ACM Transactions on Mathematical Software (TOMS)
Iterative Methods for Sparse Linear Systems
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CIT '10 Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology
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Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
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ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Automatically tuning sparse matrix-vector multiplication for GPU architectures
HiPEAC'10 Proceedings of the 5th international conference on High Performance Embedded Architectures and Compilers
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
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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 implementation to perform SpMV on the GPU using atomic operations. We compare SCOO performance to existing formats of the NVIDIA Cusp library using large sparse matrices. Our results for single-precision floating-point matrices show that SCOO outperforms the COO and CSR format for all tested matrices and the HYB format for all tested unstructured matrices on a single GPU. Furthermore, our dual-GPU implementation achieves an efficiency of 94% on average. Due to the lower performance of existing CUDA-enabled GPUs for atomic operations on double-precision floating-point numbers the SCOO implementation for double-precision does not consistently outperform the other formats for every unstructured matrix. Overall, the average speedup of SCOO for the tested benchmark dataset is 3.33 (1.56) compared to CSR, 5.25 (2.42) compared to COO, 2.39 (1.37) compared to HYB for single (double) precision on a Tesla C2075. Furthermore, comparison to a Sandy-Bridge CPU shows that SCOO on a Fermi GPU outperforms the multi-threaded CSR implementation of the Intel MKL Library on an i7-2700K by a factor between 5.5 (2.3) and 18 (12.7) for single (double) precision. Source code is available at https://github.com/danghvu/cudaSpmv.