ate units *， Jamp-/evre/matrix operations exposed in the CUDA WUMAA4 4AP1 FP16 or FP32 FP16 or FP32 Background 和 CUDA C half a[16x8] wmma::fragmentCUDA 本 codegen IR | TensorCore Intrinsics "。Kind of Auto Tensorization 下 CUDA CodeGen *。IR passes to automatically transform sub-tree to TensorCore Intrinsics Pattern Matching

Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your

但是在体系结构领域却有专家较早地认识到深度神经网络的潜在影响，从 2010 年便开始探索 加速深度神经网络的处理器架构设计。另一方面，2003 年前后，英伟达开始追求 GPU 的高 性能算力与可编程性，为 GPU+CUDA 生态大厦打下地基，成为今天 AI 算力生态的统治 82 / 111 者。 3.1 先驱者：DianNao 家族 AI 处理器 2010 年的体系结构领域国际旗舰会议 ISCA 在法国召开，当时来自法国 TPU[15]、Meta 开始自研 MTIA 芯片、Tesla 自研 Dojo 芯片等等。可以说，在这一轮全球 AI 处理器热潮中，中国科研团队起到了当之无愧的引领作用。 3.2 英伟达 GPU 与 CUDA 2001 年，斯坦福大学 Bill Dally 教授团队在处理器微结构旗舰期刊《IEEE Micro》上发 表了一篇题为“Imagine:Media Processing with Streams”的论文正式介绍 上那样进行编程。这项工作旋即得到英伟达的青睐， 于是 Buck 博士毕业后便立刻加入英伟达，带领两位工程师创立了 CUDA 项目。2007 年， CUDA 1.0 正式发布，全面适配 GeForce 8800 系列 GPU。随后，UIUC 胡文美教授团队 在 GeForce 8800 GPU 上用 CUDA 实现一些程序，性能比通用 CPU 高 10 倍到 400 倍不等，充分展示了 GPU 的高性能与可编程性[18]。至此，英伟达的

learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge FPGA Cloud FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning related workloads Credit: Siyuan FengWhere are we goingUnified Runtime For Heterogeneous Devices CUDA Driver NPU Driver Device Drivers External Runtimes NPUModule CUDAModule TFModule tvm::runtime::Module

端机器学习服务的云平台。 这些工具和库专门为加速机器学习模型的训练和推理而设计，通常利 用 GPU 或 TPU 等硬件。这类工具可以显著提高训练和推理的速度， 使得处理大规模数据集和复杂模型变得可行。NVIDIA CUDA 和 Google Cloud TPU 均是此类工具。 这类工具通常由开源社区支持和维护，提供了灵活、可扩展的工具和 库来构建和训练大型机器学习模型，如 TensorFlow 和 PyTorch 昆仑芯 XPU 从AI落地的实际需求出发，按 照复杂前沿的人工智能场景需求开展迭代，致力为开发者提供通用、易用、高性能的算力来源。 DCU 系列产品以 GPGPU 架构为基础，兼容通用的“类 CUDA”环境以及国际主流商业计 算软件和人工智能软件，可广泛应用于大数据处理、人工智能、商业计算等应用领域。 通用 GPU 高端芯片及超级算力系统提供商。拥有云边协同、训推组合的完整通用算力 系统

or not include some features of Blender. For example, some distributions do not build Blender with CUDA support, for licensing reasons. If there is a specific package for your distribution, you may choose only option is None: the CPU will be used as the computing device for Cycles. CUDA If the system has a compatible Nvidia CUDA device, it will show up an option for rendering with Cycles. OptiX If the enable GPU rendering, go into the Preferences ‣ System ‣ Cycles Render Devices, and select either CUDA, OptiX or OpenCL. Next, you must configure each scene to use GPU rendering in Properties ‣ Render

or not include some features of Blender. For example, some distributions do not build Blender with CUDA support, for licensing reasons. If there is a specific package for your distribution, you may choose only option is None: the CPU will be used as the computing device for Cycles. CUDA If the system has a compatible Nvidia CUDA device, it will show up an option for rendering with Cycles. OptiX If the enable GPU rendering, go into the Preferences ‣ System ‣ Cycles Render Devices, and select either CUDA, OptiX or OpenCL. Next, you must configure each scene to use GPU rendering in Properties ‣ Render

or not include some features of Blender. For example, some distributions do not build Blender with CUDA support, for licensing reasons. If there is a specific package for your distribution, you may choose only option is None: the CPU will be used as the computing device for Cycles. CUDA If the system has a compatible Nvidia CUDA device, it will show up an option for rendering with Cycles. OptiX If the enable GPU rendering, go into the Preferences ‣ System ‣ Cycles Render Devices, and select either CUDA, OptiX or OpenCL. Next, you must configure each scene to use GPU rendering in Properties ‣ Render

or not include some features of Blender. For example, some distributions do not build Blender with CUDA support, for licensing reasons. If there is a specific package for your distribution, you may choose only option is None: the CPU will be used as the computing device for Cycles. CUDA If the system has a compatible Nvidia CUDA device, it will show up an option for rendering with Cycles. OptiX If the enable GPU rendering, go into the Preferences ‣ System ‣ Cycles Render Devices, and select either CUDA, OptiX or OpenCL. Next, you must configure each scene to use GPU rendering in Properties ‣ Render

difference.  2021 年 7 月，OpenAI 宣布推出一种类似于 Python 的开源编程语言 Triton，并发布了 Triton 1.0 版本。目标是替代 Nvidia CUDA，可使没有 CUDA 经验的研究人员能够编写 高效的 GPU 代码，且大多数情况下可以与专家所能编写的代码质量媲美。  ● In July 2021, OpenAI announced the release similar to Python, and released version 1.0 of Triton. The goal is to replace Nvidia CUDA, enabling researchers without CUDA experience to write efficient GPU code that, for the most part, is as good as what