![]() ![]() Check Show value in OSD and any other options (I like Units in superscript).Select the stats you want sent to RTSS, and for each,.Check Enable hotkey for toggling, and set the hotkey, I use Control- Shift- F12.RTSS using internal HAL data source (HWiNFO not required), or.There are three ways to setup the OSD, and all of them need RTSS running. We will then use this to demonstrate the overheads involved with the standard launch mechanism and show how to introduce a CUDA Graph comprising the multiple kernels, which can be launched from the application in a single operation.The usual disclaimers - don’t download from untrusted sources, and don’t blindly follow what random stranges say (I am, of course, referring to myself). We are going to create a simple code which mimics this pattern. The ExampleĬonsider a case where we have a sequence of short GPU kernels within each timestep: Loop over timesteps In this article, we demonstrate how to get started using CUDA Graphs, by showing how to augment a very simple example. They address the above issue by providing a mechanism to launch multiple GPU operations through a single CPU operation, and hence reduce overheads. If each of these operations is launched to the GPU separately, and completes quickly, then overheads can combine to form a significant overall degradation to performance.ĬUDA Graphs have been designed to allow work to be defined as graphs rather than single operations. For a simulation technique to accurately model nature, typically multiple algorithmic stages corresponding to multiple GPU operations are required per timestep. For example, simulations of molecular systems iterate over many timesteps, where the position of each molecule is updated at each step based on the forces exerted on it by the other molecules. Real applications perform large numbers of GPU operations: a typical pattern involves many iterations (or timesteps), with multiple operations within each step. However, there are overheads associated with the submission of each operation to the GPU – also at the microsecond scale – which are now becoming significant in an increasing number of cases. kernel or memory copy) is now measured in microseconds. Modern GPUs are so fast that, in many cases of interest, the time taken by each GPU operation (e.g. The performance of GPU architectures continue to increase with every new generation. ![]()
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