Some optimization tricks work really well on one architecture, and are useless on others. And even with better drivers, the older architectures need some help. In other words, it helps to know what architecture the GPU has. Therefore you get some help from your friends at StreamComputing.
This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.
Overview The rate of scientific discovery is speeding up every day with the use of advancing technologies like GPUs: scientific results are being published faster than ever before. This three-step tutorial is designed to show you how to take advantage of compilers and libraries to quickly accelerate your codes with GPUs so that you can spend more time on real breakthroughs. All the tools mentioned are freely available as part of the PGI Community Edition.
When building applications that display untrusted content, security designers have a major problem— if an attacker has full control of a block of pixels, he can make those pixels look like anything he wants, including the UI of the application itself. He can then induce the user to undertake an unsafe action, and a user…
At Deep Learning Summit 2017 in San Francisco on this January, PFN announced advancements on distributed deep learning using Chainer in multi-node environment. In this post, I would like to explain the detail of the announcement
This reference is a part of a new series of DSC articles, offering selected tutorials on subjects such as deep learning, machine learning, data science, deep data science, artificial intelligence, Internet of Things, algorithms, and related topics. It is designed for the busy reader who does not have a lot of time digging into long lists of advanced publications.
At rOpenSci we are creating packages that allow access to data repositories through the R statistical programming environment that is already a familiar part of the workflow of many scientists. Our tools not only facilitate drawing data into an environment where it can readily be manipulated, but also one in which those analyses and methods can be easily shared, replicated, and extended by other researchers.
As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon Performance, and Efficient scale-out leveraging Spark architecture.
Google launched a new version of the Translate in September 2016. Since then, there have been a few interesting developments in the project, and this post attempts to explain it all in as simple terms as possible. The earlier version of the Translate used Phrase-based Machine Translation, or PBMT. What PBMT does is break up an…
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