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    <p>In timing the original problem, what you're seeing here is that with this naive code, the better specs of the GTX480 are actually hurting you.</p> <p>The code sample, a first pass at a matrix multiply, is completely dominated by memory bandwidth; each thread is accessing a different element of B which can't be coallesced because of the stride. </p> <p>The GTX480 has a 3x larger (384 bit) and 2x faster (1840 MHz) memory bus than the GT330M (128bit, 800 MHz). Nominally, that gives a peak bandwidth advantage of 177.4GB/s vs 25.6 GB/s, and since this is memory-bandwidth dominated, you might think that would win. However, because of the non-coalesced reads and the wider memory bus, the b-array accesses are only using 32 bits of that 384 bit memory access, and in the 330M case, only 32 bits out of each 128 bit access. So the effective memory bandwidths for the b access are 14.8GB/s and 6.4GB/s; so now there's only a factor of 2 difference in total memory bandwidth rather than 7 or so, and so much of the advantage of the faster card is being squandered; in addition, that memory bandwidth has to be divided by 10x as many cores, so the latency for each core to get its access and do the calculation is longer. I suspect that if you used larger matrix sizes, you could hide more of the latency and get at closer to the best-possible 2x speedup rather than the 2.5x slowdown you're seeing.</p> <p>The ultimate solution here is to use a more memory-friendly matrix multiplication algorithm as a benchmark.</p> <p>The profiling results you're seeing, though, I have no idea about. Perhaps the 330M doesn't have as good hardware support for the profiling, so things have to be implemented in software? Since the GTX numbers are about the same either way, I'd just use the simpler timing approach for now, which since you're not using asynchronous kernels or transfer, should be fine.</p>
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