Enterprise AI Analysis: Evolving HPC services to enable ML workloads on HPE Cray EX
Evolving HPC for Next-Gen ML Workloads
This paper investigates challenges and proposes technological enhancements for HPC services to better support ML workloads, focusing on containerized environments, performance profiling, observability, node vetting, service plane infrastructure, and tailored storage solutions for the Alps Research Infrastructure.
Driving Next-Gen AI Innovation on HPC
The Alps Research Infrastructure, with its 10,752 NVIDIA GH200 GPUs, is crucial for large-scale ML. However, traditional HPC services often fall short of ML community needs. We propose a suite of modular enhancements including container support, GPU saturation scoring, infrastructure observability, node vetting, a flexible service plane, and tailored storage to improve usability, performance, and reproducibility for ML workloads.
Deep Analysis & Enterprise Applications
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Calculate Your Potential HPC-ML ROI
Estimate the efficiency gains and cost savings for your enterprise by optimizing ML workloads on advanced HPC infrastructure.
HPC-ML Integration Roadmap
A strategic phased approach to evolve your HPC infrastructure for optimal ML workload support.
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