Accelerated Computing

1. Introduction

An Even Easier Introduction to CUDA (20170125)
https://devblogs.nvidia.com/even-easier-introduction-cuda/



CUDA從入門到精通
https://blog.csdn.net/Augusdi/article/details/12833235

GPUS Ladyhttps://cloud.tencent.com/developer/user/1539448

2. Atomic Operations (warp vote)

CUDA Pro Tip: Optimized Filtering with Warp-Aggregated Atomics (20141001)
https://devblogs.nvidia.com/cuda-pro-tip-optimized-filtering-warp-aggregated-atomics/

GPU Pro Tip: Fast Histograms Using Shared Atomics on Maxwell (20150317)

https://devblogs.nvidia.com/gpu-pro-tip-fast-histograms-using-shared-atomics-maxwell/

Accelerating Dissipative Particle Dynamics Simulation on Tesla GPUs (20150416)

https://devblogs.nvidia.com/accelerating-dissipative-particle-dynamics-simulation-tesla-gpus/

Voting and Shuffling to Optimize Atomic Operations (20150806)

https://devblogs.nvidia.com/voting-and-shuffling-optimize-atomic-operations/

Using CUDA Warp-Level Primitives (20180115)

https://devblogs.nvidia.com/using-cuda-warp-level-primitives/
http://www.xipengli.com/20180125_CUDA_Warp_Level_Primitives.html

instruction

https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#warp-vote-functions

DPD slides
http://on-demand.gputechconf.com/gtc/2014/presentations/S4518-dissipative-particle-dymanics-sims-kepler.pdf


warp shuffles, and reduction / scan operations

https://drive.google.com/file/d/1mgv38BhdCt0-sGD0I_i0RMCHGsbEa-zj/view?usp=sharing

閱讀Warp Vote Functions

https://cloud.tencent.com/developer/article/1173225

閱讀Warp Shuffle Functions

https://cloud.tencent.com/developer/article/1173233

CUDA之Warp Shuffle詳解

https://blog.csdn.net/Bruce_0712/article/details/64926471

cuda的Shuffle技術以及自定義雙精度版本

https://blog.csdn.net/lingerlanlan/article/details/25401565

__shfl_down and __shfl_down_sync give different results

https://stackoverflow.com/questions/50639194/shfl-down-and-shfl-down-sync-give-different-results

3. Unified Memory

Beyond GPU Memory Limits with Unified Memory on Pascal (20161214)

https://devblogs.nvidia.com/beyond-gpu-memory-limits-unified-memory-pascal/
Unified Memory for CUDA Beginners (20170619)

https://devblogs.nvidia.com/unified-memory-cuda-beginners/

Maximizing Unified Memory Performance in CUDA (20171119)
https://devblogs.nvidia.com/maximizing-unified-memory-performance-cuda/

4. C++11

The Power of C++11 in CUDA 7 (20150318)https://devblogs.nvidia.com/power-cpp11-cuda-7/

C++11 in CUDA: Variadic Templates (20150326)

https://devblogs.nvidia.com/cplusplus-11-in-cuda-variadic-templates/

CUDA 7 Release Candidate Feature Overview: C++11, New Libraries, and More (20150113)

https://devblogs.nvidia.com/cuda-7-release-candidate-feature-overview/

5. COOPERATIVE GROUPS

Cooperative Groups: Flexible CUDA Thread Programming (20171004)

https://devblogs.nvidia.com/cooperative-groups/



CUDA 9 AND BEYOND
https://drive.google.com/file/d/1YipovGErr3mfCBG3dqlaAgfUcFt__BCd/view?usp=sharing
Cooperative Groups
https://drive.google.com/file/d/13eN5flsds307eIIAtwBNAq8BScscoyOe/view?usp=sharing

6. Stream
https://devblogs.nvidia.com/how-overlap-data-transfers-cuda-cc/

7. Other

CUDA Spotlight: Michela Taufer on GPU-Accelerated Scientific Computing (20140821)https://devblogs.nvidia.com/cuda-spotlight-michela-taufer-gpu-accelerated-scientific-computing/

Register Cache: Caching for Warp-Centric CUDA Programs (20171012)

https://devblogs.nvidia.com/register-cache-warp-cuda/

未來會增加
Streams, zero-copy memory, texture objects, PTX (parallel thread execution) assembly, warp-level vote/shuffle