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    The One-Decade Task: Putting std::atomic in CUDA. - Olivier Giroux - CppCon 2019

    CppCon

    5 HN mentions · mentioned in 5 months, 2019–2022

    YouTube

    1:05:39 · 11K views · Sep 28, 2019

    Olivier Giroux's CppCon 2019 talk recounts the roughly ten-year effort to bring std::atomic and the C++ memory model to CUDA GPUs, through ten stories from that work.

    Cited as a reference when discussing NVIDIA's C++ memory model, libcu++, and GPU C++ resources.

    Software Engineering & LanguagesC, C++, Rust, Go & systems languagesComputer architecture & CPUs
    1. 1:14:22
      CppCon 2017: Fedor Pikus “C++ atomics, from basic to advanced. What do they really do?”CppCon289K views • 9 years ago
    2. 36:10
      CppCon 2019: Emery Berger “Mesh: Automatically Compacting Your C++ Application's Memory”CppCon15K views • 7 years ago
    3. 1:12:27
      CppCon 2015: Andrei Alexandrescu “std::allocator...”CppCon196K views • 11 years ago
    4. 1:04:33
      CppCon 2014: Jeff Preshing "How Ubisoft Develops Games for Multicore - Before and After C++11"CppCon53K views • 12 years ago
    5. 1:19:52
      CppCon 2016: Jason Turner “Rich Code for Tiny Computers: A Simple Commodore 64 Game in C++17”CppCon264K views • 10 years ago
    6. 1:21:13
      C++ and Beyond 2012: Herb Sutter - atomic Weapons 1 of 2alexsmail71K views • 9 years ago
    7. 1:13:41
      CppCon 2014: Chandler Carruth "Efficiency with Algorithms, Performance with Data Structures"CppCon235K views • 12 years ago
    8. 1:54:00
      Can C++ be 10x Simpler & Safer? - Herb Sutter - CppCon 2022CppCon277K views • 3 years ago
    9. 59:53
      CppCon 2019: Chandler Carruth “There Are No Zero-cost Abstractions”CppCon147K views • 7 years ago
    10. 1:27:46
      CppCon 2014: Mike Acton "Data-Oriented Design and C++"CppCon863K views • 12 years ago
    11. 1:04:00
      CppCon 2015: Sean Parent "Better Code: Data Structures"CppCon106K views • 11 years ago
    12. 1:39:25
      CppCon 2016: Herb Sutter “Leak-Freedom in C++... By Default.”CppCon149K views • 10 years ago
    13. 1:00:07
      CppCon 2017: Carl Cook “When a Microsecond Is an Eternity: High Performance Trading Systems in C++”CppCon358K views • 9 years ago
    14. 55:49
      CppCon 2016: Chandler Carruth “High Performance Code 201: Hybrid Data Structures"CppCon119K views • 10 years ago
    15. 1:29:06
      CppCon 2015: Herb Sutter "Writing Good C++14... By Default"CppCon193K views • 11 years ago
    16. 1:29:54
      CppCon 2015: Chandler Carruth "Tuning C++: Benchmarks, and CPUs, and Compilers! Oh My!"CppCon188K views • 11 years ago
    17. 1:04:26
      CppCon 2018: G. Nishanov “Nano-coroutines to the Rescue! (Using Coroutines TS, of Course)”CppCon40K views • 8 years ago
    18. 1:01:09
      Plenary: Performance Matters - Emery Berger - CppCon 2020CppCon33K views • 6 years ago
    19. 1:40:46
      CppCon 2015: Bjarne Stroustrup “Writing Good C++14”CppCon351K views • 11 years ago
    20. 1:31:14
      CppCon 2017: Titus Winters “C++ as a "Live at Head" Language”CppCon53K views • 9 years ago

    5 comments from Hacker News

    1. @moonchildOct 2022· in “My mesh shaders talk at XDC 2022”

      > I really wish that everybody would finally admit that the "fixed function pipeline" is dead There is a great deal of space between 'fixed function pipeline' (which no one is using) and 'all compute all the time' (which pretty much no one is using for real-time graphics either). > GPUs don't yet have a well-defined memory model Nvidia has a memory model. (I have heard--from an nvidia employee--that amd plays a bit more fast and loose.) this video > GPUs don't have the equivalent of an MMU--so they can't be partitioned or protected. etc. You do not need an MMU to partition or to protect. (Not to say that GPU security isn't a shitshow.) > The problem is that switching to a "compute" model and making that work well would commodify the GPU--and that's the last thing that AMD and NVIDIA want. [LINK] look at where nvidia's revenue is coming from, and tell me again that they're not already there.

      CitesView on HN
    2. @pjmlpDec 2019· in “Taking ML to production with Rust”

      Right now, NVidia is designing their GPGPUs to run C++ code. [LINK] this video With Intel just releasing oneAPI (aka Data Parallel C++) last month, and Khronos pushing SYCL as alternative to CUDA C++. It took 30 years for C++ to reach this stage, and in some domains (like embedded) it is still fighting for relevance. That is something to keep in mind whenever to advocate Rust as replacement for Domain XYZ.

      CitesView on HN
    3. @pjmlpApr 2021· in “Learn C in Y Minutes”

      CppCon 2019: Olivier Giroux “The One-Decade Task: Putting std::atomic in CUDA.” this video Future of ISO and CUDA C++ [LINK] Thrust, the CUDA C++ template library [LINK] Optix, single-ray shader programming model using C++, and ray Tracing acceleration using RT Cores. [LINK] Phew, so many wrappers yet to list.

      CitesView on HN
    4. @pjmlpAug 2021· in “Looking into Zig”

      Sure, "CppCon 2016: “Bringing Clang and C++ to GPUs: An Open-Source, CUDA-Compatible GPU C++ Compiler" [LINK] "CppCon 2019: Olivier Giroux “The One-Decade Task: Putting std::atomic in CUDA.”" this video "Octane render. The Future of GPU Rendering: Real-Time Raytracing" (with CUDA) [LINK] NVIDIA HPC SDK [LINK]

      CitesView on HN
    5. @davvidSep 2020· in “Libcu++: Nvidia C++ Standard Library”

      Here's a somewhat related talk from CppCon '19: "The One-Decade Task: Putting std::atomic in CUDA" this video

      CitesView on HN