Read All Mark Harris Articles

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2019

• [ ] CUDA Pro Tip: The Fast Way to Query Device Properties

  2018

• [ ] RAPIDS Accelerates Data Science End-to-End

  2017

• [ ] Cooperative Groups: Flexible CUDA Thread Programming
• [ ] Unified Memory for CUDA Beginners
• [ ] CUDA 9 Features Revealed: Volta, Cooperative Groups and More
• [ ] Inside Volta: The World’s Most Advanced Data Center GPU
• [ ] NVIDIA DGX-1: The Fastest Deep Learning System
• [x] An Even Easier Introduction to CUDA

  2016

• [ ] Mixed-Precision Programming with CUDA 8
• [ ] New Pascal GPUs Accelerate Inference in the Data Center
• [ ] Train Your Reinforcement Learning Agents at the OpenAI Gym
• [ ] Inside Pascal: NVIDIA';s Newest Computing Platform
• [ ] CUDA 8 Features Revealed

  2015

• [ ] Accelerating Hyperscale Data Center Applications with Tesla GPUs
• [ ] Performance Portability from GPUs to CPUs with OpenACC
• [ ] https://devblogs.nvidia.com/simple-portable-parallel-c-hemi-2/
• [ ] https://devblogs.nvidia.com/new-features-cuda-7-5/
• [ ] https://devblogs.nvidia.com/fast-great-circle-distance-calculation-cuda-c/
• [ ] https://devblogs.nvidia.com/cplusplus-11-in-cuda-variadic-templates/
• [ ] https://devblogs.nvidia.com/lerp-faster-cuda/
• [ ] https://devblogs.nvidia.com/power-cpp11-cuda-7/
• [ ] https://devblogs.nvidia.com/gpu-pro-tip-cuda-7-streams-simplify-concurrency/
• [ ] https://devblogs.nvidia.com/cuda-7-release-candidate-feature-overview/

  2014

• [ ] https://devblogs.nvidia.com/porting-gpu-accelerated-applications-power8-systems/
• [ ] https://devblogs.nvidia.com/how-nvlink-will-enable-faster-easier-multi-gpu-computing/
• [ ] https://devblogs.nvidia.com/12-things-tesla-accelerated-computing-platform/
• [ ] https://devblogs.nvidia.com/maxwell-most-advanced-cuda-gpu-ever-made/
• [ ] https://devblogs.nvidia.com/10-ways-cuda-6-5-improves-performance-productivity/
• [ ] https://devblogs.nvidia.com/unified-memory-cuda-fortran-programmers/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-occupancy-api-simplifies-launch-configuration/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-fast-robust-computation-givens-rotations/
• [ ] https://devblogs.nvidia.com/powerful-new-features-cuda-6/
• [ ] https://devblogs.nvidia.com/jetson-tk1-mobile-embedded-supercomputer-cuda-everywhere/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-increase-application-performance-nvidia-gpu-boost/
• [ ] https://devblogs.nvidia.com/5-things-you-should-know-about-new-maxwell-gpu-architecture/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-kepler-shuffle/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/
• [ ] https://devblogs.nvidia.com/register-gtc-2014-now-save/

  2013

• [ ] https://devblogs.nvidia.com/unified-memory-in-cuda-6/
• [ ] https://devblogs.nvidia.com/new-parallel-forall/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-nvprof-your-handy-universal-gpu-profiler/
• [ ] https://devblogs.nvidia.com/numba-python-cuda-acceleration/
• [ ] https://devblogs.nvidia.com/prototyping-algorithms-and-testing-cuda-kernels-matlab/
• [ ] https://devblogs.nvidia.com/cuda-arm-platforms-now-available/
• [ ] https://devblogs.nvidia.com/develop-your-notebook-geforce-deploy-tesla/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-understand-fat-binaries-jit-caching/
• [ ] https://devblogs.nvidia.com/pro-tip-clean-up-after-yourself-ensure-correct-profiling/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-write-flexible-kernels-grid-stride-loops/
• [ ] https://devblogs.nvidia.com/finite-difference-methods-cuda-c-part-2/
• [ ] https://devblogs.nvidia.com/finite-difference-methods-cuda-cc-part-1/
• [ ] https://devblogs.nvidia.com/developing-portable-cuda-cc-code-hemi/
• [ ] https://devblogs.nvidia.com/efficient-matrix-transpose-cuda-cc/
• [ ] https://devblogs.nvidia.com/using-shared-memory-cuda-cc/
• [ ] https://devblogs.nvidia.com/join-me-and-other-nvidia-experts-gpu-technology-conference/
• [ ] https://devblogs.nvidia.com/cuda-pro-tip-flush-denormals-confidence/
• [ ] https://devblogs.nvidia.com/how-access-global-memory-efficiently-cuda-c-kernels/

  2012

• [ ] https://devblogs.nvidia.com/how-overlap-data-transfers-cuda-cc/
• [ ] https://devblogs.nvidia.com/how-optimize-data-transfers-cuda-cc/
• [ ] https://devblogs.nvidia.com/how-query-device-properties-and-handle-errors-cuda-cc/
• [ ] https://devblogs.nvidia.com/how-implement-performance-metrics-cuda-cc/
• [ ] https://devblogs.nvidia.com/do-more-code-less-arrayfire-gpu-matrix-library/
• [x] An Easy Introduction to CUDA C and C++
• [ ] https://devblogs.nvidia.com/welcome-back-parallel-forall/
• [ ] https://devblogs.nvidia.com/six-ways-saxpy/
• [ ] https://devblogs.nvidia.com/my-favorites-gtc-2012/
• [x] Expressive Algorithmic Programming with Thrust
• [ ] https://devblogs.nvidia.com/trenches-gtc-faster-finite-elements-wave-propagation/
• [ ] https://devblogs.nvidia.com/trenches-gtc-inside-kepler/
• [ ] https://devblogs.nvidia.com/trenches-gtc-swift-gpu-based-smith-waterman-sequence-alignment-program/
• [ ] https://devblogs.nvidia.com/trenches-gtc-cuda-5-and-beyond/
• [ ] https://devblogs.nvidia.com/trenches-gtc-programming-gpus-openacc/
• [ ] https://devblogs.nvidia.com/trenches-gtc-languages-apis-and-development-tools-gpu-computing/
• [ ] https://devblogs.nvidia.com/coming-next-week-trenches-reports-gtc/
• [ ] https://devblogs.nvidia.com/learn-about-cuda-5-gtc/
• [ ] https://devblogs.nvidia.com/what-are-your-favorite-parallel-programming-references/
• [ ] https://devblogs.nvidia.com/openacc-example-part-2/
• [ ] https://devblogs.nvidia.com/openacc-example-part-1/
• [ ] https://devblogs.nvidia.com/openacc-directives-gpus/
• [ ] https://devblogs.nvidia.com/introducing-parallel-forall/

  2011

• [ ] https://devblogs.nvidia.com/graphcuts-using-npp/
• [ ] https://devblogs.nvidia.com/accelerated-solution-sparse-linear-systems/
• [ ] https://devblogs.nvidia.com/everything-you-ever-wanted-know-about-floating-point-were-afraid-ask/