In this week’s Sponsored Post, Katie Garrison, of One Stop Systems explains how GPUs and Flash solutions are used in radar simulation and anti-submarine warfare applications. “High-performance compute and flash solutions are not just used in the lab anymore. Government agencies, particularly the military, are using GPUs and flash for complex applications such as radar simulation, anti-submarine warfare and other areas of defense that require intensive parallel processing and large amounts of data recording.”
High-performance computing (HPC) tools are helping financial firms survive and thrive in this highly demanding and data-intensive industry. As financial models grow in complexity and greater amounts of data must be processed and analyzed on a daily basis, firms are increasingly turning to HPC solutions to exploit the latest technology performance improvements. Suresh Aswani, Senior Manager, Solutions Marketing, at Hewlett Packard Enterprise, shares how to overcome the learning curve of new processor architectures.
“A new data type called a “posit” is designed for direct drop-in replacement for IEEE Standard 754 floats. Unlike unum arithmetic, posits do not require interval-type mathematics or variable size operands, and they round if an answer is inexact, much the way floats do. However, they provide compelling advantages over floats, including simpler hardware implementation that scales from as few as two-bit operands to thousands of bits. For any bit width, they have a larger dynamic range, higher accuracy, better closure under arithmetic operations, and simpler exception-handling.”
In-Memory Computing can accelerate traditional applications by using a memory first design. Applicable to a wide range of domains, In-Memory Computing and In-Memory Data Grids take advantage of the latest trends in computer systems technology. “In-memory computing is designed to address some of the most critical and real-time task requirements today. This include real-time fraud detection, biometrics and border security and financial risk analytics. All of these use cases require very low latency access to data from very large amounts of data, which results in faster and more accurate decisions.”
“D-Wave’s leap from 1000 qubits to 2000 qubits is a major technical achievement and an important advance for the emerging field of quantum computing,” said Earl Joseph, IDC program vice president for high performance computing. “D-Wave is the only company with a product designed to run quantum computing problems, and the new D-Wave 2000Q system should be even more interesting to researchers and application developers who want to explore this revolutionary new approach to computing.”
“Intel recently announced the first product release of its High Performance Python distribution powered by Anaconda. The product provides a prebuilt easy-to-install Intel Architecture (IA) optimized Python for numerical and scientific computing, data analytics, HPC and more. It’s a free, drop in replacement for existing Python distributions that requires no changes to Python code. Yet benchmarks show big Intel Xeon processor performance improvements and even bigger Intel Xeon Phi processor performance improvements.”
In this week’s Sponsored Post, Nicolas Dube of Hewlett Packard Enterprise outlines the future of HPC and the role and challenges of exascale computing in this evolution. The HPE approach to exascale is geared to breaking the dependencies that come with outdated protocols. Exascale computing will allow users to process data, run systems, and solve problems at a totally new scale, which will become increasingly important as the world’s problems grow ever larger and more complex.
To achieve high performance, modern computer systems rely on two basic methodologies to scale resources: scale-up or scale-out. The scale-up in-memory system provides a much better total cost of ownership and can provide value in a variety of ways. “If the application program has concurrent sections then it can be executed in a “parallel” fashion. Much like using multiple bricklayers to build a brick wall. It is important to remember that the amount and efficiency of the concurrent portions of a program determine how much faster it can run on multiple processors. Not all applications are good candidates for parallel execution.”
“The multidisciplinary research team and computational facilities –including MareNostrum– make BSC an international centre of excellence in e-Science. Since its establishment in 2005, BSC has developed an active role in fostering HPC in Spain and Europe as an essential tool for international competitiveness in science and engineering. The center manages the Red Española de Supercomputación (RES), and is a hosting member of the Partnership for Advanced Computing in Europe (PRACE) initiative.”
Applications such as machine learning and deep learning require incredible compute power, and these are becoming more crucial to daily life every day. These applications help provide artificial intelligence for self-driving cars, climate prediction, drugs that treat today’s worst diseases, plus other solutions to more of our world’s most important challenges. There is a multitude of ways to increase compute power but one of the easiest is to use the most powerful GPUs.