This feature continues our series of articles that survey the landscape of HPC and AI. This final post explores AI hardware options to support the growing artificial intelligence software ecosystem. Balance ratios are key to understanding the plethora of AI hardware solutions that are being developed or are soon to become available. Future proofing procurements to support run-anywhere solutions--rather than hardware specific solutions--is key! The basic idea behind balance ratios is to keep what works and improve on those hardware characteristics when possible.

Intel Scalable Systems Framework (Intel SSF) reduces confusion given the wealth of new technologies now available to HPC customers, and offers guidance for the right mix of balanced and validated hardware and software technologies. Intel SSF incorporates a host of software and hardware technologies including Intel Omni-Path Architecture (Intel OPA), Intel Optane SSDs built on 3D XPoint technology, and new Intel Silicon Photonics – plus it incorporates Intel's compute and storage products, including Intel Xeon processors, Intel Xeon Phi processors, and Intel Enterprise Edition for Lustre* software. Benchmarks show that a combination of Intel SSF technologies (Intel Xeon Phi and Intel OPA) provide significantly better scaling and performance when training deep learning neural networks than GPU-based products on well-known benchmarks such as AlexNet and GoogleNet [1]. These and other deep-learning benchmarks can be viewed on the Intel machine learning portal. Intel Xeon Phi processors deliver superior neural networking training performance using up to seventy two (72) processing cores per processor where each core contains two Intel AVX-512 vector processing units.