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Baidu Research Announces DeepBench Benchmark for Deep Learning

“Deep learning developers and researchers want to train neural networks as fast as possible. Right now we are limited by computing performance,” said Dr. Diamos. “The first step in improving performance is to measure it, so we created DeepBench and are opening it up to the deep learning community. We believe that tracking performance on different hardware platforms will help processor designers better optimize their hardware for deep learning applications.”

Intel Xeon Phi Boosts Supercomputing at NCI in Australia

The National Computational Infrastructure in Canberra, Australia’s national advanced computing facility, is the first Australian institution to deploy the latest generation of Intel Xeon Phi processors, formerly code named Knights Landing. “NCI is leading efforts in the scientific community to tune applications for Intel Xeon Phi processors,” explains Dr Muhammad Atif, NCI’s HPC Systems and Cloud Services Manager. “We have identified a large number of applications that will benefit from this hardware and software paradigm, including those applications in the domains of computational physics, computational chemistry and climate research.”

Fortran for HPC

“Fortran has been proven to be extremely resilient to new developments that have appeared in other programming languages over the years. New versions continue to be available and associated with ANSI standards, so that an application written for one operating system should be able to be compiled and run with different compilers on different operating systems. The latest version is Fortran 2008, with the next version reportedly to be available as Fortran 2015, in 2018.”

Better Software For HPC through Code Modernization

Vectorization and threading are critical to using such innovative hardware product such as the Intel Xeon Phi processor. Using tools early in the design and development processor that identify where vectorization can be used or improved will lead to increased performance of the overall application. Modern tools can be used to determine what might be blocking compiler vectorization and the potential gain from the work involved.

Deep Learning with the Intel Xeon Phi Processor

“An environment that assists in deep learning usually consists of algorithms that can draw conclusions from data that is run at very high speeds. Processors such as the Intel Xeon Phi Processor that contain a significant number of processing cores and operate in a SIMD mode are critical to these new environments. With the Intel Xeon Phi processor, new insights can be discovered from either existing data or new data sources.”

Cray to Add Intel Xeon Phi to Archer Supercomputing Service in the UK

EPSRC and Cray have signed an agreement to add a Cray XC40 Development System with Intel Xeon Phi processors to ARCHER, the UK National Supercomputing Service. “The new Development system will have a very similar environment to the main ARCHER system, including Cray’s Aries interconnect, operating system and Cray tools, meaning that interested users will enjoy a straightforward transition.”

Python and HPC

“In the HPC domain, Python can be used to develop a wide range of applications. While tight loops may still need to be coded in C or FORTRAN, Python can still be used. As more systems become available with coprocessors or accelerators, Python can be used to offload the main CPU and take advantage of the coprocessor. pyMIC is a Python Offload Module for the Intel Xeon Phi Coprocessor and is available at popular open source code repositories.”

Machine Learning and the Intel Xeon Phi Processor

“With up to 72 processing cores, the Intel Xeon Phi processor x200 can accelerate applications tremendously. Each core contains two Advanced Vector Extensions, which speeds up the floating point performance. This is important for machine learning applications which in many cases use the Fused Multiply-Add (FMA) instruction.”

Taming Heterogeneity in HPC – The DEEP-ER take

Norbert Eicker from the Jülich Supercomputing Centre presented this talk at the SAI Computing Conference in London. “The ultimate goal is to reduce the burden on the application developers. To this end DEEP/-ER provides a well-accustomed programming environment that saves application developers from some of the tedious and often costly code modernization work. Confining this work to code-annotation as proposed by DEEP/-ER is a major advancement.”

Video: Intel Sneak Peek at Knights Mill Processor for Machine Learning

In this video from the 2016 Intel Developer Forum, Diane Bryant describes the company’s efforts to advance Machine Learning and Artificial Intelligence. Along the way, she offers a sneak peak at the Knights Mill processor, the next generation of Intel Xeon Phi slated for release sometime in 2017. “Now you can scale your machine learning and deep learning applications quickly – and gain insights more efficiently – with your existing hardware infrastructure. Popular open frameworks newly optimized for Intel, together with our advanced math libraries, make Intel Architecture-based platforms a smart choice for these projects.”