Deep Graph Library Optimizations for Intel(R) x86 Architecture
Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty
TL;DR
This paper presents performance optimizations for the Deep Graph Library on Intel x86 CPUs, achieving significant speed-ups in graph neural network applications compared to baseline implementations.
Contribution
It introduces CPU-specific optimizations for DGL, improving execution speed for GNN applications on Intel x86 architecture.
Findings
Speed-ups of 1.5x to 13x across 7 applications
Enhanced CPU performance for DGL GNN workloads
Optimizations tailored for Intel x86 architecture
Abstract
The Deep Graph Library (DGL) was designed as a tool to enable structure learning from graphs, by supporting a core abstraction for graphs, including the popular Graph Neural Networks (GNN). DGL contains implementations of all core graph operations for both the CPU and GPU. In this paper, we focus specifically on CPU implementations and present performance analysis, optimizations and results across a set of GNN applications using the latest version of DGL(0.4.3). Across 7 applications, we achieve speed-ups ranging from1 1.5x-13x over the baseline CPU implementations.
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Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Ferroelectric and Negative Capacitance Devices