Indirection Stream Semantic Register Architecture for Efficient Sparse-Dense Linear Algebra

Autor:	Scheffler, Paul, Zaruba, Florian, Schuiki, Fabian, Hoefler, Torsten, Benini, Luca
Rok vydání:	2020
Předmět:	Computer Science - Hardware Architecture
Druh dokumentu:	Working Paper
Popis:	Sparse-dense linear algebra is crucial in many domains, but challenging to handle efficiently on CPUs, GPUs, and accelerators alike; multiplications with sparse formats like CSR and CSF require indirect memory lookups. In this work, we enhance a memory-streaming RISC-V ISA extension to accelerate sparse-dense products through streaming indirection. We present efficient dot, matrix-vector, and matrix-matrix product kernels using our hardware, enabling single-core FPU utilizations of up to 80% and speedups of up to 7.2x over an optimized baseline without extensions. A matrix-vector implementation on a multi-core cluster is up to 5.8x faster and 2.7x more energy-efficient with our kernels than an optimized baseline. We propose further uses for our indirection hardware, such as scatter-gather operations and codebook decoding, and compare our work to state-of-the-art CPU, GPU, and accelerator approaches, measuring a 2.8x higher peak FP64 utilization in CSR matrix-vector multiplication than a GTX 1080 Ti GPU running a cuSPARSE kernel. Comment: 6 pages, 4 figures. Submitted to DATE 2021. Camera-ready version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.08070 Zobrazit plný text záznamu View this record from Arxiv