On Performance Analysis of Graphcore IPUs: Analyzing Squared and Skewed Matrix Multiplication
S.-Kazem Shekofteh, Christian Alles, Nils Kochend\"orfer, Holger, Fr\"oning

TL;DR
This paper analyzes the performance of Graphcore IPUs in executing matrix multiplication, especially skewed matrices, comparing them to GPUs and highlighting their advantages in certain computational scenarios.
Contribution
It provides an extensive analysis of IPU performance on matrix multiplication, including efficiency, memory usage, and comparison with GPUs, emphasizing their strengths with skewed matrices.
Findings
IPUs outperform GPUs in skewed matrix multiplication.
IPUs show consistent robustness across various matrix aspect ratios.
Analysis includes execution efficiency and memory considerations.
Abstract
In recent decades, High Performance Computing (HPC) has undergone significant enhancements, particularly in the realm of hardware platforms, aimed at delivering increased processing power while keeping power consumption within reasonable limits. The Intelligence Processing Unit (IPU) represents an entirely novel category of massively parallel processors, meticulously designed to expedite parallel computations through a multitude of processing cores and on-chip memory components interconnected via high-speed fabrics. While IPUs are primarily tailored for machine learning applications and come equipped with several libraries for the seamless implementation of neural networks, they also retain the capability to execute traditional parallel programs like matrix multiplication. However, it is essential to acknowledge that there are certain considerations and limitations when utilizing IPUs…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Advanced Data Storage Technologies · Ferroelectric and Negative Capacitance Devices
