Processors are often designed with very specific tasks in mind. They’re tailored to excel in their own specific area, and it’s rare for any processor to prove applicable to processes or tasks beyond its original scope.
GPUs provide an example of processors that buck that trend. They were first designed to speed up and improve graphics, specifically within the gaming sector. Nonetheless, developing technology has seen GPUs become integral to a range of other activities. They are now the primary processors used in Bitcoin mining, and they’re also crucial to video analysis and smart surveillance. What’s more, using GPUs for HPC (high-performance computing) is starting to become far more common.
Most computer systems include a CPU and one or more GPUs. The CPU is the general-purpose processor used to power most computing tasks, whereas GPUs were first developed to render on-screen graphics. To make this process more efficient, GPUs feature a collection of cores. The single-purpose design of those cores means they’re much smaller than CPU cores. Consequently, GPUs often have far more of them – potentially thousands of separate cores.
This makes GPUs ideal for massive parallel processing, where many computing tasks are conducted simultaneously. Parallel processing allows GPUs to render graphics at greater speed, as well as explaining the processor’s applicability to diverse applications. And this greater power may be leveraged in many ways…
A high-performance model
High-performance computing (HPC) is a generic term used covering a range of computing activities. At the most basic level, it refers to the aggregation of computing power to deliver higher performance. This aggregation is typically undertaken to solve large or complex problems in the fields of science, business or engineering. Applications using GPUs for HPC might include weather forecasting, data mining or other diverse processes.
A high-performance computer is basically a cluster of computers, sometimes referred to as a ‘supercomputer’. Each computer within a cluster is described as a node, which may include an array of different processors. These combine to provide enhanced processing power. Indeed, the main benefit of building high-performance computers is that nodes work together, enabling them to solve problems which would confound a standalone machine.
Using GPUs for HPC is becoming ever more common since the parallel architecture and performance offered by GPUs make them ideal for HPC workloads. Their use enables researchers and data scientists to boost the processing power of their data centers. They can parse huge amounts of data, and do so in such orders of magnitude and at faster rates than computers reliant on CPUs could achieve.
CPU setups typically utilize x86 chips, which are restricted by the limitations of traditional processors. Energy and time are spent moving data into and out of the chips, making them less suited to parallel processing. Unsurprisingly, many experts believe that using GPUs for HPC is the future, representing one of a number of exciting new applications for these truly multi-purpose processors.