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Matrix engines or units, in different forms and affinities, are becoming areality in modern processors; CPUs and otherwise. The current and dominantalgorithmic approach to Deep Learning merits the commercial investments inthese units, and deduced from the No.1 benchmark in supercomputing, namely HighPerformance Linpack, one would expect an awakened enthusiasm by the HPCcommunity, too.  Hence, our goal is to identify the practical added benefits for HPC andmachine learning applications by having access to matrix engines. For thispurpose, we perform an in-depth survey of software stacks, proxy applicationsand benchmarks, and historical batch job records. We provide a cost-benefitanalysis of matrix engines, both asymptotically and in conjunction withstate-of-the-art processors. While our empirical data will temper theenthusiasm, we also outline opportunities to misuse these densematrix-multiplication engines if they come for free.

Matrix Engines for High Performance Computing:A Paragon of Performance or Grasping at Straws?