Computational science is the field of study concerned with constructing mathematical models and numerical techniques that represent scientific, social scientific or engineering problems and employing these models on computers, or clusters of computers to analyze, explore or solve these models. Numerical simulation enables the study of complex phenomena that would be too expensive or dangerous to study by direct experimentation. The quest for ever higher levels of detail and realism in such simulations requires enormous computational capacity, and has provided the impetus for breakthroughs in computer algorithms and architectures.
Among the high-performance computing systems, HPC clusters have become the preferred solution as they provide an efficient performance compute solution based on industry-standard hardware connected by a high-speed network. The main benefits of clusters are affordability, flexibility and availability. While the HPC cluster architecture is being used by the majority of HPC systems today (for example, more than 80% of the TOP500 supercomputer list is listed as clusters), very large-scale systems tend to use proprietary solutions. Historically, proprietary solutions were necessary to provide low-latency, high bandwidth and high reliability to enable scaling to tens and hundreds-of-thousands of CPUs. Today, commodity-based clusters provide a low-latency and high bandwidth solution, in addition to advanced scalability and reliability capabilities, delivering the solution requirements for the world’s largest supercomputers.
The HPC|Scale working group mission is to explore the capabilities of upcoming advanced clustering technology to be utilized by HPC commodity clusters to replace expensive, non-flexible proprietary systems and provide a better solution for future, large-scale HPC systems.