[Project] Docs minor update & add perf section

Author: EgorOrachyov

CHANGELOG.md

@@ -22,7 +22,6 @@ Added new vector C API, exposed vector primitive into python-package.
 - Vector creation (empty, from data, with random data)
 - Matrix-vector operations (matrix-vector and vector-matrix multiplication)
 - Vector-vector operations (element-wise addition)
-- Matrix operations (equality, reduce to value, extract sub-vector)
 - Vector data extraction (as list of indices)
 - Vector syntax sugar (pretty string printing, slicing, iterating through non-zero indices)
 - Matrix operations (extract row or matrix column as sparse vector, reduce matrix (optionally transposed) to vector)

README.md

@@ -51,8 +51,8 @@ prototyping algorithms on a local computer for later running on a powerful serve
 ### Platforms
 
 - Linux based OS (tested on Ubuntu 20.04)
-- Windows (not tested yet)
-- macOS (not tested yet)
+- Windows (coming soon)
+- macOS (coming soon)
 
 ### Simple example
 
@@ -74,9 +74,32 @@ b[2, 1] = True
 print(a, b, a.mxm(b), sep="\n")
 ```
 
+### Performance
+
+Sparse Boolean matrix-matrix multiplication evaluation results are listed bellow.
+Machine configuration: PC with Ubuntu 20.04, Intel Core i7-6700 3.40GHz CPU, DDR4 64Gb RAM, GeForce GTX 1070 GPU with 8Gb VRAM. 
+
+![time](https://github.com/JetBrains-Research/cuBool/raw/master/docs/pictures/mxm-perf-time.svg?raw=true&sanitize=true)
+![mem](https://github.com/JetBrains-Research/cuBool/raw/master/docs/pictures/mxm-perf-mem.svg?raw=true&sanitize=true)
+
+The matrix data is selected from the SuiteSparse Matrix Collection [link](https://sparse.tamu.edu).
+
+| Matrix name              | # Rows      | Nnz M       | Nnz/row   | Max Nnz/row | Nnz M^2     |
+|---                       |---:         |---:         |---:       |---:         |---:         |
+| SNAP/amazon0312          | 400,727     | 3,200,440   | 7.9       | 10          | 14,390,544  |
+| LAW/amazon-2008          | 735,323     | 5,158,388   | 7.0       | 10          | 25,366,745  |
+| SNAP/web-Google          | 916,428     | 5,105,039   | 5.5       | 456         | 29,710,164  |
+| SNAP/roadNet-PA          | 1,090,920   | 3,083,796   | 2.8       | 9           | 7,238,920   |
+| SNAP/roadNet-TX	       | 1,393,383   | 3,843,320   | 2.7       | 12          | 8,903,897   |
+| SNAP/roadNet-CA	       | 1,971,281   | 5,533,214   | 2.8       | 12          | 12,908,450  |
+| DIMACS10/netherlands_osm | 2,216,688   | 4,882,476   | 2.2       | 7           | 8,755,758   |
+  
+Detailed comparison is available in the full paper text at 
+[link](https://github.com/YaccConstructor/articles/blob/master/2021/GRAPL/Sparse_Boolean_Algebra_on_GPGPU/Sparse_Boolean_Algebra_on_GPGPU.pdf).
+
 ### Installation
 
-If you are running **Linux based** OS (tested on Ubuntu 20.04) you can download the official
+If you are running **Linux-based** OS (tested on Ubuntu 20.04) you can download the official
 PyPI **pycubool** python package, which includes compiled library source code
 with Cuda and Sequential computations support. Installation process 
 requires only `python3` to be installed on your machine. Python can be installed 
@@ -102,7 +125,7 @@ These steps are required if you want to build library for your specific platform
 
 ### Requirements
 
-- Linux based OS (tested on Ubuntu 20.04)
+- Linux-based OS (tested on Ubuntu 20.04)
 - CMake Version 3.15 or higher
 - CUDA Compatible GPU device (to run Cuda computations)
 - GCC Compiler 

docs/pictures/mxm-perf-mem.svg

@@ -31,8 +31,6 @@ prototyping algorithms on a local computer for later running on a powerful serve
 
 ### Features
 
-- C API for performance-critical computations
-- Python package for every-day tasks
 - Cuda backend for computations
 - Cpu backend for computations
 - Matrix/vector creation (empty, from data, with random data)
@@ -47,6 +45,29 @@ prototyping algorithms on a local computer for later running on a powerful serve
 - GraphViz (export single matrix or set of matrices as a graph with custom color and label settings)
 - Debug (matrix string debug markers, logging)
 
+### Performance
+
+Sparse Boolean matrix-matrix multiplication evaluation results are listed bellow.
+Machine configuration: PC with Ubuntu 20.04, Intel Core i7-6700 3.40GHz CPU, DDR4 64Gb RAM, GeForce GTX 1070 GPU with 8Gb VRAM. 
+
+![time](https://github.com/JetBrains-Research/cuBool/raw/master/docs/pictures/mxm-perf-time.svg?raw=true&sanitize=true)
+![mem](https://github.com/JetBrains-Research/cuBool/raw/master/docs/pictures/mxm-perf-mem.svg?raw=true&sanitize=true)
+
+The matrix data is selected from the SuiteSparse Matrix Collection [link](https://sparse.tamu.edu).
+
+| Matrix name              | # Rows      | Nnz M       | Nnz/row   | Max Nnz/row | Nnz M^2     |
+|---                       |---:         |---:         |---:       |---:         |---:         |
+| SNAP/amazon0312          | 400,727     | 3,200,440   | 7.9       | 10          | 14,390,544  |
+| LAW/amazon-2008          | 735,323     | 5,158,388   | 7.0       | 10          | 25,366,745  |
+| SNAP/web-Google          | 916,428     | 5,105,039   | 5.5       | 456         | 29,710,164  |
+| SNAP/roadNet-PA          | 1,090,920   | 3,083,796   | 2.8       | 9           | 7,238,920   |
+| SNAP/roadNet-TX	       | 1,393,383   | 3,843,320   | 2.7       | 12          | 8,903,897   |
+| SNAP/roadNet-CA	       | 1,971,281   | 5,533,214   | 2.8       | 12          | 12,908,450  |
+| DIMACS10/netherlands_osm | 2,216,688   | 4,882,476   | 2.2       | 7           | 8,755,758   |
+  
+Detailed comparison is available in the full paper text at 
+[link](https://github.com/YaccConstructor/articles/blob/master/2021/GRAPL/Sparse_Boolean_Algebra_on_GPGPU/Sparse_Boolean_Algebra_on_GPGPU.pdf).
+
 ### Simple example
 
 Create sparse matrices, compute matrix-matrix product and print the result to the output: