ACM is continuing its popular webcast series with a talk on “Achieve Massively Parallel Acceleration with GPUs” by Nvidia’s Mark Ebersole at 1 pm ET on Thursday, February 27.
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Bill Dally from Nvidia presented this talk at the Stanford HPC Conference. “HPC and data analytics share challenges of power, programmability, and scalability to realize their potential. The end of Dennard scaling has made all computing power limited, so that performance is determined by energy efficiency. With improvements in process technology offering little increase in efficiency innovations in architecture and circuits are required to maintain the expected performance scaling.”
Mark Harris from Nvidia presents this talk from SC13. “The performance and efficiency of CUDA, combined with a thriving ecosystem of programming languages, libraries, tools, training, and services, have helped make GPU computing a leading HPC technology. Learn how powerful new features in CUDA 6 make GPU computing easier than ever, helping you accelerate more of your application with much less code.”
“NumbaPro is a powerful compiler that takes high-level Python code directly to the GPU producing fast-code that is the equivalent of programming in a lower-level language. It contains an implementation of CUDA Python as well as higher-level constructs that make it easy to map array-oriented code to the parallel architecture of the GPU.”
Fans of accelerated computing are reminded to take advantage of the Early Bird deadline for 2014 GPU Tech Conference ends January 29.
“Dirk Pleiter from the Jülich Supercomputing Centre presents this talk from SC13. “In 2012, the NVIDIA Application Lab at Jülich was established to work with application developers on GPU enablement. In this talk we will tour through a variety of applications and evaluate opportunities of new GPU architectures and GPU-accelerated HPC systems, in particular for data-intensive applications.”
In this video from the Nvidia booth at SC13, Michael Wolfe presents on OpenACC. “The OpenACC API provides a high-level, performance portable programming mechanism for parallel programming accelerated nodes. Learn about the latest additions to the OpenACC specification, and see the PGI Accelerator compilers in action targeting the fastest NVIDIA GPUs.”
“The new system will enable researchers to solve ever more complex problems, be it in the search for new materials, in the prediction of climate changes, or in other disciplines. With the planned GPU acceleration, the application performance and the energy efficiency of our simulations will improve significantly. We are very excited about the collaborative development with Cray and NVIDIA of a truly general purpose hybrid multi-core system.”