The new CoMet application consists of implementations of the 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient using native or adapted GEMM kernels optimized for GPU architectures, and received the 2018 Gordon Bell Prize. Nearly 300 quadrillion element comparisons per second and over 2.3 mixed precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. These similarity metrics form the major parts of largescale Genome-Wide Epistasis Studies (GWES) and pleiotropy studies. These efforts seek to identify genetic variants that contribute to individual phenotypes, including susceptibility (or robustness) to disease. We are using CoMet to investigate the
genetic architectures underlying complex traits in applications from bioenergy to human clinical genomics.

Dan Jacobson’s career as a computational systems biologist has included leadership roles in academic, corporate, NGO and national lab settings. His lab focuses on the development and subsequent application of computational methods to biological datasets. These methods are applied to various population-scale multiomics data sets in an attempt to better understand the functional relationships at play in biological organisms and communities. His group at ORNL studies many systems - from viruses to microbes to plants to Drosophila, mice and humans. His lab is actively involved in the development of new exascale applications for biology and he is a recent recipient of the Gordon Bell Prize.

Ensuring performance of applications running on large-scale clusters is one of the primary focuses in HPC research. In this talk, we will show our strategies for performance analysis and optimization of applications in various fields of research using large-scale HPC clusters. Our strategies are designed to comprehensively analyze runtime features of applications, parallelization strategies of physical models, algorithmic implementations, and other technical details. These three levels of strategy cover platform optimization, technological innovation, and model innovation, and targeted optimization based on these features. State-of-the-art CPU instructions, network communication patterns, and innovative parallel strategies have been optimized for various applications.

Dr. Liu is the Head of the HPC Application Support Team at Inspur. Since joining Inspur, he and his team have engaged primarily in the optimization and acceleration of large-scale scientific computing applications in the fields of meteorology, oceanography, climatology, physics, life sciences and chemistry. At Inspur, he has designed and developed the in-house software package “Teye” for monitoring and analyzing characteristics of HPC applications. In addition, he has refined and deepened the Inspur HPC application characterstics analysis method and also extracted a methodology for profiling computational science applications from perspectives of theory and algorithm. He and his team have been involved in the design and optimization of the core codes and algorithms for a number of research projects in to multi-disciplinary computational science. Dr. Liu received his PhD in Condensed Matter Physics at the Chinese Academy of Sciences in 2011.

New hardware technology such as rack-scale computers (RSCs) are redefining the landscape of data center computing today, achieving both higher bandwidth and lower latency. As a basic building block of a redesigned stack of hardware, OS, storage and network, such infrastructure is increasingly suitable for HPC workloads. However, most of today’s data center technology was designed or conceived in the era before the cloud existed. Many of the technologies represent compromises, encumbered by legacy thinking. At Microsoft Research Cambridge, we are exploring optical technologies across the three primary resources, networking, storage, and compute.
In this talk, we will outline the future challenges in the data center and discuss the limitations of current technology. We will take a holistic end-to-end view of the needs of the cloud and present our work on optical networking and storage.

Dr. Scarlet Schwiderski-Grosche is a Director at Microsoft Research Cambridge. She drives strategic research partnerships with academic institutions in EMEA, including the Swiss Joint Research Center with EPFL and ETH Zurich and the Inria Joint Center. Moreover, Scarlet leads academic outreach around optical storage and networking for Microsoft Optics for the Cloud. Scarlet has ten years’ experience driving collaborations with academia and industry around cutting-edge and highly interdisciplinary research projects and has managed a multitude of academic programs including the EMEA PhD Scholarship Programme and the EMEA PhD Summer Schools.