MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Jieyang Chen, Chenhao Xie, Jesun S Firoz, Jiajia Li, Shuaiwen Leon Song, Kevin Barker, Mark Raugas, Ang Li
Oak Ridge National Laboratory
arXiv:2209.07552 [cs.DC], (15 Sep 2022)




   author={Chen, Jieyang and Xie, Chenhao and Firoz, Jesun S and Li, Jiajia and Song, Shuaiwen Leon and Barker, Kevin and Raugas, Mark and Li, Ang},

   keywords={Distributed, Parallel, and Cluster Computing (cs.DC), FOS: Computer and information sciences, FOS: Computer and information sciences},

   title={MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems},



   copyright={Creative Commons Attribution 4.0 International}


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Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra kernels on one GPU will no longer be viable in these applications, simply because the rapidly growing data volume may exceed the memory capacity and computing power of a single GPU. Multi-GPU systems nowadays being ubiquitous in supercomputers and data-centers present great potentials in scaling up large sparse linear algebra kernels. In this work, we design a novel sparse matrix representation framework for multi-GPU systems called MSREP, to scale sparse linear algebra operations based on our augmented sparse matrix formats in a balanced pattern. Different from dense operations, sparsity significantly intensifies the difficulty of distributing the computation workload among multiple GPUs in a balanced manner. We enhance three mainstream sparse data formats — CSR, CSC, and COO, to enable fine-grained data distribution. We take sparse matrix-vector multiplication (SpMV) as an example to demonstrate the efficiency of our MSREP framework. In addition, MSREP can be easily extended to support other sparse linear algebra kernels based on the three fundamental formats (i.e., CSR, CSC and COO).
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