NVIDIA is now publishing Linux GPU kernel modules as open source with dual GPL/MIT license, starting with the R515 driver release. You can find the source code for these kernel modules in the NVIDIA/open-gpu-kernel-modules GitHub page This release is a significant step toward improving the experience of using NVIDIA GPUs in Linux, for tighter integration with the OS, and for developers to debug, i

NVIDIA uses containers to develop, test, benchmark, and deploy deep learning (DL) frameworks and HPC applications. We wrote about building and deploying GPU containers at scale using NVIDIA-Docker roughly two years ago. Since then,  NVIDIA-Docker has been downloaded close to 2 million times. A variety of customers used NVIDIA-Docker to containerize and run GPU accelerated workloads. NVIDIA offers

The Peak-Performance-Percentage Analysis Method for Optimizing Any GPU Workload Figuring out how to reduce the GPU frame time of a rendering application on PC is challenging for even the most experienced PC game developers. In this blog post, we describe a performance triage method we’ve been using internally at NVIDIA to let us figure out the main performance limiters of any given GPU workload (a

Many of today’s applications process large volumes of data. While GPU architectures have very fast HBM or GDDR memory, they have limited capacity. Making the most of GPU performance requires the data to be as close to the GPU as possible. This is especially important for applications that iterate over the same data multiple times or have a high flops/byte ratio. Many real-world codes have to selec

A defining feature of the new NVIDIA Volta GPU architecture is Tensor Cores, which give the NVIDIA V100 accelerator a peak throughput that is 12x the 32-bit floating point throughput of the previous-generation NVIDIA P100. Tensor Cores enable you to use mixed-precision for higher throughput without sacrificing accuracy. Tensor Cores provide a huge boost to convolutions and matrix operations. They

In efficient parallel algorithms, threads cooperate and share data to perform collective computations. To share data, the threads must synchronize. The granularity of sharing varies from algorithm to algorithm, so thread synchronization should be flexible. Making synchronization an explicit part of the program ensures safety, maintainability, and modularity. CUDA 9 introduces Cooperative Groups, w

CUDA 9 Features Revealed: Volta, Cooperative Groups and More Figure 1: CUDA 9 provides a preview API for programming Tesla V100 Tensor Cores, providing a huge boost to mixed-precision matrix arithmetic for deep learning. At the 2017 GPU Technology Conference NVIDIA announced CUDA 9, the latest version of CUDA’s powerful parallel computing platform and programming model. CUDA 9 is now available as

NVIDIA NCCL The NVIDIA Collective Communication Library (NCCL) implements multi-GPU and multi-node communication primitives optimized for NVIDIA GPUs and Networking. NCCL provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter as well as point-to-point send and receive that are optimized to achieve high bandwidth and low latency over PCIe and NVLink high-speed interconn

Personalized Aesthetics: Recording the Visual Mind using Machine Learning Visual aesthetics are very personal, often subconscious, and hard to express.  In a world with an overload of photographic content, a lot of time and effort is spent manually curating photographs, and it’s often hard to separate the good images from the visual noise. The question we put forward at EyeEm is: can a machine lea

This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA. I wrote a previous post, Easy Introduction to CUDA in 2013 that has been popular over the years. But CUDA programming has gotten easier, and GPUs have gotten much faster, so it’s time for an updated (and even easier) introduction. CUDA C++ is just one of the ways you can cre

Over the last few years there has been a dramatic rise in the use of containers for deploying data center applications at scale. The reason for this is simple: containers encapsulate an application’s dependencies to provide reproducible and reliable execution of applications and services without the overhead of a full virtual machine. If you have ever spent a day provisioning a server with a multi

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. Tod

Today I’m excited to announce the general availability of CUDA 8, the latest update to NVIDIA’s powerful parallel computing platform and programming model. In this post I’ll give a quick overview of the major new features of CUDA 8. Support for the Pascal GPU architecture, including the new Tesla P100, P40, and P4 accelerators; New Unified Memory capabilities; Native FP16 and INT8 computation for

If you’re like me, you have a GPU-accelerated in-situ visualization toolkit that you need to run on the latest-generation supercomputer. Or maybe you have a fantastic OpenGL application that you want to deploy on a server farm for offline rendering. Even though you have access to all that amazing GPU power, you’re often out of luck when it comes to GPU-accelerated rendering. The reason is that it’

This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. The first part in this series provided an overview over the field of deep learning, covering fundamental and core concepts. The third part of the series covers sequence learning topics such as recurrent neural networks and LSTM. In this secon

Introduction to Neural Machine Translation with GPUs (part 1) Note: This is the first part of a detailed three-part series on machine translation with neural networks by Kyunghyun Cho. You may enjoy part 2 and part 3. Neural machine translation is a recently proposed framework for machine translation based purely on neural networks. This post is the first of a series in which I will explain a simp