As accelerated computing continues to drive application performance in all areas of AI and scientific computing, there��s a renewed interest in GPU optimization techniques to ensure applications obtain the best possible performance. As an application developer, there are many ways to program GPUs, up and down the software stack. In this post, we introduce some of the different levels of the stack��
]]>NVIDIA leverages data science and machine learning to optimize chip manufacturing and operations workflows��from wafer fabrication and circuit probing to packaged chip testing. These stages generate terabytes of data, and turning that data into actionable insights at speed and scale is critical to ensuring quality, throughput, and cost efficiency. Over the years, we��ve developed robust ML pipelines��
]]>Imagine analyzing millions of NYC ride-share journeys��tracking patterns across boroughs, comparing service pricing, or identifying profitable pickup locations. The publicly available New York City Taxi and Limousine Commission (TLC) Trip Record Data contains valuable information that could reveal game-changing insights, but traditional processing approaches leave analysts waiting hours for results��
]]>Each year, the world recycles only around 13% of its two billion-plus tons of municipal waste. By 2050, the world��s annual municipal waste will reach 3.88B tons. But the global recycling industry is far from efficient. Annually, as much as $120B of potentially recoverable plastic��let alone paper or metals��ends up in landfills rather than within new products made with recycled materials.
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