In the first process we build a 4096×4096 matrix of random data and in the second process, a 1024×1024 matrix of random data. In this example, we create two processes to create a large amount of data and compute the mean. We can then run the program with Nsight Systems CLI: > MKL_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 nsys profile -t nvtx,osrt,cuda -stats=true -force-overwrite=true -output=multiprocessing python nvtx-multiprocess.pyįigure 2: NVIDIA Nsight Systems timeline visualization of the mean calculation workflow. color="blue")Ĭtx = multiprocessing.get_context("spawn")Ĭtx.Process(name="calculate", target=big_computation, args=]) Like py-spy, NVTX can also profile across multiple processes and threads. NVTX Push-Pop Range Statistics (nanoseconds) Time(%) Though nsys also present that view as well This means we can better evaluate end-to-end workflows and introspect code as the workflow proceeds and not just how much time total we speed in loop or f(). But unlike other profiling tools, this view is a timeline. We can visualize the timeline of the workflow by loading the qdrep file with Nsight Systems UI (available on all OSes)įigure 1: NVIDIA Nsight Systems timeline visualization of the workflowĪt first glance this isn’t the most exciting profile - the code itself is uncomplicated. In this case, nvtx annotations and OS RunTime (OSRT) functions (read/select/etc).Īfter both nsys and the python program finish, two files are generated: a qdrep file and a sqlite database.
The option -t nvtx,osrt defines what nsys should capture. We can then run the program with Nsight Systems CLI: > nsys profile -t nvtx,osrt -force-overwrite=true -stats=true -output=quickstart python nvtx-quickstart.py In the above, we’ve annotated a function, f, and a for loop. To get information about the annotated code, we typically need to run it with a third-party application such as NVIDIA Nsight Systems. For example: import timeĪnnotating code by itself doesn’t achieve anything. Python developers can either use decorators a context manager with nvtx.annotate(.): to mark code to be measured. NVTX is a code annotation tool and can be used to mark functions or chunks of code.
This means we can “see” CUDA calls like cudaMalloc, cudaMemcpy, etc. We can use Nsight Systems to trace standard Python functions, PyData libraries like Pandas/NumPy, and even the underlying C/C++ code of those same PyData libraries! Nsight Systems also ships with additional hooks for CUDA to give developers insight to what is happening on the device (on the GPU). NVTX is an annotation library for code in multiple languages, including Python, C, C++. Fortunately, such tooling exists - NVIDIA Tools Extension (NVTX) and Nsight Systems together are powerful tools for visualizing CPU and GPU performance. Developers need tooling to help debug/profile and generally understand what is happening on the GPU from Python. None of the Python profilers can profile code running on the GPU. While there are many great profiling tools within the Python ecosystem: line-profilers like cProfile and profilers which can observe code execution in C-extensions like PySpy/ Viztracer. As PyData leverages much of the static language world for speed including CUDA, we need tools which not only profile and measure across languages but also devices, CPU, and GPU.
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