Application Profiling at the HPCAC High Performance Center

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In this video from the 2017 HPC Advisory Council Stanford Conference, Pak Lui from Mellanox presents: Application Profiling at the HPCAC High Performance Center.

“To achieve good scalability performance on the HPC scientific applications typically involves good understanding of the workload though performing profile analysis, and comparing behaviors of using different hardware which pinpoint bottlenecks in different areas of the HPC cluster. In this session, a selection of HPC applications will be shown to demonstrate various methods of profiling and analysis to determine the bottleneck, and the effectiveness of the tuning to improve on the application performance from tests conducted at the HPC Advisory Council High Performance Center.”

Pak Lui is the Application Performance Manager for the HPC Advisory Council. He has been involved in demonstrating application performance on various open source and commercial applications. His main responsibilities involve characterizing HPC workloads, analyzing MPI profiles to optimize HPC applications, as well as exploring new technologies, solutions and their effectiveness on real HPC workloads. Pak also works at Mellanox Technologies. His main focus is to optimize HPC applications on products, explore new technologies and solutions and their effect on real workloads. Pak has been working in the HPC industry for over 15 years. Prior to joining Mellanox Technologies, Pak worked as a Cluster Engineer at Penguin Computing. Pak also worked at Sun Microsystems for over 7 years in Sun’s High Performance Computing (HPC) group as a Software Engineer. Pak holds a B.Sc. in Computer Systems Engineering and a M.Sc. in Computer Science from Boston University in the United States.

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