kernels that do nothing and are not being passed any arguments. If you then run on a host system with a fast CPU with high single-thread performance you should be able to get close to 5 usec when launching null kernels, i.e.
#Cudalaunch nvprof driver#
If you need / want low launch overhead, either use Windows with a TCC driver ( not possible with a consumer GPU like GTX 745) or use Linux. Batching often causes the overhead of specific launches to fluctuate from close to the lower limit imposed by hardware (around 5 usec) to much higher values (e.g. I am referring to the average because with WDDM the CUDA driver tries to batch launches in order to reduce the average launch overhead. But it is often detrimental to performance as well as other aspects such as GPU memory allocation.Īverage launch overhead of around 25 usec seems perfectly normal in a WDDM scenario. This has benefits from Microsoft’s perspective, such as increased system stability compared to the previous Windows XP driver model. With WDDM (introduced with Windows 7), and even more so with WDDM 2.x under Windows 10, it is the Windows operating system that controls most aspects of GPU operation. What operating system are you on? Number one reason for high launch overhead is the use of Windows with a WDDM driver. (int int char* short* and three int*).Īs always and help or guidance would be most welcome Thank you The kernel has 6 scalar arguments (five int, one float) and 7 pointer/array arguments The GPU and so does not include cudaDeviceSynchronize() etc. LongY reported about half a microsecond (420 tics) but that was internal to I think means about 30% of my elapse time is disappearing in just
As expected the time between kernel>Īnd following gpuErrchk( cudaDeviceSynchronize() ) falls but only to about 30% So I tried an experiment: in the existing code I insertĪ conditional return as the first line, which is always true. I have been puzzled by the performance of my GPU code.
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