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insideHPC Research Report – GPU Accelerators

In this research report, we reveal recent research showing that customers are feeling the need for speed—i.e. they’re looking for more processing cores. Not surprisingly, we found that they’re investing more money in accelerators like GPUs and moreover are seeing solid positive results from using GPUs. In the balance of this report, we take a look at the newest GPU tech from NVIDIA and how it performs vs. traditional servers and earlier GPU products. Download this guide to learn more.

Managing High Performance GPU Clusters

To fully take advantage of NVIDIA GPUs requires several sound strategies. The goal of any HPC resource should be to increase the productivity of researchers and engineers because minimizing time to solution is the goal of many leading HPC installations. Keeping users and developers focused on applications is one of the way to increase productivity and minimize wasted time.

The GPUltima: Up to a Petaflop of Networked GPUs in a Single Rack

In this week’s industry Perspective, Katie Garrison of One Stop Systems explains how GPUltima allows HPC professionals to create a highly dense compute platform that delivers a petaflop of performance at greatly reduced cost and space requirements.compute power needed to quickly process the amount of data generated in intensive applications.

GPUs in Virtual Desktop Infrastructure Solutions

VDI or Virtual Desktop Infrastructure helps companies save money, time and resources. Instead of large bulky machines on every desk in the office, companies can connect multiple workstations to a single computer using thin clients. Instead of replacing individual desktops every year, companies only have to replace thin clients every 5 years. And when it comes time to do updates, the IT staff updates the one computer instead of spending time updating every individual workstation.

Developing a Plan for Cloud Based GPU Processing

For some applications, cloud based clusters may be limited due to communication and/or storage latency and speeds. With GPUs, however, these issue are not present because application running on cloud GPUs perform exactly the same as those in your local cluster — unless the application span multiple nodes and are sensitive to MPI speeds. For those GPU applications that can work well in the cloud environment, a remote cloud may be an attractive option for both production and feasibility studies.

Planning for the Convergence of HPC and Big Data

As an open source tool designed to navigate large amounts of data, Hadoop continues to find new uses in HPC. Managing a Hadoop cluster is different than managing an HPC cluster, however. It requires mastering some new concepts, but the hardware is basically the same and many Hadoop clusters now include GPUs to facilitate deep learning.

Best Practices for Maximizing GPU Resources in HPC Clusters

HPC developers want to write code and create new applications. The advanced nature of HPC often requires that this process be associated with specific hardware and software environment present on a given HPC resource. Developers want to extract the maximum performance from HPC hardware and at the same time not get mired down in the complexities of software tool chains and dependencies.

Strategies for Managing High Performance GPU Clusters

When discussing GPU accelerators, the focus is often on the price-to-performance benefits to the end user. The true cost of managing and using GPUs goes far beyond the hardware price, however. Understanding and managing these costs helps provide more efficient and productive systems.

Microsoft Embraces GPU Computing

NVIDA’s Sanford Russell writes that Microsoft’s recent work on a new programming language extension called C++ AMP will accelerate the adoption of GPU computing. He contends that the move will push programmers get off the fence and go with GPU computing. “The take away from Microsoft’s announcement today is that the GPU computing space has reached maturity, […]