Changkun Ou. 2023. Go on GPU. GopherChina 2023. Session "Foundational Toolchains" Go on GPU Changkun Ou changkun.de/s/gogpu GopherChina 2023 Session “Foundational Toolchains” 2023 June 10 1 Changkun Ou. 2023. Go on GPU. GopherChina 2023. Session "Foundational Toolchains" Agenda ● Basic knowledge for interacting with GPUs ● Accelerate Go programs using GPUs ● Challenges in Go when using outlooks 2 Changkun Ou. 2023. Go on GPU. GopherChina 2023. Session "Foundational Toolchains" Agenda ● Basic knowledge for interacting with GPUs ○ Motivation ○ GPU Driver and Standards ○ Render and

1/66Bridging the Gap: Writing Portable Programs for CPU and GPU using CUDA Thomas Mejstrik Sebastian Woblistin 2/66Content 1 Motivation Audience etc.. Cuda crash course Quiz time 2 Patterns Oldschool Motivation Patterns The dark path Cuda proposal Thank you Why write programs for CPU and GPU Difference CPU/GPU Algorithms are designed differently Latency/Throughput Memory bandwidth Number of cores Motivation Patterns The dark path Cuda proposal Thank you Why write programs for CPU and GPU Difference CPU/GPU Why it makes sense? Library/Framework developers Embarrassingly parallel algorithms User

Deep Learning SDK accelerates widely-used deep learning frameworks such as PyTorch. PyTorch is a GPU-accelerated tensor computational framework with a Python front end. Functionality can be easily extended standard defined neural network layers, deep learning optimizers, data loading utilities, and multi-gpu, and multi-node support. Functions are executed immediately instead of enqueued in a static graph, see Preparing to use NVIDIA Containers Getting Started Guide. ‣ For non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation based on your platform. ‣ Ensure that

CPU vFast GPU vvFast PCI Bus (or other fabric)GPUs as a First-Class Computing Resource CPU GPU PCI Bus (or other fabric) NIC - Historically, network comm. was CPU-centric 1) Direct GPU access to Infiniband allows GPU-to-GPU network transfers 2) Fast in-node fabrics like NVLink, Infinity Fabric allow very fast intra-node transfers DataGPUs as a First-Class Computing Resource CPU GPU PCI Bus (or fabric) NIC Data - Historically, network comm. was CPU-centric 1) Direct GPU access to Infiniband allows GPU-to-GPU network transfers 2) Fast in-node fabrics like NVLink, Infinity Fabric allow

B" : GPU operation 9Existing TGPSs on Heterogenous Computing - Challenge A C D B! B" 5 task_b = sched.emplace([](&){ 6 // CPU code; // GPU code; 7 }); // CPU thread blocks until GPU finishes B" : GPU operation 10Existing TGPSs on Heterogenous Computing - Challenge A C D B! B" 5 task_b = sched.emplace([](&){ 6 // CPU code; // GPU code; 7 }); // CPU thread blocks until GPU finishes operation B" : GPU operation Atomic execution per task 11Existing TGPSs on Heterogenous Computing - Challenge CPU A B! C Idle GPU D B" Runtime A C D B! B" Assume one CPU and one GPU B! : CPU operation

http://wongmichael.com/about ● C++11 book in Chinese: https://www.amazon.cn/dp/B00ETOV2OQ We build GPU compilers for some of the most powerful supercomputers in the world 34 Nevin “:-)” Liber nliber@anl Attribution 4.0 International License SYCL Single Source C++ Parallel Programming GPU FPGA DSP Custom Hardware GPU CPU CPU CPU Standard C++ Application Code C++ Libraries ML Frameworks give better performance on complex apps and libs than hand-coding AI/Tensor HW GPU FPGA DSP Custom Hardware GPU CPU CPU CPU AI/Tensor HW Other BackendsSYCL 2020 is here! Open Standard for

cudaFree(x); cudaFree(y); } An Even Easier Introduction to CUDA 5 |__global__ void add_gpu(int n, float* x, float* y) { for (int i = 0; i < n; i++) y[i] = x[i] + y[i]; } TEST_CASE("cppcon-1" TEST_CASE("cppcon-1", "[CUDA]") { // … } An Even Easier Introduction to CUDA 6 |__global__ void add_gpu(int n, float* x, float* y) { for (int i = 0; i < n; i++) y[i] = x[i] + y[i]; } TEST_CASE("cppcon-1" 20; float* x; float* y; // … add_gpu<<<1, 1>>>(N, x, y); // … } An Even Easier Introduction to CUDA 7 |__global__ void add_gpu(int n, float* x, float* y) { for (int i = 0;

. . . . . . . . . 6 2.4 Keras 支持多个后端引擎，并且不会将你锁定到一个生态系统中 . . . . . . . . . . 6 2.5 Keras 拥有强大的多 GPU 和分布式训练支持 . . . . . . . . . . . . . . . . . . . . . . 6 2.6 Keras 的发展得到深度学习生态系统中的关键公司的支持 . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.3 如何在 GPU 上运行 Keras? . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.4 如何在多 GPU 上运行 Keras 模型? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 20.9 multi_gpu_model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 21 贡献 242 21

involve experimental prototypes and early research.Problem: writing parallel programs is hard - Multi-GPU, multi-CPU systems require partitioning data - Users must manually split up data amongst GPUs / execution necessary. CPU NIC GPU GPU GPU GPU Xe LinkMulti-GPU Systems - NUMA regions: - 4+ GPUs - 2+ CPUs CPU NIC GPU GPU GPU GPU Xe LinkMulti-GPU Systems - NUMA regions: - 4+ GPUs more memory domains - Software needed to reduce complexity CPU NIC GPU Tile 1 Tile 0 GPU Tile 1 Tile 0 GPU Tile 1 Tile 0 GPU Tile 1 Tile 0 Xe LinkProject Goals - Offer high-level, standard C++

to the GE, and over 400 bug fixes. 2.72 – October 2014: Cycles gets volume and SSS support on the GPU, pie menus are added and tooltips greatly improved, the Intersection modeling tool is added, new Sun Automatic Automatically use GLSL which runs on the GPU for performance but falls back to the CPU for large images which might be slow when loaded with the GPU. 2D Texture Uses CPU for display transform and images. Cycles can use either the CPU or certain GPUs to render images, for more information see the GPU Rendering page. None When set to None or when the only option is None: the CPU will be used as the