ArcSoft’s Total Media Theater is one of the top names in PC-based movie playback, and the SimHD functionality within the application, which enhances video with resolution upscaling and other features, has long been an early beneficiary of GPU-based hardware acceleration. Kam Shek, director of technical marketing for ArcSoft, sat down with Tom’s Hardware to provide an inside glimpse of his company’s process in making the switch to OpenCL-based acceleration.
Tom's Hardware: Many people fail to appreciate the expense and hard work required of hardware vendors in order to enable next-generation software. Obviously, the graphics vendors have a vested interest in popularizing OpenCL. How did that interest translate into action with ArcSoft?
Kam Shek: There are a couple of things we’ve done in optimizing our software. For AMD specifically, we added support for the fixed-function UVD 3 decode logic that includes hardware acceleration for the MPEG-4 ASP [Advanced Simple] profile. We also have our SimHD technology, which is our in-house upscaler. We recently ported that to the OpenCL language. In the past, we used ATI Stream, but about six months ago, we started the port to the new industry standard, working very closely with the company on that.
TH: Why did you choose to support OpenCL?
KS: Because OpenCL provides a heterogeneous environment for us, meaning we can actually program on OpenCL regardless of what part of the algorithm might be running on the CPU or GPU. As a result, we can have a much better balancing of the system resources when we schedule our algorithm between those two.
TH: In moving from proprietary approaches like Stream and CUDA to OpenCL, what advantages do you see in the new technology over the old?
KS: Well, one of the most important is that OpenCL is an open standard. ATI Stream was proprietary. Second, OpenCL allows us to scale better between the CPU and GPU. Let me explain a little more what we do with SimHD. SimHD is about more than upscaling. It has multiple processing units in it. One is for scaling, but then there’s a denoise unit, deblocking, sharpening, dynamic lighting, etc. So some of the algorithms by nature run better on CPU. For instance, if there’s case switching, which happens a lot in dynamic lighting, that’s more efficient on the CPU. But for floating-point, high-precision calculations, those actually run better on the GPU. So we schedule our algorithm between which function runs better on which processing core, then we balance accordingly.
TH: Do you have a sense of the optimization ratio, CPU versus GPU?
KS: I think we’re seeing, on our AMD system here, CPU usage drop by about 10% and then the GPU utilization increases about 20%.
TH: So increasing GPU usage by 20% saves 10% off your CPU utilization?
KS: I’d say 15% to 20%, yes, but I’d probably need to look up the exact number.
TH: Are there any considerations that must be made when coding for an APU compared to a discrete GPU?
KS: No, from an OpenCL perspective, it’s all the same. Now, before we run the algorithm, we do query the capability of the graphics. If the graphics have more Stream cores available, then we’ll run more algorithms on them. But in general, it’s the same. We don’t need a different binary for discrete graphics versus an APU. However, I should add something. As I said, the GPU is better for many floating-point, high-precision tasks. The CPU version for these algorithms are somewhat streamed down, so the precision is not as good as running on the GPU. The algorithm for the GPU is more complex than the one for the CPU, so it provides better precision.
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