| NVIDIA - CUDA Week in Review |
| Tuesday, 16 August 2011 | ||
NVIDIA - CUDA Week in ReviewCUDA SPOTLIGHT This week's Spotlight is on Denis Bastieri of the University of Padua, Italy, and co-founder of Mimesis HPC. Dr. Bastieri leads the NASA Fermi Large Area Telescope (LAT) team for the National Institute of Nuclear Physics (INFN) in Padua. NVIDIA: Denis, tell us about the Fermi Space Telescope. Denis: The Fermi mission is part of NASA's focus on the theme of "Structure and Evolution of the Universe." I specifically work with one of the two instruments aboard the Fermi spacecraft -- the Large Area Telescope (LAT), which observes gamma rays, the electromagnetic radiation with the highest energy. NVIDIA: Which organizations are involved in the project? Denis: Fermi is a joint project between NASA, the U.S. Department of Energy and academic and research institutions across France, Germany, Japan, Italy and Sweden. The spacecraft was built by General Dynamics. Institutions in the LAT collaboration are listed at https://www-glast.stanford.edu/cgi-bin/collab_inst.
Denis: Parallel computing is the only viable solution when dealing with many different aspects of astrophysics, and GPUs perform parallel computing at a tenth of the cost of conventional systems. Proof of the momentum behind GPUs can be seen in the exponentially growing number of astrophysics papers with "GPU" listed in the abstract! NVIDIA: What are the benefits of working with CUDA? Denis: We started looking at parallel computing on GPUs back during the time of Cg (C for Graphics). The subject looked tantalizing: different textures for positions and velocities and we could model the evolution of a particle population in an external field. The results were quite promising, but Cg was not ideal for astrophysical modeling and we were almost going to dismiss the project entirely. Then, CUDA was released in late 2006. We found programming in CUDA to be quite straightforward for any good C programmer. Our students demonstrated that they could become independent within a semester. And now, CUDA 4.0 is even better! We utilize the Thrust algorithms library. We leverage Unified Virtual Address (UVA) to extend GPU memory capabilities. The CURAND library replaced our own version of a random number generator. CUDA allows us to fully exploit the performance of our 16 GPU cluster. Read the complete interview at: https://developer.nvidia.com/cuda-spotlights CUDA DEVELOPER NEWS Forrester on JP Morgan Chase, NVIDIA: Analyst Rich Fichera of Forrester recently blogged about the use of Tesla GPUs in JP Morgan Chase's Equity Derivatives Group. The bank's hybrid GPU/CPU systems achieved a 40X acceleration in risk calculation times combined with a sizable cost savings: https://bit.ly/qvZ496 New Video: NVIDIA's Cliff Woolley provides an introduction to CUDA in this new five minute video posted on GPUGenius: https://bit.ly/nK5wKO Upcoming CUDA Webinars:
New CUDA Courses:
CUDA in Academia:
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