GraphBLAS# is a GPGPU-based GraphBLAS-like API implementation in F#. To utilize GPGPUs we use Brahma.FSharp. So, GraphBLAS# can utilize any OpenCL-compatible device.
Option<'t>
to solve explicit/implicit zeroes problem. If graph has labels of type't
then adjacency matrix isMatrix<Option<'t>>
. Sparse storage contains only values forSome<'t>
cells.- Elementwise operations have type
AtLeastOne<'t1,'t2> -> Option<'t3>
to be shure thatNone op None
isNone
. Also developer explicitly controls what should beNone
.AtLeastOne
defined as fallows:So, type of matrix-matrix elementwise oertion istype AtLeastOne<'t1, 't2> = | Both of 't1*'t2 | Left of 't1 | Right of 't2
Matrix<Option<'t1>> -> Matrix<Option<'t2>> -> (AtLeastOne<'t1,'t2> -> Option<'t3>) -> Matrix<Option<'t3>>
. - No semirings. Just functions. Ofcourse one can implement semirings on the top of provided API.
- Minimal core: high-order functions allows us to minimaze core by functions unification. For example, such functions as matrix-matrix addition, matrix-matrix element-wise multiplication, masking all are partial case of
map2
function.
- Matrix-Matrix
- COO-COO element-wize
- CSR-CSR element-wize
- CSR-CSR multiplication
- COO transpose
- CSR transpose
- Vector-Matrix
- Dense-CSR multiplication
- COO-CSR multiplication
- Vector-Vector
- Dense-Dense element-wise
- ...
- Matrix
-
map
-
iter
- ...
-
- Vector
-
map
-
iter
-
filter
-
contains
- ...
-
- BFS
- Parent BFS
- Single Source Shortest Path
- Triangles Counting
- Local Clustering Coefficient
- Community Detection using Label Propagation
- Weakly Connected Components
- PageRank
Matrices from SuiteSparse matrix collection which we choose for evaluation.
Matrix | Size | NNZ | Squared matrix NNZ |
---|---|---|---|
wing | 62 032 | 243 088 | 714 200 |
luxembourg osm | 114 599 | 119 666 | 393 261 |
amazon0312 | 400 727 | 3 200 440 | 14 390 544 |
amazon-2008 | 735 323 | 5 158 388 | 25 366 745 |
web-Google | 916 428 | 5 105 039 | 29 710 164 |
webbase-1M | 1 000 005 | 3 105 536 | 51 111 996 |
cit-Patents | 3 774 768 | 16 518 948 | 469 |
Element-wise matrix-matrix evaluation results presented below. Time is measured in milliseconds. We perform our experiments on the PC with Ubuntu 20.04 installed and with the following hardware configuration: Intel Core i7–4790 CPU, 3.60GHz, 32GB DDR4 RAM with GeForce GTX 2070, 8GB GDDR6, 1.41GHz.
Matrix | Elemint-wise addition | Elemint-wise multiplication | ||||
---|---|---|---|---|---|---|
GraphBLAS-sharp | SuiteSparse | CUSP | GraphBLAS-sharp | SuiteSparse | ||
No AtLeastOne | AtLeastOne | |||||
wing | 4,3 ± 0,8 | 4,3 ± 0,6 | 2,7 ± 0,9 | 1,5 ± 0,0 | 3,7 ± 0,5 | 3,5 ± 0,4 |
luxembourg osm | 4,9 ± 0,7 | 4,1 ± 0,5 | 3,0 ± 1,1 | 1,2 ± 0,1 | 3,8 ± 0,6 | 3,0 ± 0,6 |
amazon0312 | 22,3 ± 1,3 | 22,1 ± 1,3 | 33,4 ± 0,8 | 11,0 ± 1,4 | 18,7 ± 0,9 | 35,7 ± 1,4 |
amazon-2008 | 38,7 ± 0,8 | 39,0 ± 1,0 | 55,9 ± 1,0 | 19,1 ± 1,4 | 34,5 ± 1,0 | 58,9 ± 1,9 |
web-Google | 43,4 ± 0,8 | 43,4 ± 1,1 | 67,2 ± 7,5 | 21,3 ± 1,3 | 39,0 ± 1,2 | 66,2 ± 0,4 |
webbase-1M | 63,6 ± 1,1 | 63,7 ± 1,3 | 86,5 ± 2,0 | 24,3 ± 1,3 | 54,5 ± 0,7 | 37,6 ± 5,6 |
cit-Patents | 26,9 ± 0,7 | 26,0 ± 0,7 | 183,4 ± 5,4 | 10,8 ± 0,6 | 24,3 ± 0,7 | 162,2 ± 1,7 |
Contributions, issues and feature requests are welcome. Feel free to check issues page if you want to contribute.
Make sure the following requirements are installed on your system:
- dotnet SDK 5.0 or higher
- OpenCL-compatible device and respective OpenCL driver
To build and run all tests:
- on Windows
build.cmd
- on Linux/macOS
./build.sh
To find more options look at MiniScaffold. We use it in our project.
This project licensed under MIT License. License text can be found in the license file.