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GPU Programming for the Data Sciences
March 24, 2014 @ 6:30 pm
Mark Ebersole, NVIDIA
Agenda
6:30 Food & Networking
7:00 Presentation
Event Details
The past decade has seen a shift from serial to parallel computing. No longer the exotic domain of supercomputing, parallel hardware is ubiquitous and software must follow: a serial, sequential program will use less than 1% of a modern PC’s computational horsepower and less than 4% of a high-end smartphone. GPUs have proven themselves as world-class, massively parallel accelerators, from supercomputers to gaming consoles to smartphones, and CUDA is the platform best designed to access this power.
In this talk, we’ll cover the many different ways of accelerating your code on GPUs; from GPU-accelerated libraries, to directive-based programming using OpenACC directives, and finally to writing CUDA directly in languages such as C/C++, Fortran, or Python. In addition to covering the current state of massively parallel programming with GPUs, we will briefly touch on future challenges and potential research projects in the areas of Big Data, Machine Learning, and others. Finally, you will be provided with a number of resources to try CUDA yourself and where to go to learn more.
Speaker Bio
As CUDA Educator at NVIDIA, Mark Ebersole teaches developers the benefit of GPU computing using the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked at IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.
GPU Technology Conference
https://registration.gputechconf.com/form/session-listing
Event page provided by ACM
https://www.sfbayacm.org/event/gpu-programming-data-sciences