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GPU Computing: Past, Present and Future (47 mins, ~21 MB)

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Manage episode 205984219 series 2307601
Innhold levert av Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk). Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk) eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
The past five years have seen the use of graphical processing units for computation grow from being the interest of handful of early adopters to a mainstream technology used in the world’s largest supercomputers. The CUDA GPU programming ecosystem today provides all that a developer needs to accelerate scientific applications with GPUs. The architecture of a GPU has much to offer to the future of large-scale computing where energy-efficiency is paramount. NVIDIA is the lead contractor for the DARPA-funded Echelon project investigating efficient parallel computer architectures for the exascale era.
Timothy Lanfear is a Solution Architect in NVIDIA’s Professional Solutions Group, promoting the use of the NVIDIA Tesla(TM) computing solution for high-performance computing. He has twenty years’ experience in HPC, starting as a computational scientist in British Aerospace’s corporate research centre, and then moving to technical pre-sales roles with Hitachi, ClearSpeed, and most recently NVIDIA. He has a degree in Electrical Engineering and a PhD for research in the field of graph theory, both from Imperial College London.
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Manage episode 205984219 series 2307601
Innhold levert av Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk). Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk) eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
The past five years have seen the use of graphical processing units for computation grow from being the interest of handful of early adopters to a mainstream technology used in the world’s largest supercomputers. The CUDA GPU programming ecosystem today provides all that a developer needs to accelerate scientific applications with GPUs. The architecture of a GPU has much to offer to the future of large-scale computing where energy-efficiency is paramount. NVIDIA is the lead contractor for the DARPA-funded Echelon project investigating efficient parallel computer architectures for the exascale era.
Timothy Lanfear is a Solution Architect in NVIDIA’s Professional Solutions Group, promoting the use of the NVIDIA Tesla(TM) computing solution for high-performance computing. He has twenty years’ experience in HPC, starting as a computational scientist in British Aerospace’s corporate research centre, and then moving to technical pre-sales roles with Hitachi, ClearSpeed, and most recently NVIDIA. He has a degree in Electrical Engineering and a PhD for research in the field of graph theory, both from Imperial College London.
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