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Sparse matrix-matrix multiplication (SpMM) is a critical computational kernel in numerous scientific and machine learning applications. SpMM involves massive irregular memory accesses and poses great challenges to conventional cache-based computer architectures. Recently dedicated SpMM accelerators have been proposed to enhance SpMM performance. However, current SpMM accelerators still face challenges in adapting to varied sparse patterns, fully exploiting inherent parallelism, and optimizing cache performance. To address these issues, we introduce ACES, a novel SpMM accelerator in this study. First, ACES features an adaptive execution flow that dynamically adjusts to diverse sparse patterns. The adaptive execution flow balances parallel computing efficiency and data reuse. Second, ACES incorporates locality-concurrency co-optimizations within the global cache. ACES utilizes a concurrency-aware cache management policy, which considers data locality and concurrency for optimal replacement decisions. Additionally, the integration of a non-blocking buffer with the global cache enhances concurrency and reduces computational stalls. Third, the hardware architecture of ACES is designed to integrate all innovations. The architecture ensures efficient support across the adaptive execution flow, advanced cache optimizations, and fine-grained parallel processing. Our performance evaluation demonstrates that ACES significantly outperforms existing solutions, providing a 2.1× speedup and marking a substantial advancement in SpMM acceleration.
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Two-Face: Combining Collective and One-Sided Communication for Efficient Distributed SpMM
Sparse matrix dense matrix multiplication (SpMM) is commonly used in applications ranging from scientific computing to graph neural networks. Typically, when SpMM is executed in a distributed platform, communication costs dominate. Such costs depend on how communication is scheduled. If it is scheduled in a sparsity-unaware manner, such as with collectives, execution is often inefficient due to unnecessary data transfers. On the other hand, if communication is scheduled in a fine-grained sparsity-aware manner, communicating only the necessary data, execution can also be inefficient due to high software overhead. We observe that individual sparse matrices often contain regions that are denser and regions that are sparser. Based on this observation, we develop a model that partitions communication into sparsity-unaware and sparsity-aware components. Leveraging the partition, we develop a new algorithm that performs collective communication for the denser regions, and fine-grained, one-sided communication for the sparser regions. We call the algorithm Two-Face. We show that Two-Face attains an average speedup of 2.11x over prior work when evaluated on a 4096-core supercomputer. Additionally, Two-Face scales well with the machine size.
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- PAR ID:
- 10510623
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400703850
- Page Range / eLocation ID:
- 1200 to 1217
- Format(s):
- Medium: X
- Location:
- La Jolla CA USA
- Sponsoring Org:
- National Science Foundation
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