References & Citations
Computer Science > Distributed, Parallel, and Cluster Computing
Title: Benchmarking Machine Learning Applications on Heterogeneous Architecture using Reframe
(Submitted on 16 Apr 2024 (v1), last revised 25 Apr 2024 (this version, v2))
Abstract: With the rapid increase in machine learning workloads performed on HPC systems, it is beneficial to regularly perform machine learning specific benchmarks to monitor performance and identify issues. Furthermore, as part of the Edinburgh International Data Facility, EPCC currently hosts a wide range of machine learning accelerators including Nvidia GPUs, the Graphcore Bow Pod64 and Cerebras CS-2, which are managed via Kubernetes and Slurm. We extended the Reframe framework to support the Kubernetes scheduler backend, and utilise Reframe to perform machine learning benchmarks, and we discuss the preliminary results collected and challenges involved in integrating Reframe across multiple platforms and architectures.
Submission history
From: Joseph K. L. Lee [view email][v1] Tue, 16 Apr 2024 13:05:23 GMT (57kb)
[v2] Thu, 25 Apr 2024 16:52:08 GMT (57kb)
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