Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning

• URL: http://arxiv.org/abs/2504.20854v1
• Date: Tue, 29 Apr 2025 15:23:55 GMT
• Title: Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning
• Authors: Jinsun Yoo, ChonLam Lao, Lianjie Cao, Bob Lantz, Minlan Yu, Tushar Krishna, Puneet Sharma,
• Abstract summary: This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance.<n> Genie uses CPU-initiated traffic over a hardware testbed to emulate GPU to GPU communication, and adapts the ASTRA-sim simulator to model interaction between the network and the ML workload.
• Score: 7.872558491633935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance, without requiring expensive GPUs. Genie uses CPU-initiated traffic over a hardware testbed to emulate GPU to GPU communication, and adapts the ASTRA-sim simulator to model interaction between the network and the ML workload.

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