Using GPUs for Parallel Processing

The GPUs inside graphics cards have tremendous parallel processing capabilities and research is on to use this capability for non-graphics applications

Saturday, March 10, 2007

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Thought graphics cards could do only graphics? Think again! If you look at the action in the x86 graphics processing arena over the past six months or so, you would notice they are talking about something called 'Stream Computing.' To be truthful, stream computing by itself is nothing new. It is taking a process with multiple threads that can be executed independently of each other, and executing them in one go using multiple processors in parallel. How is this related to graphics processors? We will get into the details shortly, but consider this: a modern graphics processor (GPU) consists of up to 128 parallel processing units called 'pixel shaders.' Now these pixel shaders cannot do fantastic things by themselves individually. But take a bunch of them and run parallel threads through them and you will get much better computer performance.

This has value in applications that have a large pool of data and the same instruction needs to be applied to all of it. Some applications that require this sort of computing are used for protein sequences analysis and geographical data mapping from satellites. So how does stream computing on graphics processors affect you? Until now, stream computing involved special stream computing machines (much like super computers) and were restricted to high-budget research institutions. But now, as graphics cards have this spare computing capacity within them, they can be harnessed for the same effect at a much lower cost. And, vendors of such cards (like AMD/ATi and NVIDIA) have come up with mechanisms to let software developers produce stream computing applications that can run on an off-the-shelf workstation with nothing more powerful than a modern graphics card. So maybe your next payroll processing engine will need the latest high-end gaming card instead of the latest in server clusters to do their job faster.

Inside a modern GPU
Inside a GPU on any modern graphics card you pickup, there are specialized processors (let's call them 'cores' instead) called 'vertex' and 'fragment' shaders. A vertex shader operates on a vertex of any polygon being considered for rendering. For instance, such a processor would determine the position and color of the vertex. A fragment shader works on sets of pixels and generates lighting effects (for example) on these pixels. Different shader models that have evolved in-step with successive DirectX specifications have increased the capabilities of what GPUs can do. For instance, higher floating point precision called double-precision, involving 64-bit values instead of traditional 16 or 32 bit ones has enabled a lighting effects paradigm called HDR (High Dynamic Range) that allows for more realistic shadows and effects.

Consider a GPU with 8 layers of 8 fragment shaders. This makes up 64 cores. This group can render 64 fragments simultaneously. This sort of processing is of course called SIMD (Single Instruction Multiple Data) that we discussed in an earlier part of this series. To sum up, SIMD is an operation where you apply a single instruction (or transformation) on multiple data elements. For example increasing the light on a set of rendered pixels is an example of SIMD. Let's take SIMD a step further.

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