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CALSCALE:GREGORIAN
X-WR-CALNAME:High Performance Computing with CUDA
METHOD:PUBLISH
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
DTSTART:20070311T020000
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TZNAME:CDT
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DTSTART:20071104T020000
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TZNAME:CST
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BEGIN:VEVENT
SEQUENCE:2
DTSTART;TZID=America/Chicago:20101114T083000
DESCRIPTION:ABSTRACT: CUDA is a general purpose architecture for writing highly parallel applications. It provides several key abstractions--a hierarchy of thread blocks\, shared memory\, and barrier synchronization--for scalable high-performance parallel computing. Scientists throughout industry and academia use CUDA to achieve dramatic speedups on production and research codes. The CUDA architecture supports many languages\, programming environments\, and libraries including C/C++\, Fortran\, OpenCL\, DirectCompute\, Python\, Matlab\, FFT\, LAPACK\, etc. In this tutorial NVIDIA engineers will partner with academic and industrial researchers to present CUDA and discuss its advanced use for science and engineering domains. The morning session will teach the basics of CUDA C programming\, give an overview of the various tools\, and cover the main optimization techniques. The afternoon session will discuss best practices for tuning and profiling CUDA programs and close with real-world case studies from domain scientists using CUDA for computational biophysics\, fluid dynamics\, seismic imaging\, and theoretical physics.
UID:tut126@sc10.supercomputing.org
SUMMARY:High Performance Computing with CUDA
DTEND;TZID=America/Chicago:20101114T170000
LOCATION:391-392
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