The high performance computing (HPC) industry’s need for computation is increasing as large and complex computational problems become commonplace across many industry segments. Traditional CPU technology, however, is no longer capable of scaling in performance sufficiently to address this demand.
The parallel processing capability of the GPU allows it to divide complex computing tasks into thousands of smaller tasks that can be run concurrently. This ability is enabling computational scientists and researchers to address some of the world’s most challenging computational problems up to several orders of magnitude faster than CPU-only methods.
While a CPU consists of 4 to 18 CPU cores, a GPU consists of hundreds or thousands of smaller cores. GPU computing is the use of these GPU cores as a co-processor to accelerate CPUs, specifically for very high demand scientific and engineering applications, allowing them to operate together to crunch through the data in the application.
By utilizing the GPU’s massively parallel processing power and offloading some of the compute-intensive and time consuming portions of the code to the GPU’s cores, the GPU can exponentially accelerate applications running on the CPU. This massively parallel architecture is what gives the GPU its high compute performance. The result is the application power of a massive CPU-only cluster in a desktop form factor that consumes less power and increases performance 10x – 100x, solving equations in minutes instead of hours.
From climate modeling to advances in medical tomography, GPU based systems are enabling a wide variety of segments in science and industry to progress in ways that were previously impractical, or even impossible, due to technological limitations. Industries already benefitting from GPU-accelerated applications include:
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