GPU Case Study Highlights Financial Application Acceleration

Richard Fichera

NVIDIA recently shared a case study involving risk calculations at a JP Morgan Chase that I think is significant for the extreme levels of acceleration gained by integrating GPUs with conventional CPUs, and also as an illustration of a mainstream financial application of GPU technology.

JP Morgan Chase’s Equity Derivatives Group began evaluating GPUs as computational accelerators in 2009, and now runs over half of their risk calculations on hybrid systems containing x86 CPUs and NVIDIA Tesla GPUs, and claims a 40x improvement in calculation times combined with a 75% cost savings. The cost savings appear to be derived from a combination of lower capital costs to deliver an equivalent throughput of calculations along with improved energy efficiency per calculation.

Implicit in the speedup of 40x, from multiple hours to several minutes, is the implication that these calculations can become part of a near real-time business-critical analysis process instead of an overnight or daily batch process. Given the intensely competitive nature of derivatives trading, it is highly likely that JPMC will enhance their use of GPUs as traders demand an ever increasing number of these calculations. And of course, their competition has been using the same technology as well, based on numerous conversations I have had with Wall Street infrastructure architects over the past year.

My net take on this is that we will see a succession of similar announcements as GPUs become a fully mainstream acceleration technology as opposed to an experimental fringe. If you are an I&O professional whose users are demanding extreme computational performance on a constrained space, power and capital budget, you owe it to yourself and your company to evaluate the newest accelerator technology. Your competitors are almost certainly doing so.

Intel Steps On The Accelerator, Reveals Many Independent Core Road Map

Richard Fichera

While NVIDIA and to a lesser extent AMD (via its ATI branded product line) have effectively monopolized the rapidly growing and hyperbole-generating market for GPGPUs, highly parallel application accelerators, Intel has teased the industry for several years, starting with its 80-core Polaris Research Processor demonstration in 2008. Intel’s strategy was pretty transparent – it had nothing in this space, and needed to serve notice that it was actively pursuing it without showing its hand prematurely. This situation of deliberate ambiguity came to an end last month when Intel finally disclosed more details on its line of Many Independent Core (MIC) accelerators.

Intel’s approach to attached parallel processing is radically different than its competitors and appears to make excellent use of its core IP assets – fabrication and expertise and the x86 instruction set. While competing products from NVIDIA and AMD are based on graphics processing architectures, employing 100s of parallel non-x86 cores, Intel’s products will feature a smaller (32 – 64 in the disclosed products) number of simplified x86 cores on the theory that developers will be able to harvest large portions of code that already runs on 4 – 10 core x86 CPUs and easily port them to these new parallel engines.

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