DeepSparse: A Task-Parallel Framework for SparseSolvers on Deep Memory Architectures
| Autor: | Ümit V. Çatalyürek, Afibuzzaman, Fazlay Rabbi, M. Yusuf Özkaya, Hasan Metin Aktulga |
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| Rok vydání: | 2019 |
| Předmět: |
020203 distributed computing
Computer science Task parallelism 010103 numerical & computational mathematics 02 engineering and technology Parallel computing Energy consumption Solver LOBPCG 01 natural sciences Bottleneck Task (computing) Lanczos resampling 0202 electrical engineering electronic engineering information engineering Cache 0101 mathematics Sparse matrix computations |
| Zdroj: | HiPC |
| DOI: | 10.1109/hipc.2019.00052 |
| Popis: | Data movement is an important bottleneck against efficiency and energy consumption in large-scale sparse matrix computations that are commonly used in linear solvers, eigensolvers and graph analytics. We introduce a novel task-parallel sparse solver framework, named DeepSparse, which adopts a fully integrated task-parallel approach. DeepSparse framework differs from existing work in that it adopts a holistic approach that targets all computational steps in a sparse solver rather than narrowing the problem into small kernels (e.g., SpMM, SpMV). We present the implementation details of DeepSparse and demonstrate its merit in two popular eigensolvers, LOBPCG and Lanczos algorithms. We observe that DeepSparse achieves 2× - 16× fewer cache misses across different cache layers (L1, L2 and L3) over implementations of the same solvers based on optimized library function calls. We also achieve 2× - 3.9× improvement in execution time when using DeepSparse over the same library versions. |
| Databáze: | OpenAIRE |
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