Presentation
TRIDENT: An End-to-End Streaming Accelerator for TriSpGEMM
DescriptionTriple Sparse General Matrix-Matrix Multiplication (TriSpGEMM) is a major performance bottleneck in scientific computing and graph analytics. Existing domain-specific accelerators support only Sparse General Matrix-Matrix Multiplication (SpGEMM). Performing TriSpGEMM by sequencing two SpGEMM kernels introduces a global synchronization barrier, forces intermediate data to be written to and read from off-chip memory, and leads to frequent pipeline stalls, all of which significantly degrade performance. A natural solution is to design a fused architecture that executes TriSpGEMM directly, but such a design must overcome three key challenges, i.e., pipeline overflow caused by highly irregular and bursty sparse dataflows, locality inefficiency stemming from the trade-off between input reuse and output aggregation, and severe head-of-line blocking due to variable memory access latencies.
In this work, we present TRIDENT, a transient-data-centric accelerator for fused TriSpGEMM processing. TRIDENT integrates a hierarchical flow-control mechanism, a hybrid dataflow co-designed with a windowed sparse format, and an out-of-order decoupled execution scheme to ensure pipeline stability, locality efficiency, and latency tolerance.
Comprehensive evaluations demonstrate that TRIDENT achieves an average of 3.56x performance speedup over state-of-the-art SpGEMM accelerators, and up to 49.6x and 6.3x improvements over CPU and GPU baselines, respectively.
In this work, we present TRIDENT, a transient-data-centric accelerator for fused TriSpGEMM processing. TRIDENT integrates a hierarchical flow-control mechanism, a hybrid dataflow co-designed with a windowed sparse format, and an out-of-order decoupled execution scheme to ensure pipeline stability, locality efficiency, and latency tolerance.
Comprehensive evaluations demonstrate that TRIDENT achieves an average of 3.56x performance speedup over state-of-the-art SpGEMM accelerators, and up to 49.6x and 6.3x improvements over CPU and GPU baselines, respectively.
Event Type
Research Manuscript
TimeMonday, July 273:42pm - 3:54pm PDT
LocationMtg Room 202C
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