Posts Tagged 'Nvidia'

August 21, 2012

High Performance Computing - GPU v. CPU

Sometimes, technical conversations can sound like people are just making up tech-sounding words and acronyms: "If you want HPC to handle Gigaflops of computational operations, you probably need to supplement your server's CPU and RAM with a GPU or two." It's like hearing a shady auto mechanic talk about replacing gaskets on double overhead flange valves or hearing Chris Farley (in Tommy Boy) explain that he was "just checking the specs on the endline for the rotary girder" ... You don't know exactly what they're talking about, but you're pretty sure they're lying.

When we talk about high performance computing (HPC), a natural tendency is to go straight into technical specifications and acronyms, but that makes the learning curve steeper for people who are trying to understand why a solution is better suited for certain types of workloads than technology they are already familiar with. With that in mind, I thought I'd share a quick explanation of graphics processing units (GPUs) in the context of central processing units (CPUs).

The first thing that usually confuses people about GPUs is the name: "Why do I need a graphics processing unit on a server? I don't need to render the visual textures from Crysis on my database server ... A GPU is not going to benefit me." It's true that you don't need cutting-edge graphics on your server, but a GPU's power isn't limited to "graphics" operations. The "graphics" part of the name reflects the original intention for kind of processing GPUs perform, but in the last ten years or so, developers and engineers have come to adapt the processing power for more general-purpose computing power.

GPUs were designed in a highly parallel structure that allows large blocks of data to be processed at one time — similar computations are being made on data at the same time (rather than in order). If you assigned the task of rendering a 3D environment to a CPU, it would slow to a crawl — it handles requests more linearly. Because GPUs are better at performing repetitive tasks on large blocks of data than CPUs, you start see the benefit of enlisting a GPU in a server environment.

The Folding@home project and bitcoin mining are two of the most visible distributed computing projects that GPUs are accelerating, and they're perfect examples of workloads made exponentially faster with the parallel processing power of graphics processing units. You don't need to be folding protein or completing a blockchain to get the performance benefits, though; if you are taxing your CPUs with repetitive compute tasks, a GPU could make your life a lot easier.

If that still doesn't make sense, I'll turn the floor over to the Mythbusters in a presentation for our friends at NVIDIA:

SoftLayer uses NVIDIA Tesla GPUs in our high performance computing servers, so developers can use "Compute Unified Device Architecture" (CUDA) to easily take advantage of their GPU's capabilities.

Hopefully, this quick rundown is helpful in demystifying the "technobabble" about GPUs and HPC ... As a quick test, see if this sentence makes more sense now than it did when you started this blog: "If you want HPC to handle Gigaflops of computational operations, you probably need to supplement your server's CPU and RAM with a GPU or two."

-Phil

April 17, 2012

High Performance Computing for Everyone

This guest blog was submitted by Sumit Gupta, senior director of NVIDIA's Tesla High Performance Computing business.

The demand for greater levels of computational performance remains insatiable in the high performance computing (HPC) and technical computing industries, as researchers, geophysicists, biochemists, and financial quants continue to seek out and solve the world's most challenging computational problems.

However, access to high-powered HPC systems has been a constant problem. Researchers must compete for supercomputing time at popular open labs like Oak Ridge National Labs in Tennessee. And, small and medium-size businesses, even large companies, cannot afford to constantly build out larger computing infrastructures for their engineers.

Imagine the new discoveries that could happen if every researcher had access to an HPC system. Imagine how dramatically the quality and durability of products would improve if every engineer could simulate product designs 20, 50 or 100 more times.

This is where NVIDIA and SoftLayer come in. Together, we are bringing accessible and affordable HPC computing to a much broader universe of researchers, engineers and software developers from around the world.

GPUs: Accelerating Research

High-performance NVIDIA Tesla GPUs (graphics processing units) are quickly becoming the go-to solution for HPC users because of their ability to accelerate all types of commercial and scientific applications.

From the Beijing to Silicon Valley — and just about everywhere in between — GPUs are enabling breakthroughs and discoveries in biology, chemistry, genomics, geophysics, data analytics, finance, and many other fields. They are also driving computationally intensive applications, like data mining and numerical analysis, to much higher levels of performance — as much as 100x faster.

The GPU's "secret sauce" is its unique ability to provide power-efficient HPC performance while working in conjunction with a system's CPU. With this "hybrid architecture" approach, each processor is free to do what it does best: GPUs accelerate the parallel research application work, while CPUs process the sequential work.

The result is an often dramatic increase in application performance.

SoftLayer: Affordable, On-demand HPC for the Masses

Now, we're coupling GPUs with easy, real-time access to computing resources that don't break the bank. SoftLayer has created exactly that with a new GPU-accelerated hosted HPC solution. The service uses the same technology that powers some of the world's fastest HPC systems, including dual-processor Intel E5-2600 (Sandy Bridge) based servers with one or two NVIDIA Tesla M2090 GPUs:

NVIDIA Tesla

SoftLayer also offers an on-demand, consumption-based billing model that allows users to access HPC resources when and how they need to. And, because SoftLayer is managing the systems, users can keep their own IT costs in check.

You can get more system details and pricing information here: SoftLayer HPC Servers

I'm thrilled that we are able to bring the value of hybrid HPC computing to larger numbers of users. And, I can't wait to see the amazing engineering and scientific advances they'll achieve.

-Sumit Gupta, NVIDIA - Tesla

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