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.
Accelerated computing continues to gain momentum as the HPC community moves towards Exascale. Our recent Tesla P100 GPU review shows how these accelerators are opening up new worlds of performance vs. traditional CPU-based systems and even vs. NVIDIA’s previous K80 GPU product. We’ve got benchmarks, case studies, and more in the insideHPC Research Report on GPU Accelerators.
NVIDIA is a leading provider of GPU accelerators that are used in many High Performance Computing environments. This research paper from Gabriel Consulting Group explains the need for this new generation of hardware in today’s data center and looks at what new technologies actual users are looking for.
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 these finding and and the newest GPU tech from NVIDIA and how it performs vs. traditional servers and earlier GPU products.
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.
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.
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.
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.
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.
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.