Scans as Primitive Parallel Operations
IEEE Transactions on Computers
Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors
Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Fast scan algorithms on graphics processors
Proceedings of the 22nd annual international conference on Supercomputing
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Implementing sparse matrix-vector multiplication on throughput-oriented processors
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Model-driven autotuning of sparse matrix-vector multiply on GPUs
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
CSX: an extended compression format for spmv on shared memory systems
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Reduced-Bandwidth Multithreaded Algorithms for Sparse Matrix-Vector Multiplication
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Optimizing SpMV for Diagonal Sparse Matrices on GPU
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Optimization of sparse matrix-vector multiplication using reordering techniques on GPUs
Microprocessors & Microsystems
Automatically tuning sparse matrix-vector multiplication for GPU architectures
HiPEAC'10 Proceedings of the 5th international conference on High Performance Embedded Architectures and Compilers
clSpMV: A Cross-Platform OpenCL SpMV Framework on GPUs
Proceedings of the 26th ACM international conference on Supercomputing
StreamScan: fast scan algorithms for GPUs without global barrier synchronization
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
Accelerating sparse matrix-vector multiplication on GPUs using bit-representation-optimized schemes
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
| Hi-index | 0.00 |
SpMV is a key linear algebra algorithm and has been widely used in many important application domains. As a result, numerous attempts have been made to optimize SpMV on GPUs to leverage their massive computational throughput. Although the previous work has shown impressive progress, load imbalance and high memory bandwidth remain the critical performance bottlenecks for SpMV. In this paper, we present our novel solutions to these problems. First, we devise a new SpMV format, called blocked compressed common coordinate (BCCOO), which uses bit flags to store the row indices in a blocked common coordinate (COO) format so as to alleviate the bandwidth problem. We further improve this format by partitioning the matrix into vertical slices to enhance the cache hit rates when accessing the vector to be multiplied. Second, we revisit the segmented scan approach for SpMV to address the load imbalance problem. We propose a highly efficient matrix-based segmented sum/scan for SpMV and further improve it by eliminating global synchronization. Then, we introduce an auto-tuning framework to choose optimization parameters based on the characteristics of input sparse matrices and target hardware platforms. Our experimental results on GTX680 GPUs and GTX480 GPUs show that our proposed framework achieves significant performance improvement over the vendor tuned CUSPARSE V5.0 (up to 229% and 65% on average on GTX680 GPUs, up to 150% and 42% on average on GTX480 GPUs) and some most recently proposed schemes (e.g., up to 195% and 70% on average over clSpMV on GTX680 GPUs, up to 162% and 40% on average over clSpMV on GTX480 GPUs).