Paper Info
Title
GraphOps: A Dataflow Library for Graph Analytics Acceleration.
Abstract
Analytics and knowledge extraction on graph data structures have become areas of great interest. For frequently executed algorithms, dedicated hardware accelerators are an energy-efficient avenue to high performance. Unfortunately, they are notoriously labor-intensive to design and verify while meeting stringent time-to-market goals. In this paper, we present GraphOps, a modular hardware library for quickly and easily constructing energy-efficient accelerators for graph analytics algorithms. GraphOps provide a hardware designer with a set of composable graph-specific building blocks, broad enough to target a wide array of graph analytics algorithms. The system is built upon a dataflow execution platform and targets FPGAs, allowing a vendor to use the same hardware to accelerate different types of analytics computation. Low-level hardware implementation details such as flow control, input buffering, rate throttling, and host/interrupt interaction are automatically handled and built into the design of the GraphOps, greatly reducing design time. As an enabling contribution, we also present a novel locality-optimized graph data structure that improves spatial locality and memory efficiency when accessing the graph in main memory. Using the GraphOps system, we construct six different hardware accelerators. Results show that the GraphOps-based accelerators are able to operate close to the bandwidth limit of the hardware platform, the limiting constraint in graph analytics computation.
Year
DOI
Venue
2016
10.1145/2847263.2847337
ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Keywords
Field
DocType
FPGA, Graph analysis, Analytics, Dataflow, Accelerator
Interrupt,Data structure,Computer science,Parallel computing,Field-programmable gate array,Real-time computing,Power graph analysis,Dataflow,Modular design,Analytics,Graph (abstract data type),Embedded system
Conference
Citations 
PageRank 
References 
19
0.66
8
Authors
2
Name
Order
Citations
PageRank
Tayo Oguntebi136013.47
Kunle Olukotun24532373.50