You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’ve used them to attain state-of-the-art results in text sentiment analysis and generative modeling of text and images. The development of model arch
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
Patchset is on top of mmotm mmotm-2017-04-18 and Michal patchset ([PATCH -v3 0/13] mm: make movable onlining suck less). Branch: https://cgit.freedesktop.org/~glisse/linux/log/?h=hmm-v20 I have included all suggestion made since v19, it is all build fix and change in respect to memory hotplug with Michal rework. Changes since v19: - Included various build fix and compilation warning fix - Limit HM
Futhark is a small programming language designed to be compiled to efficient parallel code. It is a statically typed, data-parallel, and purely functional array language in the ML family, and comes with a heavily optimising ahead-of-time compiler that presently generates either GPU code via CUDA and OpenCL, or multi-threaded CPU code. As a simple example, this function computes the average of an a
基礎講座を担当したNVIDIAのCUDA & Deep Learning Solution Architectの村上真奈氏 2017年1月17日に開催されたNVIDIAの「Deep Learning Institute 2017」では、ディープラーニング(深層学習)の基礎講座と実際にNVIDIAのディープラーニング開発ツールである「DIGITS」を使うハンズオントレーニングセッションが行われた。 ニューラルネットワークの基礎 ディープラーニングの基礎講座は、「これから始める人の為の」というもので、NVIDIAディープラーニング部の村上真奈氏がディープラーニングの基本的な考え方や用語などを解説した。 まず、ディープラーニングの位置づけであるが、ディープラーニングは機械学習の1つの方法で、それもニューラルネットワークを使う機械学習の1つのやり方ということになる。
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
Using Intel.com Search You can easily search the entire Intel.com site in several ways. Brand Name: Core i9 Document Number: 123456 Code Name: Emerald Rapids Special Operators: “Ice Lake”, Ice AND Lake, Ice OR Lake, Ice* Quick Links You can also try the quick links below to see results for most popular searches. Product Information Support Drivers & Software
2015年6月のGreen500で1～3位を独占した理研のスーパーコンピュータ(スパコン)「菖蒲」、高エネルギー加速器研究機構(KEK)の「青睡蓮」と「睡蓮」を開発したPEZY Computingの齊藤社長は、「エクサスケールの衝撃(PHP研究所)」と題する本を出している。進歩は加速しており、爆発的な発展が起こる特異点が来ると説くRay Kurtzweilの説のスパコン版という本であり、齊藤社長は、エクサスケールのスパコンの登場が社会全体を大きく変える特異点になるという。この本を読むと、エクサスケールのスパコンの開発に賭ける齊藤社長の意気込みを感じ取ることができる。 その齊藤社長にインタビューを行い、最近の開発状況と、エクサスケールのスパコンへとつながる開発戦略を伺った。なお、PEZY ComputingはスパコンのエンジンとなるメニーコアのPEZY-SCチップの開発を行い、それを液浸の冷
June 4, 2015 Volume 13, issue 5 PDF Hadoop Superlinear Scalability The perpetual motion of parallel performance Neil Gunther, Performance Dynamics Paul Puglia Kristofer Tomasette, Comcast "We often see more than 100 percent speedup efficiency!" came the rejoinder to the innocent reminder that you can't have more than 100 percent of anything. But this was just the first volley from software enginee
Process the whole Wikidata in 7 minutes with your laptop (and Akka Streams) How Akka Streams can be used to process the Wikidata dump in parallel and using constant memory with just your laptop. Here at Intent HQ we use Wikipedia and Wikidata as sources of data. They are very important to us because they both encode an enormous amount of information in several languages that we use to build our To
As research computing and data becomes more complex and diverse, we need more professional services firms and fewer utilties (Note: This post is adapted from #127 of the Research Computing Teams Newsletter) I get to talk with a lot of research computing and data teams - software, data, and systems. Sometimes in these conversations it’s pretty clear that some teams, or the team and their funder, or
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
