Compilation Techniques for Graph Algorithms on GPUs

TL;DR

This paper introduces GG, a GPU extension to the GraphIt compiler, enabling high-performance graph processing on GPUs with novel optimizations and a unified algorithm specification, achieving significant speedups.

Contribution

GG extends GraphIt with GPU support, a new scheduling language, and optimizations like ETWC and EdgeBlocking, improving performance and ease of programming for GPU graph algorithms.

Findings

Up to 5.11x speedup over existing frameworks

Fastest in 66 out of 90 experiments

Effective on multiple algorithms and input graphs

Abstract

The performance of graph programs depends highly on the algorithm, the size and structure of the input graphs, as well as the features of the underlying hardware. No single set of optimizations or one hardware platform works well across all settings. To achieve high performance, the programmer must carefully select which set of optimizations and hardware platforms to use. The GraphIt programming language makes it easy for the programmer to write the algorithm once and optimize it for different inputs using a scheduling language. However, GraphIt currently has no support for generating high performance code for GPUs. Programmers must resort to re-implementing the entire algorithm from scratch in a low-level language with an entirely different set of abstractions and optimizations in order to achieve high performance on GPUs. We propose GG, an extension to the GraphIt compiler framework, that achieves high performance on both CPUs and GPUs using the same algorithm specification. GG significantly expands the optimization space of GPU graph processing frameworks with a novel GPU scheduling language and compiler that enables combining graph optimizations for GPUs. GG also introduces two performance optimizations, Edge-based Thread Warps CTAs load balancing (ETWC) and EdgeBlocking, to expand the optimization space for GPUs. ETWC improves load balancing by dynamically partitioning the edges of each vertex into blocks that are assigned to threads, warps, and CTAs for execution. EdgeBlocking improves the locality of the program by reordering the edges and restricting random memory accesses to fit within the L2 cache. We evaluate GG on 5 algorithms and 9 input graphs on both Pascal and Volta generation NVIDIA GPUs, and show that it achieves up to 5.11x speedup over state-of-the-art GPU graph processing frameworks, and is the fastest on 66 out of the 90 experiments.