Best Hardware Practices for Modern HPC Workloads Design

Many new technologies used in High Performance Computing (HPC) have allowed new application areas to become possible. Advances like multi-core, GPU, NVMe, and others have created application verticals that include accelerator assisted HPC, GPU based Deep Learning, fast storage and parallel file systems, and Big Data Analytics systems. Unfortunately, implementing a general purpose balanced system solution is not possible for these applications. To achieve the best price-to-performance in each of these application verticals, attention to hardware features and design is most important.

This white paper explores several best hardware practices for modern HPC workloads that can be broken into three general design types:

  • Accelerated HPC Computation – includes both traditional HPC and Deep Learning systems
  • IO-Heavy HPC Computing – includes systems that provide fast NVMe implementations for local IO or as part of a parallel file system
  • Big Data (Database) Computing – includes system designed for high density bulk storage of large amounts of data

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