Developer Spotlight: Earth Science Monitoring with Satellite Imagery

Research, Climate/Weather/Ocean Modeling, Cluster / Supercomputing, Developer Spotlight, Government / National Labs, Image Recognition, Machine Learning & Artificial Intelligence

Nadeem Mohammad, posted Mar 01 2017

Sangram Ganguly, a senior research scientist at the NASA Ames Research Center shares how they are analyzing satellite imagery with deep learning to gain a better understanding of our planet. As a founding member of NASA Earth Exchange (NEX), which utilizes NASA’s GPU-accelerated Pleiades supercomputer, Ganguly helped develop the collaboration platform that combines state-of-the-art supercomputing,

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GameWorks, GDC17

David Coombes, posted Feb 28 2017

The Game Developer Conference will return to San Francisco on Feb 27th 2017 for 5 days of tutorials, presentations and the expo. This will be NVIDIA’s biggest year yet. Our booth with be packed with the hottest tech and we have 16 sponsored sessions. New for this year we have talks about deep learning, the AI technique that is revolutionizing computer science. Read on to find out more.

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GameWorks DX12 Released At GDC


David Coombes, posted Feb 28 2017

Today at GDC NVIDIA released of the latest version of GameWorks. We are accelerating the pace of innovation in game development through advanced technologies for rendering, VR, ray tracing and simulation. GameWorks DX12 adds a range of new APIs, combined with powerful tools and support for DirectX® 12 and DirectCompute. This is our best release yet.

We released tools and technologies in the following catories.

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Aftermath: Debugging Crashes and TDRs on the GPU

GameWorks Expert Developer, GameWorks

Alex Dunn, posted Feb 28 2017

“Device Removed” – the bane of every PC rendering programmers existence. “The GPU has crashed and who knows why?” If you’ve said this (or similar; accounting for variants including profanity) then this blog post is for you!

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Pro Tip: cuBLAS Strided Batched Matrix Multiply

Research, Algorithms & Numerical Techniques, CUDA, Education & Training, Machine Learning & Artificial Intelligence

Nadeem Mohammad, posted Feb 28 2017

There’s a new computational workhorse in town. For decades, general matrix-matrix multiply—known as GEMM in Basic Linear Algebra Subroutines (BLAS) libraries—has been a standard benchmark for computational performance. GEMM is possibly the most optimized and widely used routine in scientific computing. Expert implementations are available for every architecture and quickly achieve the peak performance of

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