166839843 | 1000 |
| traditional_16384x | 6.470711037837837 | 185 |
| simple_warp_16384x | 6.482257572972973 | 185 |
| functional_warp_16384x | 6.414636946524065 | 187 |
| traditional_65536x | 22.48350437735849 | 53 |
| simple_warp_65536x | 22.561115754716983 | 53 |
| functional_warp_65536x | 22.399149188679246 | 53 |
| name | met (ms) | iters |
| ---------------------- | --------------------- | ----- |
| traditional_1x | 0.00460128 | 100 |
| simple_warp_1x | 0.00574047 | 100 |
| functional_warp_1x | 0.00484192 | 100 |
| traditional_4x | 0.00492671 | 100 |
| simple_warp_4x | 0.00485247 | 100 |
| functional_warp_4x | 0.00587679 | 100 |
| traditional_32x | 0.0062406399999999996 | 100 |
| simple_warp_32x | 0.0054918400000000004 | 100 |
| functional_warp_32x | 0.00552447 | 100 |
| traditional_256x | 0.0050614300000000004 | 100 |
| simple_warp_256x | 0.00488768 | 100 |
| functional_warp_256x | 0.00461472 | 100 |
| traditional_2048x | 0.01120031 | 100 |
| simple_warp_2048x | 0.00884383 | 100 |
| functional_warp_2048x | 0.007038720000000001 | 100 |
| traditional_16384x | 0.038533750000000005 | 100 |
| simple_warp_16384x | 0.0323264 | 100 |
| functional_warp_16384x | 0.01674271 | 100 |
| traditional_65536x | 0.19784991999999998 | 100 |
| simple_warp_65536x | 0.12870176 | 100 |
| functional_warp_65536x | 0.048680310000000004 | 100 |

Benchmarks completed!

WARP OPERATIONS PERFORMANCE ANALYSIS:
GPU Architecture: NVIDIA (WARP_SIZE=32) vs AMD (WARP_SIZE=64)
- 1 x WARP_SIZE: Single warp baseline
- 4 x WARP_SIZE: Few warps, warp overhead visible
- 32 x WARP_SIZE: Medium scale, warp benefits emerge
- 256 x WARP_SIZE: Large scale, dramatic warp advantages
- 2048 x WARP_SIZE: Massive scale, warp operations dominate
- 1,...,256 x WARP_SIZE: Grid size too small to benchmark
- 2048 x WARP_SIZE: Warp primative benefits emerge
- 16384 x WARP_SIZE: Large scale (512K-1M elements)
- 65536 x WARP_SIZE: Massive scale (2M-4M elements)
- Note: AMD GPUs process 2 x elements per warp vs NVIDIA!

Expected Results at Large Scales:
• Traditional: Slower due to more barrier overhead
Expand Down
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