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Strohmaier, Erich

Publications and source records attributed to Strohmaier, Erich.

The TOP500 List and Progress in High-Performance Computing

The TOP500 list has enjoyed incredible success as a metric for supercomputing performance and as a source of data for identifying technological trends, for more than two decades. Here, the project's editors reflect on its usefulness and limitations for guiding large-scale scientific computing into the exascale era.

97 MATHEMATICS AND COMPUTING↗

HPGMG 1.0: A Benchmark for Ranking High Performance Computing Systems

This document provides an overview of the benchmark - HPGMG - for ranking large scale general purpose computers for use on the Top500 list. We provide a rationale for the need for a replacement for the current metric HPL, some background of the Top500 list and the challenges of developing such a metric; we discuss our design philosophy and methodology, and an overview of the specification of the benchmark. The primary documentation with maintained details on the specification can be found at hpgmg.org and the Wiki and benchmark code itself can be found in the repository https://bitbucket.org/hpgmg/hpgmg.

97 MATHEMATICS AND COMPUTING↗

TORCH Computational Reference Kernels - A Testbed for Computer Science Research

For decades, computer scientists have sought guidance on how to evolve architectures, languages, and programming models in order to improve application performance, efficiency, and productivity. Unfortunately, without overarching advice about future directions in these areas, individual guidance is inferred from the existing software/hardware ecosystem, and each discipline often conducts their research independently assuming all other technologies remain fixed. In today's rapidly evolving world of on-chip parallelism, isolated and iterative improvements to performance may miss superior solutions in the same way gradient descent optimization techniques may get stuck in local minima. To combat this, we present TORCH: A Testbed for Optimization ResearCH. These computational reference kernels define the core problems of interest in scientific computing without mandating a specific language, algorithm, programming model, or implementation. To compliment the kernel (problem) definitions, we provide a set of algorithmically-expressed verification tests that can be used to verify a hardware/software co-designed solution produces an acceptable answer. Finally, to provide some illumination as to how researchers have implemented solutions to these problems in the past, we provide a set of reference implementations in C and MATLAB.

97 MATHEMATICS AND COMPUTING↗