This is the source code of two papers:
(1) Weifeng Liu and Brian Vinter, "An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data". Parallel and Distributed Processing Symposium, 2014 IEEE 28th International (IPDPS '14), pp.370-381, 19-23 May 2014. [pdf][slides].
(2) Weifeng Liu and Brian Vinter, "A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors". Journal of Parallel and Distributed Computing (JPDC), pp.47-61, Volume 85, November 2015. (Extended version of the IPDPS '14 paper) [pdf][slides].
Contact: Weifeng Liu and Brian Vinter (vinter at nbi.ku.dk).
Now this work has two separate branches: CUDA and OpenCL. The CUDA version requires an nVidia GPU, CUDA SDK and CUSP library. The OpenCL version only needs an OpenCL-enabled GPU and OpenCL compiling environment.
Update (April 2016): fixed a bug of processing long rows.
To use this SpGEMM program, the first thing you need to do is to change the 'Makefile' with correct CUDA installation path and OpenCL libs path. Further, if you are using a CUDA device, check its compute capability (e.g., 3.5, 5.0 or above) and change item like '-arch=sm_35' if needed. Then you can build the code.
This program executes C=AB operation, where A, B and C are all sparse matrices.
You can either (1) use bhSPARSE class and call its SpGEMM method in your own code, or (2) load an off-line square matrix file (*.mtx in matrix market format) as input matrix A, then benchmark C=A^2 operation. In 'main.cpp' file, see function 'test_small_spgemm()' or 'benchmark_spgemm()' for details.
Here are some command-line execution examples using CUDA version:
(1) run SpGEMM on a small matrix
./spgemm -cuda -spgemm 0
(2) run SpGEMM on poisson5pt matrices generated by CUSP
./spgemm -cuda -spgemm 1
(3) run SpGEMM on poisson9pt matrices generated by CUSP
./spgemm -cuda -spgemm 2
(4) run SpGEMM on poisson7pt matrices generated by CUSP
./spgemm -cuda -spgemm 3
(5) run SpGEMM on poisson27pt matrices generated by CUSP
./spgemm -cuda -spgemm 4
(6) run SpGEMM on a matrix loaded from a matrix market file
./spgemm -cuda -spgemm cage4.mtx
(7) run SpGEMM on a matrix loaded from a matrix market file
./spgemm -cuda -spgemm /home/username/matrices/cage4.mtx
Here are some command-line execution examples using OpenCL version:
(1) run SpGEMM on a small matrix
./spgemm -cuda -spgemm 0
(2) run SpGEMM on a matrix loaded from a matrix market file
./spgemm -opencl -spgemm cage4.mtx
(3) run SpGEMM on a matrix loaded from a matrix market file
./spgemm -opencl -spgemm /home/username/matrices/cage4.mtx
(4) run SpGEMM (using re-allocatable system memory of AMD APU) on a matrix loaded from a matrix market file
./spgemm -opencl-hcmp -spgemm /home/username/matrices/cage4.mtx
The SpGEMM supports single precision and double precision floating-point numbers. The default data type is 64-bit double precision. If 32-bit single precision is required, change typedef value_type in 'common.h' in the CUDA version. For the OpenCL version, change typedef value_type in 'common.h' and typedef vT in files 'SpGEMM_EM_kernels.cl', 'SpGEMM_ESC_0_1_kernels.cl', 'SpGEMM_ESC_2heap_kernels.cl', 'SpGEMM_ESC_bitonic_kernels.cl' and 'SpGEMM_copyCt2C_kernels.cl'.
The CUDA version has been tested on nVidia GeForce GT 650M, GTX 680, GTX Titan, GTX Titan Black and GTX 980 with CUDA SDK v6.0/v6.5, CUSP v0.4.0 and multiple operating systems (Mac OS X v10.9 and Ubuntu v12.04/v14.04).
The OpenCL version has been tested on AMD Radeon HD 7970, R9 290X and A10-7850k APU with OpenCL v1.2/v2.0 and Ubuntu v12.04/v14.04.