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.”
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.”
“The major functionality of the Intel Xeon Phi coprocessor is a chip that does the heavy computation. The current version utilizes up to 16 channels of GDDR5 memory. An interesting notes is that up to 32 memory devices can be used, by using both sides of the motherboard to hold the memory. This doubles the effective memory availability as compared to more conventional designs.”
“Few fields are moving faster right now than deep learning,” writes Buck. “Today’s neural networks are 6x deeper and more powerful than just a few years ago. There are new techniques in multi-GPU scaling that offer even faster training performance. In addition, our architecture and software have improved neural network training time by over 10x in a year by moving from Kepler to Maxwell to today’s latest Pascal-based systems, like the DGX-1 with eight Tesla P100 GPUs. So it’s understandable that newcomers to the field may not be aware of all the developments that have been taking place in both hardware and software.”
LANL reports that a moment of inspiration during a wiring diagram review has saved more than $2 million in material and labor costs for the Trinity supercomputer at Los Alamos National Laboratory.
In this Intel Chip Chat Podcast, Nidhi Chappell, the Director of Machine Learning Strategy at Intel discusses the company’s planned acquisition of Nervana Systems to further drive Intel’s capabilities in the artificial intelligence (AI) field. “We will apply Nervana’s software expertise to further optimize the Intel Math Kernel Library and its integration into industry standard frameworks. Nervana’s Engine and silicon expertise will advance Intel’s AI portfolio and enhance the deep learning performance and TCO of our Intel Xeon and Intel Xeon Phi processors.”
“High performance systems now typically a host processor and a coprocessor. The role of the coprocessor is to provide the developer and the user the ability to significantly speed up simulations if the algorithm that is used can run with a high degree of parallelization and can take advantage of an SIMD architecture. The Intel Xeon Phi coprocessor is an example of a coprocessor that is used in many HPC systems today.”
AMD’s motivation for developing these open-source GPU tools is based on an opportunity to remove the added complexity of proprietary programming frameworks to GPU application development. “If successful, these tools – or similar versions – could help to democratize GPU application development, removing the need for proprietary frameworks, which then makes the HPC accelerator market much more competitive for smaller players. For example, HPC users could potentially use these tools to convert CUDA code into C++ and then run it on an Intel Xeon co-processor.”
The ability to develop applications independent of the hardware availability at run time is a very important concept that enables developers to take advantage of the latest and greatest processing and coprocessing power. Without having to make run time checks on hardware availability is critical to a smooth running HPC environment.
Raj Hazra presented this talk at ISC 2016. “As part of the company’s launch of the Intel Xeon Phi processor, Hazra describes how how cognitive computing and HPC are going to work together. “Intel will introduce and showcase a range of new technologies helping to fuel the path to deeper insight and HPC’s next frontier. Among this year’s new products is the Intel Xeon Phi processor. Intel’s first bootable host processor is specifically designed for highly parallel workloads. It is also the first to integrate both memory and fabric technologies. A bootable x86 CPU, the Intel Xeon Phi processor offers greater scalability and is capable of handling a wider variety of workloads and configurations than accelerator products.”