HPC Workload Convergence Paves the Way for AI in the Exascale Era

Today, we are witnessing a significant shift in the ways high performance computing (HPC) workloads and artificial intelligence (AI) converge, creating a powerful synergy between the two. The convergence is happening now in industries such as climate research and health and life sciences. Examples include:

With so many use cases, organizations today need the flexibility to choose an architecture that meets their unique needs.

While the benefits of convergence are clear, it will take time to overcome challenges. The HPC community has traditionally focused on compute-centric workflows where distributed memory algorithms, double-precision floating-point math, and large-scale data storage take precedence. In contrast, the AI community builds upon the analysis and interpretation of a multi-sourced data deluge. They focus on data-centric workflows where reduced precision math, and a range of applications from image classification, translation, and recommendation engines to autonomous driving take center stage.

These differences create the potential for the expansion of two ecosystems rather than one. Instead, we need to bring them together to tackle new and more complex challenges. It’s not just HPC that benefits from this convergence, though. As AI models continue to grow in their size and need for compute power, training workloads will need rely more heavily on the technologies underlying HPC like MPI and fabric. The convergence will tie the two ecosystems together, innately, over time providing mutual benefit.

Intel recognizes that a critical element of accelerating this convergence is building an infrastructure comprised of technologies focused on delivering high performance across a broad range of workloads, including AI. The Intel® Xeon® Scalable processor does just that. Innovations such as Intel® AVX-512 and Intel® DL Boost technology offer instructions to make optimal use of hardware. Currently, over 20 million developers worldwide code for the Intel Architecture.

Intel also recognizes that given the evolving and nascent nature of AI algorithms, and demand for varying power and performance curves, there is a need for other compute architectures such as FPGA, GPU and ASIC, further driving Intel’s investments in these areas.