.: Keynote 1

HPC & AI
Dr. Eng Lim Goh, HPE

Bio: Dr. Eng Lim Goh joined SGI in 1989, becoming a chief engineer in 1998 and then chief technology officer in 2000. After acquisition, HPE appointed him vice president and SGI chief technology officer. He oversees technical computing programs with the goal to develop the next generation computer architecture for the new many-core era. His current research interest is in the progression from data intensive computing to analytics, machine learning, artificial specific to general intelligence and autonomous systems. He continues his studies in human perception for user interfaces and virtual and augmented reality.

In 2005, InfoWorld named Dr. Goh one of the World’s 25 Most Influential CTOs. He was included twice in the HPCwire list of “People to Watch”; 2005 and 2015. In 2007, he was named “Champions 2.0" of the industry by BioIT World magazine, and received the HPC Community Recognition Award from HPCwire. Dr. Goh is a frequent industry speaker and he continues to discuss, in different forums, innovative technologies and their applications. He co-presented with NASA at the inaugural 1st plenary of the Supercomputing 2014 Conference to an audience of 2,500.

Before joining SGI, Dr. Goh worked for Intergraph Systems, Schlumberger Wireline and Shell Research. A Shell Cambridge University Scholar, Dr. Goh completed his Ph.D. research and dissertation on parallel architectures and computer graphics, and holds a first-class honors degree in mechanical engineering from Birmingham University in the U.K. Dr. Goh has been granted six U.S. patents.

.: Keynote 2

The Latest Advances in GPU Architectures and New Programming Model Features
Nikolay Sakharnykh, Nvidia

Abstract: GPU architectures are approaching a terabyte per second memory bandwidth that, coupled with high-throughput computational cores, creates an ideal device for data-intensive tasks. We'll discuss GPU accelerator fundamentals as well as the best practices when developing your applications for modern GPU architectures. Both the architecture and the programming model evolved over the past few years to help developers achieve high performance quicker and with less effort. New features, such as Unified Memory, have been introduced to simplify development on heterogeneous architectures and provide seamless processing of large out-of-core data workloads. New libraries, such as nvGraph, make it possible to build interactive and high throughput graph and data analytics applications. An overview of existing tools and libraries will be covered to help you get started with the GPU programming.

Bio: Nikolay Sakharnykh is a senior developer technology engineer at NVIDIA, where he works on accelerating HPC and data analytics applications on GPUs. He joined NVIDIA in 2008 as a graphics engineer working on making video games run faster and enabling new visual effects. At the same time, CUDA started to pick up, and he got excited about the general compute capabilities of the GPUs. After a few years his professional interests shifted towards more serious applications in HPC. Now he's exploring GPU applications for graph and data analytics and new memory management techniques.