Five Nvidia CUDA-Enabled Apps Tested

crazipper writes "Much fuss has been made about Nvidia's CUDA technology and its general-purpose computing potential. Now, in 2009, a steady stream of launches from third-party software developers sees CUDA gaining traction at the mainstream. Tom's Hardware takes five of the most interesting desktop apps with CUDA support and compares the speed-up yielded by a pair of mainstream GPUs versus a CPU-only. Not surprisingly, depending on the workload you throw at your GPU, you'll see results ranging from average to downright impressive."

Comments

h.264 encoding

[BikeHelmet]

h.264 encoding didn't improve with more shaders for some of the results(like PowerDirector 7), because of the law of diminishing returns.

I remember reading about x264 when quad-cores were becoming common. It mentioned that if quality is of the utmost importance, you should still encode on a single core. It splits squares of pixels between the cores; where those squares connect there can be very minor artifacts. It smooths these artifacts out with a small amount of extra data and post processing; the end result is a file hardly 1-2% bigger than if encoded on a single core, but encoded roughly 4x faster.

Now, if we're talking about 32 cores, or 64, or 128, would the size difference be bigger than 1-2%? Probably. After a certain point, it would almost certainly not be worth it.

This is supported by Badaboom's results, where the higher resolution videos (with more encoded squares) seem to make use of more shaders when encoding, while most of the lower resolution vids do not. (indicating that some shaders may be lying idle)

What I'm curious about, is could the 9800GTX encode two videos at once, while the 9600GT could only manage one? ;)

I'm also curious why the 320x240 video encoded so quickly - but that could be from superior memory bandwidth, shader clockspeed, and some other important factor in h.264 encoding.

Take it with a grain of salt; I'm not an encoder engineer; just regurgitating what I once read, hopefully accurately. ;)

OpenCL is an Open Standard Compute Language

[Gary W. Longsine]

It's not really clear what you're looking for, possibly because you're looking for the wrong thing. It might help if you first spend an hour or three learning a little more about OpenCL, and reading up at various sites to see who's doing what.

OpenCL is an Open Standard compute language which comprises:

• a language extended from C99,
• a platform (hardware + OpenCL-aware device driver), and
• a compiler and runtime (which may decide where to send a compute task at run time).

If you're writing an OpenCL-aware device device driver for a GPU, you'll probably need to wait a bit for some open source examples. It's reasonably likely that there will be some included in Darwin (once updated for Snow Leopard).

Look to the LLVM project (sponsored heavily by Apple and others) for an open source compiler which will (if it doesn't already) know about OpenCL.

It sounds like you might be looking for a higher level API which allows you to more easily use the OpenCL, or possibly for language bindings to Java or Python perhaps? I suspect you'll see those coming along, once Apple ships Snow Leopard, and people have a chance to kick the tires, and then integrate LLMV into their tool chains, extend various higher level API, bridge to Java and whatnot.

The earliest high level API to take easy and broad advantage of OpenCL will probably be from Apple, of course. They'll likely provide some nicely automatic ways to take advantage of OpenCL without programming the OpenCL C API directly. As a Cocoa programmer, you'll be using various high level objects, maybe an indexer for example, which have been taught new OpenCL tricks. You'll just recompile your program and it will tap the GPU as appropriate and if available. The Cocoa implementation is closed source, but people will see what's possible and emulate it in various open source libraries, on other platforms, for Java and other languages.

Here's a good place to start: OpenCL - Parallel Computing on the GPU and CPU. Follow up with a google search.