{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## RDF\n", "The radial distribution function (RDF) denoted in equations by g(r) defines the probability of finding a particle at a distance r from another tagged particle. The RDF is strongly dependent on the type of matter so will vary greatly for solids, gases and liquids.\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you might have observed the code complexity of the algorithm in $N^{2}$ . Let us get into details of the sequential code. **Understand and analyze** the code present at:\n", "\n", "[RDF Serial Code](../../source_code/serial/rdf.f90)\n", "\n", "[Makefile](../../source_code/serial/Makefile)\n", "\n", "Open the downloaded file for inspection." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cd ../../source_code/serial && make clean && make" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plan to follow the typical optimization cycle that every code needs to go through\n", "\n", "\n", "In order analyze the application we we will make use of profiler \"nsys\" and add \"nvtx\" marking into the code to get more information out of the serial code. Before running the below cells, let's first start by divining into the profiler lab to learn more about the tools. Using Profiler gives us the hotspots and helps to understand which function is important to be made parallel.\n", "\n", "-----\n", "\n", "#
[Profiling lab](../../../../../profiler/English/jupyter_notebook/profiling.ipynb)
\n", "\n", "-----\n", "\n", "Now, that we are familiar with the Nsight Profiler and know how to [NVTX](../../../../../profiler/English/jupyter_notebook/profiling.ipynb#nvtx), let's profile the serial code and checkout the output." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cd ../../source_code/serial&& nsys profile -t nvtx --stats=true --force-overwrite true -o rdf_serial ./rdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you run the above cell, you should see the following in the terminal.\n", "\n", "\n", "\n", "To view the profiler report, you would need to [Download the profiler output](../../source_code/serial/rdf_serial.qdrep) and open it via the GUI. For more information on how to open the report via the GUI, please checkout the section on [How to view the report](../../../../../profiler/English/jupyter_notebook/profiling-c.ipynb#gui-report). \n", "\n", "From the timeline view, right click on the nvtx row and click the \"show in events view\". Now you can see the nvtx statistic at the bottom of the window which shows the duration of each range. In the following labs, we will look in to the profiler report in more detail. \n", "\n", "\n", "\n", "The obvious next step is to make **Pair Calculation** algorithm parallel using different approaches to GPU Programming. Please follow the below link and choose one of the approaches to parallelise th serial code.\n", "\n", "-----\n", "\n", "#
[HOME](../../../nways_MD_start.ipynb)
\n", "-----\n", "\n", "\n", "# Links and Resources\n", "\n", "\n", "[NVIDIA Nsight System](https://docs.nvidia.com/nsight-systems/)\n", "\n", "\n", "\n", "\n", "\n", "[Profiling timelines with NVTX](https://devblogs.nvidia.com/cuda-pro-tip-generate-custom-application-profile-timelines-nvtx/)\n", "\n", "**NOTE**: To be able to see the Nsight System profiler output, please download Nsight System latest version from [here](https://developer.nvidia.com/nsight-systems).\n", "\n", "Don't forget to check out additional [OpenACC Resources](https://www.openacc.org/resources) and join our [OpenACC Slack Channel](https://www.openacc.org/community#slack) to share your experience and get more help from the community.\n", "\n", "--- \n", "\n", "## Licensing \n", "\n", "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 4 }