Machine Learning Is Helping High-Performance Computing Go Mainstream

Once confined to specialized workloads with high compute requirements, such as academic and medical research, financial modeling, and energy exploration, high-performance computing, or HPC, has in recent years been finding its way into IT of all stripes. This has partly been brought about by the mainstreaming of machine learning (a subset of artificial intelligence), which generally operates at a snail's pace on conventional servers and needs the added oomph that HPC brings to the table. Like much in life, its boundaries aren't clearly defined and its part of a continuum. Although the term is often used interchangeably with supercomputers, behemoth systems such as Fugaku -- which employs close to 160,000 processors to produce 415.53 petaflops -- HPC systems range from clusters of garden-variety racked x86 servers and storage devices to supercomputers like Fugaku. They're much faster than typical servers, generally employing much more silicon than conventional systems, both as CPUs and GPUs, the latter being used to "accelerate" the system, or offload some of the number crunching from the former.