Accelerating Graph Analytics on a Reconfigurable Architecture with a Data-Indirect Prefetcher
Abstract
The irregular nature of memory accesses of graph workloads makes their performance poor on modern computing platforms. On manycore reconfigurable architectures (MRAs), in particular, even state-of-the-art graph prefetchers do not work well (only 3% speedup), since they are designed for traditional CPUs. This is because caches in MRAs are typically not large enough to host a large quantity of prefetched data, and many employs shared caches that such prefetchers simply do not support. This paper studies the design of a data prefetcher for an MRA called Transmuter. The prefetcher is built on top of Prodigy, the current best-performing data prefetcher for CPUs. The key design elements that adapt the prefetcher to the MRA include fused prefetcher status handling registers and a prefetch handshake protocol to support run-time reconfiguration, in addition, a redesign of the cache structure in Transmuter. An evaluation of popular graph workloads shows that synergistic integration of these architectures outperforms a baseline without prefetcher by 1.27x on average and by as much as 2.72x on some workloads.
Cite
@article{arxiv.2301.12312,
title = {Accelerating Graph Analytics on a Reconfigurable Architecture with a Data-Indirect Prefetcher},
author = {Yichen Yang and Jingtao Li and Nishil Talati and Subhankar Pal and Siying Feng and Chaitali Chakrabarti and Trevor Mudge and Ronald Dreslinski},
journal= {arXiv preprint arXiv:2301.12312},
year = {2023}
}