Tesla C1060 memory performance

According to the CUDA programming guide the memory coalescing rules have been relaxed in devices with compute capability 1.2 or greater.  Chapter 5 has a subsection entitled "Coalescing on Devices with Compute Capability 1.2 and Higher" which gives more information.

As I now have a C1060 which has compute capability of 1.3 I thought I'd run my old coalescing tests on it to see how it has improved.  I modified the launch configuration to 32760 blocks in order to maximize utilization of all the Multiprocessors and increased the thread count to 256. These changes cause the kernels to report 100% utilization in profiler.  I expected the uncoalesced part of the old tests to be quite kind to the Tesla as they fall within the parameters given in the programming guide and indeed the memory transfer rates were similar to a pure device to device copy. I then modified the uncoalesced kernels to avoid the auto coalescing of memory accesses in a half-warp.

NVIDIA Tesla Personal Supercomputing

I attended the NVIDIA Tesla personal supercomputing launch on the 4th December in London. It was really interesting to meet other people using cuda and gpu based devices for solving incredibly compute intensive problems.

What some groups have managed to do in the fields of Tomography / CFD / Molecular modelling and Seismology is truly awe inspiring and its only a matter of time before they have a real inpact on the average persons life. The advances in medical imaging along with the power of Tesla is a prime example in this regard.