Search results for "Sparse matrix-vector multiplication"
showing 4 items of 4 documents
Iterative sparse matrix-vector multiplication for accelerating the block Wiedemann algorithm over GF(2) on multi-graphics processing unit systems
2012
SUMMARY The block Wiedemann (BW) algorithm is frequently used to solve sparse linear systems over GF(2). Iterative sparse matrix–vector multiplication is the most time-consuming operation. The necessity to accelerate this step is motivated by the application of BW to very large matrices used in the linear algebra step of the number field sieve (NFS) for integer factorization. In this paper, we derive an efficient CUDA implementation of this operation by using a newly designed hybrid sparse matrix format. This leads to speedups between 4 and 8 on a single graphics processing unit (GPU) for a number of tested NFS matrices compared with an optimized multicore implementation. We further present…
The Sliced COO Format for Sparse Matrix-Vector Multiplication on CUDA-enabled GPUs
2012
Abstract Existing formats for Sparse Matrix-Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an effcient CUDA implementation to perform SpMV on the GPU. While previous work shows experiments on small to medium-sized sparse matrices, we perform evaluations on large sparse matrices. We compared SCOO performance to existing formats of the NVIDIA Cusp library. Our resutls on a Fermi GPU show that SCOO outperforms the COO and CSR format for all tested matrices and the HYB format for all tested unstructured matrices. Furthermore, comparison to a Sandy-Bridge CPU sho…
LightSpMV: Faster CSR-based sparse matrix-vector multiplication on CUDA-enabled GPUs
2015
Compressed sparse row (CSR) is a frequently used format for sparse matrix storage. However, the state-of-the-art CSR-based sparse matrix-vector multiplication (SpMV) implementations on CUDA-enabled GPUs do not exhibit very high efficiency. This has motivated the development of some alternative storage formats for GPU computing. Unfortunately, these alternatives are incompatible with most CPU-centric programs and require dynamic conversion from CSR at runtime, thus incurring significant computational and storage overheads. We present LightSpMV, a novel CUDA-compatible SpMV algorithm using the standard CSR format, which achieves high speed by benefiting from the fine-grained dynamic distribut…
CUDA-enabled Sparse Matrix–Vector Multiplication on GPUs using atomic operations
2013
We propose the Sliced Coordinate Format (SCOO) for Sparse Matrix-Vector Multiplication on GPUs.An associated CUDA implementation which takes advantage of atomic operations is presented.We propose partitioning methods to transform a given sparse matrix into SCOO format.An efficient Dual-GPU implementation which overlaps computation and communication is described.Extensive performance comparisons of SCOO compared to other formats on GPUs and CPUs are provided. Existing formats for Sparse Matrix-Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an efficient CUDA i…