“AMD has been away from the HPC space for a while, but now they are coming back in a big way with an open software approach to GPU computing. The Radeon Open Compute Platform (ROCm) was born from the Boltzmann Initiative announced last year at SC15. Now available on GitHub, the ROCm Platform bringing a rich foundation to advanced computing by better integrating the CPU and GPU to solve real-world problems.”
The big data analytics market has seen rapid growth in recent years. Part of this trend includes the increased use of machine learning (Deep Learning) technologies. Indeed, machine learning speed has been drastically increased though the use of GPU accelerators. The issues facing the HPC market are similar to the analytics market — efficient use of the underlying hardware. A position paper from the third annual Big Data and Extreme Computing conference (2015) illustrates the power of co-design in the analytics market.
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.
Over at the Nvidia Blog, Jamie Beckett writes that the company’s is expanding its Deep Learning Institute with Microsoft and Coursera. The institute provides training to help people apply deep learning to solve challenging problems.
Gary Paek from Intel presented this talk at the HPC User Forum in Austin. “Traditional high performance computing is hitting a performance wall. With data volumes exploding and workloads becoming increasingly complex, the need for a breakthrough in HPC performance is clear. Intel Scalable System Framework provides that breakthrough. Designed to work for small clusters to the world’s largest supercomputers, Intel SSF provides scalability and balance for both compute- and data intensive applications, as well as machine learning and visualization. The design moves everything closer to the processor to improve bandwidth, reduce latency and allow you to spend more time processing and less time waiting.”
PRACE has announced the winners of its 13th Call for Proposals for PRACE Project Access. Selected proposals will receive allocations to the following PRACE HPC resources: Marconi and MareNostrum.
“SGI and Bright Computing have been working together for the last year to provide our joint customers with enterprise level clustered infrastructure management software for production supercomputing,” said Gabriel Broner, vice president and general manager of HPC, SGI. “By partnering with Bright Computing, our customers are able to select the cluster management tool that best suits their needs.”
Today the Energy Department’s Advanced Manufacturing Office announced up to $3 million in available funding for manufacturers to use high-performance computing resources at the Department’s national laboratories to tackle major manufacturing challenges. The High Performance Computing for Manufacturing (HPC4Mfg) program enables innovation in U.S. manufacturing through the adoption of high performance computing (HPC) to advance applied science and technology in manufacturing, with an aim of increasing energy efficiency, advancing clean energy technology, and reducing energy’s impact on the environment.
“The growing number of use cases that object storage can satisfy represents a huge opportunity for DDN – especially as cases like collaboration and active archive for large and ‘forever’ data sets are concentrated in DDN customer sites and well-established DDN markets,” said Molly Rector, CMO, executive vice president product management and worldwide marketing at DDN. “WOS’ differentiated benefits give it a strong competitive advantage for current and emerging use cases, and with multiple appliance and software-only options customers have complete architectural flexibility and choice.”
Nvidia’s GPU platforms have been widely used on the training side of the Deep Learning equation for some time now. Today the company announced a new Pascal-based GPU tailor-made for the inferencing side of Deep Learning workloads. “With the Tesla P100 and now Tesla P4 and P40, NVIDIA offers the only end-to-end deep learning platform for the data center, unlocking the enormous power of AI for a broad range of industries,” said Ian Buck, general manager of accelerated computing at NVIDIA.”