Presentation
Sting: Near-storage accelerator framework for scalable triangle counting and beyond
DescriptionOne of the most critical limitations to scalable graph mining is memory capacity, as graphs of interest continue to grow while the rate of DRAM scaling diminishes. While high-performance NVMe storage is cheap and dense enough to better support larger graphs, the relative performance limitations of secondary storage force a cost-performance trade-off. We present STING, which uses an asynchronous callback function to provide a general interface to in-storage graphs while allowing transparent near-storage acceleration. Using triangle counting, we show with transparent filtering and sorting acceleration, STING can achieve improve state-of-the-art by 3x for cost and power efficiency.
Event Type
Research Manuscript
TimeThursday, June 272:00pm - 2:15pm PDT
Location3003, 3rd Floor
Design
In-memory and Near-memory Computing Circuits