详解 Nvidia Ampere 架构

NVIDIA的Ampere架构在计算性能、AI和光线追踪方面取得重大进步,通过增加CUDA核心、第三代TensorCore和第二代RTCore,提升GPU的性能、能效和AI训练效率。A100GPU的MIG功能和PCIe4.0支持使其适用于云计算和数据中心。

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NVIDIA Ampere架构是NVIDIA推出的高性能GPU微架构,它是继Volta和Turing架构之后的新一代技术。Ampere架构在2020年首次发布,并被用于构建多种类型的GPU产品,包括数据中心的A100以及面向消费者的GeForce RTX 30系列显卡。Ampere架构代表了在计算性能、能效和AI加速方面的重大进步。

主要特点和改进:

  1. 更多的CUDA核心:Ampere架构显著增加了CUDA核心数量,提高了每个SM(流式处理器)的计算能力,使得整体性能得到大幅提升。

  2. 第三代Tensor Core:Ampere引入了第三代Tensor Core,这些专用核心针对AI和深度学习计算进一步优化,支持更高效的混合精度运算,显著提升了AI训练和推理的性能。

  3. 第二代RT Core:Ampere架构加入了第二代RT Core,这些核心专门为光线追踪技术设计,提供更高效的光线追踪计算能力,带来更逼真的渲染效果。

  4. 更大的内存带宽和容量:Ampere GPU使用了更快的内存技术(如GDDR6X)和更大的内存容量,为大规模数据集和复杂应用提供支持。

  5. 改进的能效:Ampere架构在保持或提升性能的同时,也优化了能效比,使得同等能耗下能够执行更多的计算任务。

  6. 多实例GPU(MIG)功能:A100引入了多实例GPU功能,允许将GPU划分为多个独立的硬件分区,每个分区可以运行不同的任务,适用于云计算和数据中心环境。

  7. PCI Express 4.0支持:Ampere GPU支持PCI Express 4.0标准,提供比前一代GPU更高的数据传输速度。

  8. 支持更多的并发运算:Ampere架构支持更多的并发运算和更复杂的计算任务,适合执行高性能计算(HPC)和复杂的数据分析。

  9. 异步复制:提供了一个新的异步复制指令,可将数据直接从全局内存加载到 SM 共享内存中,无需使用中间寄存器文件 (RF)。异步复制可减少寄存器文件带宽,更有效地使用内存带宽,并降低功耗。顾名思义,当 SM 执行其他计算时,可以在后台完成异步复制。

以 NVIDIA GA100 为 例子

NVIDIA GA100 GPU 由多个 GPU 处理集群 (GPC)、纹理处理集群 (TPC)、流式多处理器 (SM) 和 HBM2 内存控制器组成。

GA100 GPU 的完整实现包括以下单元:

  • 8 个 GPC、8 个 TPC/GPC、2 个 SM/TPC、16 个 SM/GPC、128
NVIDIA A100 Tensor Core GPU Architecture UNPRECEDENTED ACCELERATION AT EVERY SCALE Introduction The diversity of compute-intensive applications running in modern cloud data centers has driven the explosion of NVIDIA GPU-accelerated cloud computing. Such intensive applications include AI deep learning training and inference, data analytics, scientific computing, genomics, edge video analytics and 5G services, graphics rendering, cloud gaming, and many more. From scaling-up AI training and scientific computing, to scaling-out inference applications, to enabling real-time conversational AI, NVIDIA GPUs provide the necessary horsepower to accelerate numerous complex and unpredictable workloads running in today’s cloud data centers. NVIDIA® GPUs are the leading computational engines powering the AI revolution, providing tremendous speedups for AI training and inference workloads. In addition, NVIDIA GPUs accelerate many types of HPC and data analytics applications and systems, allowing customers to effectively analyze, visualize, and turn data into insights. NVIDIA’s accelerated computing platforms are central to many of the world’s most important and fastest-growing industries. HPC has grown beyond supercomputers running computationally-intensive applications such as weather forecasting, oil & gas exploration, and financial modeling. Today, millions of NVIDIA GPUs are accelerating many types of HPC applications running in cloud data centers, servers, systems at the edge, and even deskside workstations, servicing hundreds of industries and scientific domains. AI networks continue to grow in size, complexity, and diversity, and the usage of AI-based applications and services is rapidly expanding. NVIDIA GPUs accelerate numerous AI systems and applications including: deep learning recommendation systems, autonomous machines (self-driving cars, factory robots, etc.), natural language processing (conversational AI, real-time language translation, etc.), smart city video analytics, software-defined 5G networks (that can deliver AI-based services at the Edge), molecular simulations, drone control, medical image analysis, and more.
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