A scientific approach to workload-aware computing on AWS
HPC Blog
This comprehensive blog post presents a scientific approach to selecting high-performance computing (HPC) instances on AWS by analyzing workload characteristics and coupling patterns.
- Categorizes HPC workloads into four coupling types: tightly-coupled, loosely-coupled, mixed-coupling, and variable coupling
- Develops a multi-dimensional performance scoring framework using mathematical weights derived from scientific research
- Provides performance weight derivation for CPU (1.5x), memory (0.5x), network (0.3x-1.2x), GPU (2.0x per GB), and storage (10x per TB)
- Introduces a systematic methodology for instance selection that considers workload-specific characteristics
- Addresses current limitations in the approach and suggests future research directions
The methodology aims to help organizations make informed HPC instance selection decisions without extensive custom benchmarking, leveraging existing performance research and empirical validation.
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