![]() ![]() In this case, we’ll be looking at a CUDA code that does a matrix-matrix element-wise add operation, effectively a vector add, but using a 2D CUDA grid configuration, along with 2D (i.e. We’re going to analyze a code that is a variant of the vector add code that was used in the previous blog. inspecting timelines, measuring activity durations, etc.) can be performed using Nsight Systems. Many of the other profiler activities you may be interested in (e.g. This blog focuses on how to do that using Nsight Compute. ![]() If you’ve used either the NVIDIA Visual Profiler, or nvprof (the command-line profiler), you may have inspected specific metrics for your CUDA kernels. /rebates/&252fcudalaunch-nvprof. One of the main purposes of Nsight Compute is to provide access to kernel-level analysis using GPU performance metrics. For the example project in this blog, using the new tools will be necessary to get the results we are after for Turing architecture GPUs and beyond.Īs covered previously, Nsight Compute and Nsight Systems differ in their purpose and functionality, so profiling activities will be accomplished in one or the other of these new tools. The tools become more and more important when using newer GPU architectures. If you design kernels that run for a long time and do a lot of work, this overhead can become insignificant.By now, hopefully you read the first two blogs in this series “ Migrating to NVIDIA Nsight Tools from NVVP and Nvprof” and “ Transitioning to Nsight Systems from NVIDIA Visual Profiler / nvprof,” and you’ve discovered NVIDIA added a few new tools, both Nsight Compute and Nsight Systems, to the repertoire of CUDA tools available for developers. I don't think the exact reason why a GPU kernel launch takes ~5-50 microseconds of CPU time is documented or explained anywhere in an authoritative fashion, and it is a closed source library, so you will need to acknowledge that overhead as something you have little control over. The duration here is a function of what the code in your kernel is doing, and how long it takes. The start and end of this yellow block are marked by the start and end of kernel activity on the GPU. The GPU yellow block represents the actual time period during which the kernel was executing on the GPU. your next line of code after the kernel launch). The end of this period is marked by the time at which the library returns control to your code (i.e. The start of this period is marked by the start of the call into the library. ![]() This library call activity usually has some time overhead associated with it, in the range of 5-50 microseconds. The CPU (API) yellow block represents the duration of time that the CPU thread spends in a library call into the CUDA Runtime library, to launch the kernel (i.e. So there is no defined relationship between the CPU (API) activity, and the GPU activity with respect to time, except that the CPU kernel launch must obviously precede (at least slightly) the GPU kernel execution. In fact, the CPU activity is actually placing the kernel in a launch queue - the actual execution of the kernel may be delayed if anything else is happening on the GPU. This means that commands in the default stream may. Second, these default streams are regular streams. This means that commands issued to the default stream by different host threads can run concurrently. First, it gives each host thread its own default stream. That means that the CPU thread launches the kernel but does not wait for the kernel to complete. CUDA 7 introduces a new option, the per-thread default stream, that has two effects. Note that you mention NVPROF but the pictures you are showing are from nvvp - the visual profiler. Sum up vector c and print result divided by n, this should equal 1 within error Int id = blockIdx.x*blockDim.x+threadIdx.x ĬudaMemcpy( h_c, d_c, bytes, cudaMemcpyDeviceToHost ) How to query for a specific metric say Dram reads. nvprof -query-metrics How to query for all metric nvprof -metrics all. ![]() How to find all the metric available for the device It is a big list see at EOF. _global_ void vecAdd(double *a, double *b, double *c, int n) How to get all/certain meterics from nvprof. Each thread takes care of one element of c Please notice that the start, end, and duration of those yellow blocks in CPU and GPU are different.Why CPU invocation of vecAdd>(d_a, d_b, d_c, n) takes that long time? #include Visual Profiler and nvprof now support NVLink analysis for devices with compute capability 6.0. What is the definition of start and end of kernel launch in the CPU and GPU (yellow block)? Where is the boundary between them? The profiling tools contain a number of changes and new features as part of the CUDA Toolkit 8.0 release. ![]()
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