Matrix multiplication on gpu using cuda with cublas. Matrix matrix multiplication on the gpu with nvidia cuda in the previous article we discussed monte carlo methods and their implementation in cuda, focusing on option pricing. It appears that many straightforward cuda implementations including matrix multiplication can outperform the cpu if given a large enough data set, as explained and demonstrated here. Uses a variety of dense vector algebra, and sparse matrixdense vector algebra spmv. Cublas matrix multiplication with cstyle arrays posted on 17 june 20. The cublas library cublas is an implementation of blas basic linear algebra.
Other baselines typically faired worse than cublas. In fact, commercial codes for large lp problems seem to have predated codes for sparse linear equations even though solving a sparse lpproblem requires. This paper covers the multithreaded parallel processing of a sparse triangular solver for a linear system with a sparse coefficient matrix, focusing on its application to a parallel iccg solver. New rowgrouped csr format for storing sparse matrices on gpu. We compare against cublas cuda 8 matrix multiplication. To overcome the limitations, we propose escort, an ef. Our algorithm is written in cuda and uses a block size of. If a matrix contains many zeros, converting the matrix to sparse storage saves memory. Sparse matrices, spmv, parallel computing, gpu, thread computing, cuda.
The basic model by which applications use the cublas library is to create matrix and. Cublas is a highlevel api designed for compatibility with the original fortran subprograms. Spgemm implementation for highly sparse matrices 80% of the set. Matrixmatrix multiplication on the gpu with nvidia cuda. Pdf on jan 1, 1984, sergio pissanetzky and others published sparse matrix technology find, read and cite all the research you need on researchgate. I would like to compute c s b, with s a sparse matrix in format coo or csc in 0indexing and c and b two dense matrix in colmajor and 0indexing format. Themajorapplication of sparse matrix techniques in optimization up to the present has been in the implementation of the simplex method for linear programming lp see, e. Implementing sparse matrix vector multiplication on throughputoriented processors nathan bell and michael garland proceedings of supercomputing 09 efficient sparse matrix vector multiplication on cuda nathan bell and michael garland nvidia technical report nvr2008004, december 2008 iterative methods for sparse linear systems yousef saad. Sparse matrices provide efficient storage of double or logical data that has a large percentage of zeros. Jun 17, 20 cublas matrix multiplication with cstyle arrays posted on 17 june 20. It allows the user to access the computational resources of nvidia graphics processing unit gpu.
A scalable sparse matrixvector multiplication kernel for energyefficient sparseblas on fpgas conference paper pdf available february 2014 with 643 reads how we measure reads. Matrix computations on the gpu, cublas and magma by example. The performance of sparse matrix algorithms tends to be much lower than that of dense matrices due to two key factors. The cublas library is an implementation of blas basic linear algebra subprograms on top of the nvidiacuda runtime. The source code of the rgcsr format is available as a part of the template. Large matrices in big applications always sparse ef. Gpu kernels for block sparse weights scott gray, alec radford and diederik p. Contribute to srkiranrajspmm development by creating an account on github. Besides, although sparse matrix computation using compressed data structure could save memory space, there is overhead to decode the sparse format at runtime. Empirical speedups, in terms of relative gflops, of block sparse matrix multiplication with a 12288 12288 weight matrix, a minibatch size of 32, and a block size of 32. Let us use a smaller factor din order to investigate how sparse the x matrix would be. Sparse matrices sparse many elements are zero dense few elements are zero example of sparse matrices diagonal tridiagonal lower triangular. Adaptive sparse matrixmatrix multiplication on the gpu people.
