Tom's Hardware Q&A With MotionDSP, Continued
TH: Not that long ago, we were seeing test results that showed how users got most of their performance pop simply from having GPU-based acceleration, not necessarily from dropping a small fortune on a top-end card with GPGPU support. Has that changed? Because with APUs, that support is essentially now built-in.
SV: What’s changed is companies like AMD now are shipping these Fusion chips that combine the GPU with the CPU on the same die. That makes a difference. You’re right—before, if you had a discrete GPU, you’d get the most bang for your buck just having the GPU there. But having that discrete GPU meant you were going to be buying a higher-end laptop or desktop. What’s cool about the Fusion chips is that you’ve got a pretty darn good integrated GPU in, say, the Llano platform. For a $500 or $600 price point, a consumer can get a laptop that seriously kicks ass with our software. Before, they would have had to buy a $1000 laptop with a discrete GPU to get the same level of performance.
NB: Let’s try to provide a different metric. A high-end, modern CPU from AMD or Intel will retail in the $200 to $300 range. You can outperform that CPU by a factor of 3x to 5x with a GPU that costs about the same. If you have a discrete GPU that costs about $300 and you run our software on that component, you’ll get a three to five times higher frame rate. Now let’s say you want to spend $1000 on a CPU. As I said, there are certain bottlenecks where massive multi-frame processing cannot happen on that CPU because of memory bandwidth and compute bottlenecks. You’re hitting the limits, and it really becomes irrelevant how much money you’re spending on the CPU. You may be hitting a threshold of six or seven frames per second and you can’t go any faster. That's just because you are hitting certain inherent limits on the CPU the GPU doesn't have.
SV: Nik and I are saying two different things. I’m saying on the consumer side, on a $500 or $600 laptop with Fusion chips like Llano, you’re getting the performance that recently cost $1000 in a laptop. And on the discrete side, he’s saying that for the same price, you’re going to get like five to eight times the performance per dollar from a GPU compared to spending that money on a CPU.
NB: If you go to standard def-video, that’s where CPUs in general fare better than GPUs. But everybody takes 720p or 1080p now with their Android phones and iPhones, so the difference between CPU and the GPU becomes much, much larger. As Sean said, an off-the-shelf laptop you buy for $600 is going to give you the performance of a $2000 desktop from before the heterogeneous computing era, just because you didn't have the power of parallel computing at your fingertips.
TH: We’ve got OpenCL in play from AMD, Nvidia, and Intel now. Nvidia is also still advocating CUDA as a closer-to-the-hardware solution, and Intel's approach seems to be running OpenCL on its CPU cores. As a developer, you obviously need to support as broadly as possible, but technically, how much of a difference do these rival strategies make?
NB: From an ISV perspective, we would like to have one standard that runs everywhere. CUDA, being a vendor-specific technology, is something less appealing to us than OpenCL at this time. Now, it’s a fact that CUDA was the first one out there. Nvidia's the incumbent. But we’ve honestly been impressed by the speed of development on OpenCL-based tools and the entire chain, from like SDK to tools to runtime and how things run in a driver. We’ve seen very significant commitment, and I’m talking from an ISV perspective, where things were not so pretty even 18 or 12 months ago. It was very hard or impossible at that time for us to promise to deliver a solid product using heterogeneous computing on OpenCL. That changed completely over the last year. Right now, AMD is the vendor behind OpenCL development and implementation. We don't know what Intel is going to achieve with Ivy Bridge, but ARM announced that it's going to dip its toes into OpenCL water.
