GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.
Over at ORNL, Katie Elyce Jones writes that the US Department of Energy (DOE) is mining for alternatives to rare earth magnetic material, an obviously scarce resource. For manufacturers of electric motors and other devices, procuring these materials involves environmental concerns from mining rare earth metals, their costs, and an unpredictable supply chain.
Since 2011, ONS (responsible for planning and operating the Brazilian Electric Sector) has been using AWS to run daily simulations using complex mathematical models. The use of the MIT StarCluster toolkit makes running HPC on AWS much less complex and lets ONS provision a high performance cluster in less than 5 minutes.
Most IaaS (infrastructure as a service) vendors such as Rackspace, Amazon and Savvis use various virtualization technologies to manage the underlying hardware they build their offerings on. Unfortunately the virtualization technologies used vary from vendor to vendor and are sometimes kept secret. Therefore, the question about virtual machines versus physical machines for high performance computing (HPC) applications is germane to any discussion of HPC in the cloud.
“Optimizing HPC Applications with Intel Cluster Tools takes the reader on a tour of the fast-growing area of high performance computing and the optimization of hybrid programs. These programs typically combine distributed memory and shared memory programming models and use the Message Passing Interface (MPI) and OpenMP for multi-threading to achieve the ultimate goal of high performance at low power consumption on enterprise-class workstations and compute clusters. The book focuses on optimization for clusters consisting of the Intel Xeon processor, but the optimization methodologies also apply to the Intel Xeon Phi coprocessor and heterogeneous clusters mixing both architectures.”
Has Cloud HPC finally made it’s way to the Missing Middle? In this slidecast, Jason Stowe from Cycle Computing describes how the company enabled HGST to spin up a 70,000-core cluster from AWS and then return it 8 hours later. “One of HGST’s engineering workloads seeks to find an optimal advanced drive head design, taking 30 days to complete on an in-house cluster. In layman terms, this workload runs 1 million simulations for designs based upon 22 different design parameters running on 3 drive media Running these simulations using an in-house, specially built simulator, the workload takes approximately 30 days to complete on an internal cluster.”