SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems

04/15/2021
by   Maciej Besta, et al.
0

Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such as clustering or maximal clique listing. These algorithms are memory-bound and thus could be accelerated by hardware techniques such as Processing-in-Memory (PIM). However, they also come with nonstraightforward parallelism and complicated memory access patterns. In this work, we address this problem with a simple yet surprisingly powerful observation: operations on sets of vertices, such as intersection or union, form a large part of many complex graph mining algorithms, and can offer rich and simple parallelism at multiple levels. This observation drives our cross-layer design, in which we (1) expose set operations using a novel programming paradigm, (2) express and execute these operations efficiently with carefully designed set-centric ISA extensions called SISA, and (3) use PIM to accelerate SISA instructions. The key design idea is to alleviate the bandwidth needs of SISA instructions by mapping set operations to two types of PIM: in-DRAM bulk bitwise computing for bitvectors representing high-degree vertices, and near-memory logic layers for integer arrays representing low-degree vertices. Set-centric SISA-enhanced algorithms are efficient and outperform hand-tuned baselines, offering more than 10x speedup over the established Bron-Kerbosch algorithm for listing maximal cliques. We deliver more than 10 SISA set-centric algorithm formulations, illustrating SISA's wide applicability.

READ FULL TEXT

Authors

page 3

page 5

page 9

03/05/2021

GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

We propose GraphMineSuite (GMS): the first benchmarking suite for graph ...
03/11/2021

MPU: Towards Bandwidth-abundant SIMT Processor via Near-bank Computing

With the growing number of data-intensive workloads, GPU, which is the s...
04/19/2021

Algoritmos de minería de datos en la industria sanitaria

In this paper, we review data mining approaches for health applications....
10/28/2020

Substream-Centric Maximum Matchings on FPGA

Developing high-performance and energy-efficient algorithms for maximum ...
02/18/2022

Uniting Control and Data Parallelism: Towards Scalable Memory-Driven Dynamic Graph Processing

Control parallelism and data parallelism is mostly reasoned and optimize...
03/25/2022

Efficient k-clique Listing with Set Intersection Speedup [Technical Report]

Listing all k-cliques is a fundamental problem in graph mining, with app...
04/02/2022

Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Systems

Several manufacturers have already started to commercialize near-bank Pr...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.