Page 3:Vive le GeForce FX!
Page 4:The advent of GPGPU
Page 6:The CUDA APIs
Page 7:A Few Definitions
Page 8:The Theory: CUDA from the Hardware Point of View
Page 9:Hardware Point of View, Continued
Page 10:The Theory: CUDA from the Software Point of View
Page 11:In Practice
Page 15:Conclusion, Continued
However we did decide to measure the processing time to see if there was any advantage to using CUDA even with our crude implementation, or on the other hand if was going to take long, exhaustive practice to get any real control over the use of the GPU. The test machine was our development box – a laptop computer with a Core 2 Duo T5450 and a GeForce 8600M GT, operating under Vista. It’s far from being a supercomputer, but the results are interesting since our test is not all that favorable to the GPU. It’s fine for Nvidia to show us huge accelerations on systems equipped with monster GPUs and enormous bandwidth, but in practice many of the 70 million CUDA GPUs existing on current PCs are much less powerful, and so our test is quite germane.
The results we got are as follows for processing a 2048x2048 image:
- CPU 1 thread: 1419 ms
- CPU 2 threads: 749 ms
- CPU 4 threads: 593 ms
- GPU (8600M GT) blocks of 256 pixels: 109 ms
- GPU (8600M GT) blocks of 128 pixels: 94 ms
- GPU (8800 GTX) blocks of 128 pixels / 256 pixels: 31 ms
Several observations can be made about these results. First of all you’ll notice that despite our crack about programmers’ laziness, we did modify the initial CPU implementation by threading it. As we said, the code is ideal for this situation – all you do is break down the initial image into as many zones as there are threads. Note that we got an almost linear acceleration going from one to two threads on our dual-core CPU, which shows the strongly parallel nature of our test program. Fairly unexpectedly, the four-thread version proved faster, whereas we were expecting to see no difference at all on our processor, or even – and more logically – a slight loss of efficiency due to the additional cost generated by the creation of the additional threads. What explains that result? It’s hard to say, but it may be that the Windows thread scheduler has something to do with it; but in any case the result was reproducible. With a texture with smaller dimensions (512x512), the gain achieved by threading was a lot less marked (approximately 35% as opposed to 100%) and the behavior of the four-thread version was more logical, showing no gain over the two-thread version. The GPU was still faster, but less markedly so (the 8600M GT was three times faster than the two-thread version).
The second notable observation is that even the slowest GPU implementation was nearly six times faster than the best-performing CPU version. For a first program and a trivial version of the algorithm, that’s very encouraging. Notice also that we got significantly better results using smaller blocks, whereas intuitively you might think that the reverse would be true. The explanation is simple – our program uses 14 registers per thread, and with 256-thread blocks it would need 3,584 registers per block, and to saturate a multiprocessor it takes 768 threads, as we saw. In our case, that’s three blocks or 10,572 registers. But a multiprocessor has only 8,192 registers, so it can only keep two blocks active. Conversely, with blocks of 128 pixels, we need 1,792 registers per block; 8,192 divided by 1,792 and rounded to the nearest integer works out to four blocks being processed. In practice, the number of threads are the same (512 per multiprocessor, whereas theoretically it takes 768 to saturate it), but having more blocks gives the GPU additional flexibility with memory access – when an operation with a long latency is executed, it can launch execution of the instructions on another block while waiting for the results to be available. Four blocks would certainly mask the latency better, especially since our program makes several memory accesses.
- Vive le GeForce FX!
- The advent of GPGPU
- The CUDA APIs
- A Few Definitions
- The Theory: CUDA from the Hardware Point of View
- Hardware Point of View, Continued
- The Theory: CUDA from the Software Point of View
- In Practice
- Conclusion, Continued