While full or dense matrices store every single element in memory regardless of value, sparse matrices store only the nonzero elements and their row indices. In the sparse matrix, half of the total elements are zero. Sparse matrix storage formats for sparse blas routines. Unlike the previous baseline, the computational complexities. After this transformation, the convolution is replaced by a single matrix multiplication in fig. Get unlimited access to the best stories on medium and support writers while youre at it. A scalable sparse matrixvector multiplication kernel for. Today, we take a step back from finance to introduce a couple of essential topics, which will help us to write more advanced and efficient. In order to implement the preconditioned bicgstab and cg we use the sparse matrix vector multiplication 3, 15 and the sparse triangular solve 8, 16 implemented in the cusparse library.
Pdf a scalable sparse matrixvector multiplication kernel. The api reference guide for cusolver, the cuda sparse matix library. It is implemented on top of the nvidia cuda runtime. Incompletelu and cholesky preconditioned iterative. Sparse matrix vector spmv multiplication and sparse matrix. Introduction deep neural networks dnns 28 have been widely. Sparsemodelmatrices the comprehensive r archive network. The sparse triangular solve is not as well known, so we brie y point out the strategy. Spmv sparse matrix vector multiplication, saxpy scalar alpha x plus y. The sparse matrix vector multiplication has already been extensively studied in the following references 3, 15. Unfortunately lme4does not use a sparse xmatrix for the. Cublas has decently optimized calls, but it stuck with columnfirst indexing, which makes it mindbogglingly annoying to use in c code. Cublas is not necessary to show the gpu outperform the cpu, though cublas would probably outperform it more. Efficient sparsematrix multivector product on gpus nsfpar.
Incompletelu and cholesky preconditioned iterative methods. Suitesparse, a suite of sparse matrix algorithms, geared toward the direct solution of sparse linear systems. Cublas has decently optimized calls, but it stuck with columnfirst indexing, which makes it. Thunking allows interfacing to existing applications without any changes during each call, the wrappers allocate gpu memory, copy source data from cpu memory space to gpu. Sparse linear algebra the nvidia cuda sparse matrix library cusparse provides gpuaccelerated basic linear algebra subroutines for sparse matrices that perform up to 5x faster than cpuonly alternatives. High performance math routines for your applications. Recently when i used cusparse and cublas in cuda toolkit 6.
Since the user is unable to synchronize ctas in cuda, this is the. Petsc, a large c library, containing many different matrix solvers for a variety of matrix storage formats. Lu and cholesky preconditioned iterative methods is spent in the sparse matrix vector multiplication and triangular solve. The cusolver library is a highlevel package based on the cublas and cusparse libraries. Pdf extensive set of examples how to use modern gpus to high performance. Design principles for sparse matrix multiplication on the gpu. Efficient sparse matrix vector multiplication on cachebased gpus, reguly and mike giles, 2012. Batched gemm api in cublas performs many small matrix multiplies sparse matrixvector multiply in cusparse is up to 2x faster using ellhyb format significant optimization to ieee floatingpoint operations implemented in software i. On parallel solution of sparse triangular linear systems. Sparse linear algebra in cuda hpc algorithms and applications alexander poppl. Unstructured sparse matrices airline flight matrix. In all my experiments, i used cusparsescsrmm in cusparse and cublassgemm in cublas.
May be mapped into a 1d array so that a mapping function can be used to locate an element. Generalpurpose sparse matrix building blocks using the. High performance computing algorithms and applications, november 18th 2015. Getting started with gsl gnu scientific library on windows, macos and linux. A scalable sparse matrix vector multiplication kernel for energyefficient sparse blas on fpgas conference paper pdf available february 2014 with 643 reads how we measure reads. Developer reference for intel math kernel library fortran. A scalable sparse matrix vector multiplication kernel for energyefficient sparse blas on fpgas richard dorrance ee department, ucla. Why cusparse is much slower than cublas for sparse matrix.
968 345 1241 90 316 1088 1413 937 317 568 1484 28 1069 272 117 878 379 1510 1249 110 102 71 1200 974 1569 1146 812 463 766 950 1069 41 941 714 1444 1129 1209 892 1123 416 261 1189 705 669 1401 792 163 1365