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Inside Pascal: NVIDIA’s Newest Computing Platform

Today at the 2016 GPU Technology Conference in San Jose, NVIDIA CEO Jen-Hsun Huang announced the new NVIDIA Tesla P100, the most advanced accelerator ever built. Based on the new NVIDIA Pascal GP100 GPU and powered by ground-breaking technologies, Tesla P100 delivers the highest absolute performance for HPC, technical computing, deep learning, and many computationally intensive datacenter workloads.

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In this blog post I’ll provide an overview of the Pascal architecture and its benefits to you as a developer.

At GTC today, Lars Nyland and I gave a talk about details of the Tesla P100 and the Pascal GP100 architecture. The slides and recording from this talk are now available (GTC on-demand site registration required). To learn more, read the Tesla P100 white paper.

Tesla P100: Extreme Performance and Features for GPU Computing

The GP100 GPU used in Tesla P100 incorporates multiple revolutionary new features and unprecedented performance. Key features of Tesla P100 include:

  • Extreme performance—powering HPC, deep learning, and many more GPU Computing areas;
  • NVLink™—NVIDIA’s new high speed, high bandwidth interconnect for maximum application scalability;
  • HBM2—Fastest, high capacity, extremely efficient stacked GPU memory architecture;
  • Unified Memory and Compute Preemption—significantly improved programming model;
  • 16nm FinFET—enables more features, higher performance, and improved power efficiency.

The Pascal GP100 Architecture: Faster in Every Way

With every new GPU architecture, NVIDIA introduces major improvements to performance and power efficiency. The heart of the computation in Tesla GPUs is the SM, or streaming multiprocessor. The streaming multiprocessor creates, manages, schedules and executes instructions from many threads in parallel.

Like previous Tesla GPUs, GP100 is composed of an array of Graphics Processing Clusters (GPCs), Streaming Multiprocessors (SMs), and memory controllers. GP100 achieves its colossal throughput by providing six GPCs, up to 60 SMs, and eight 512-bit memory controllers (4096 bits total). The Pascal architecture’s computational prowess is more than just brute force: it increases performance not only by adding more SMs than previous GPUs, but by making each SM more efficient. Each SM has 64 CUDA cores and four texture units, for a total of 3840 CUDA cores and 240 texture units.

Pascal GP100 Block Diagram
Pascal GP100 Block Diagram

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CUDA 8 Features Revealed

This week at the GPU Technology Conference developers are getting a preview of some powerful new features coming in CUDA 8 later this year. In this post I wanted to give a quick overview of the major new features of CUDA 8:

  • Support for the Pascal GPU architecture, including the new Tesla P100 accelerator;
  • New Unified Memory capabilities;
  • The new nvGRAPH GPU-Accelerated Graph Analytics library;
  • Powerful new profiling capabilities; and
  • Improved compiler performance and heterogeneous lambda support.

To learn more at GTC 2016, you can check out my talk from GTC 2016, “CUDA 8 and Beyond” (GTC on-demand site registration required).

CUDA 8 Supports the new NVIDIA Pascal Architecture

A crucial goal for CUDA 8 is to provide support for the powerful new Pascal architecture, the first incarnation of which was launched at GTC today: Tesla P100. For full details on P100 and the Pascal GP100 GPU architecture, check out the blog post “Inside Pascal“.

pascal_key_imageIn a nutshell, Tesla P100 provides massive double-, single- and half-precision computational performance, 3x the memory bandwidth of Maxwell GPUs via HBM2 stacked memory, and with its support for NVLink, up to 5x the GPU-GPU communication performance of PCI Express. Pascal also improves support for Unified Memory thanks to a larger virtual address space and a new page migration engine.

CUDA 8 will enable CUDA applications to get high performance on Tesla P100 out of the box. Moreover, improvements in CUDA 8 enable developing efficient code for new Tesla P100 features such as NVLink and improved Unified Memory. Continue reading

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NVLink, Pascal and Stacked Memory: Feeding the Appetite for Big Data

For more recent info on NVLink, check out the post, “How NVLink Will Enable Faster, Easier Multi-GPU Computing”.

NVIDIA GPU accelerators have emerged in High-Performance Computing as an energy-efficient way to provide significant compute capability. The Green500 supercomputer list makes this clear: the top 10 supercomputers on the list feature NVIDIA GPUs. Today at the 2014 GPU Technology Conference, NVIDIA announced a new interconnect called NVLink which enables the next step in harnessing the full potential of the accelerator, and the Pascal GPU architecture with stacked memory, slated for 2016.

Stacked Memory

pascal_modulePascal will support stacked memory, a technology which enables multiple layers of DRAM components to be integrated vertically on the package along with the GPU. Stacked memory provides several times greater bandwidth, more than twice the capacity, and quadrupled energy efficiency, compared to current off-package GDDR5. Stacked memory lets us combine large, high-bandwidth memory in the same package with the GPU, allowing us to place the place the voltage regulators close to the chip for efficient power delivery. Stacked Memory, combined with a new Pascal module that is one-third the size of current PCIe boards, will enable us to build denser solutions than ever before.

Outpacing PCI Express

Today a typical system has one or more GPUs connected to a CPU using PCI Express. Continue reading