{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallelizing analysis\n", "\n", "As we approach the exascale barrier, researchers are handling increasingly large volumes of molecular dynamics (MD) data. Whilst MDAnalysis is a flexible and relatively fast framework for complex analysis tasks in MD simulations, implementing a parallel computing framework would play a pivotal role in accelerating the time to solution for such large datasets.\t\n", "\n", "This document illustrates how you can run your own analysis scripts in parallel with MDAnalysis.\n", "\n", "**Last executed:** Sep 23, 2020 with MDAnalysis 2.0.0-dev0\n", "\n", "**Last updated:** August 2020\n", "\n", "**Minimum version of MDAnalysis:** 2.0.0\n", "\n", "**Packages required:**\n", " \n", "* MDAnalysis (Michaud-Agrawal *et al.*, 2011, Gowers *et al.*, 2016)\n", "* MDAnalysisData\n", "* dask (https://dask.org/)\n", "* dask.distributed (https://distributed.dask.org/en/latest/) \n", "* joblib (https://joblib.readthedocs.io/en/latest/)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import MDAnalysis as mda\n", "from MDAnalysisData.adk_equilibrium import fetch_adk_equilibrium\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from multiprocessing import cpu_count\n", "n_jobs = cpu_count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "In MDAnalysis, most implemented analysis methods are based on `AnalysisBase`, which provides a generic API for users to write their own trajectory analysis. However, this framework only takes single-core power of the PC by iterating through the trajectory and running a frame-wise analysis. Below we aim to first explore some possible simple implementations of parallelism, including using multiprocessing and dask. We will also discuss the acceleration approaches that should be considered, ranging from your own multiple-core laptops/desktops to distributed clusters. \"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The test files we will be working with here feature adenylate kinase (AdK), a phosopho-transferase enzyme. (Beckstein *et al.*, 2009). The trajectory has 4187 frames, which will take quite some time to run the analysis on with the conventional serial (single-core) approach.\n", "\n", "Note: downloading these datasets from MDAnalysisData may take some time." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "adk = fetch_adk_equilibrium()\n", "u = mda.Universe(adk.topology, adk.trajectory)\n", "protein = u.select_atoms('protein')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u.trajectory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Radius of gyration\n", "For a detail description of this analysis, read [Writing your own trajectory](https://mdanalysis.org/UserGuide/examples/analysis/custom_trajectory_analysis.html).\n", "\n", "Here is a common form of single-frame method that we can normally see inside `AnalysisBase`. It may contain both some dynamic parts that changes along time either implicitly or explicitly (e.g. `AtomGroup`) and some static parts (e.g. a reference frame)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def radgyr(atomgroup, masses, total_mass=None):\n", " # coordinates change for each frame\n", " coordinates = atomgroup.positions\n", " center_of_mass = atomgroup.center_of_mass()\n", " \n", " # get squared distance from center\n", " ri_sq = (coordinates-center_of_mass)**2\n", " # sum the unweighted positions\n", " sq = np.sum(ri_sq, axis=1)\n", " sq_x = np.sum(ri_sq[:,[1,2]], axis=1) # sum over y and z\n", " sq_y = np.sum(ri_sq[:,[0,2]], axis=1) # sum over x and z\n", " sq_z = np.sum(ri_sq[:,[0,1]], axis=1) # sum over x and y\n", " \n", " # make into array\n", " sq_rs = np.array([sq, sq_x, sq_y, sq_z])\n", " \n", " # weight positions\n", " rog_sq = np.sum(masses*sq_rs, axis=1)/total_mass\n", " # square root and return\n", " return np.sqrt(rog_sq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Serial Analysis\n", "\n", "Below is the serial version of the analysis that we normally use." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "result = []\n", "for frame in u.trajectory:\n", " result.append(radgyr(atomgroup=protein,\n", " masses=protein.masses,\n", " total_mass=np.sum(protein.masses)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result = np.asarray(result).T\n", "\n", "labels = ['all', 'x-axis', 'y-axis', 'z-axis']\n", "for col, label in zip(result, labels):\n", " plt.plot(col, label=label)\n", "plt.legend()\n", "plt.ylabel('Radius of gyration (Å)')\n", "plt.xlabel('Frame');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parallelization in a simple per-frame fashion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frame-wise form of the function\n", "\n", "The coordinates of the ``atomgroup`` analysed change with each frame of the trajectory. We need to explicitly point the analysis function to the frame that needs to be analysed with a ``frame_index``: `atomgroup.universe.trajectory[frame_index]` in order to update the positions (and any other dynamic per-frame information) appropriately. Therefore, the first step to making the ``radgyr`` function parallelisable is to add a ``frame_index`` argument." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def radgyr_per_frame(frame_index, atomgroup, masses, total_mass=None):\n", " \n", " # index the trajectory to set it to the frame_index frame\n", " atomgroup.universe.trajectory[frame_index]\n", " \n", " # coordinates change for each frame\n", " coordinates = atomgroup.positions\n", " center_of_mass = atomgroup.center_of_mass()\n", " \n", " # get squared distance from center\n", " ri_sq = (coordinates-center_of_mass)**2\n", " # sum the unweighted positions\n", " sq = np.sum(ri_sq, axis=1)\n", " sq_x = np.sum(ri_sq[:,[1,2]], axis=1) # sum over y and z\n", " sq_y = np.sum(ri_sq[:,[0,2]], axis=1) # sum over x and z\n", " sq_z = np.sum(ri_sq[:,[0,1]], axis=1) # sum over x and y\n", " \n", " # make into array\n", " sq_rs = np.array([sq, sq_x, sq_y, sq_z])\n", " \n", " # weight positions\n", " rog_sq = np.sum(masses*sq_rs, axis=1)/total_mass\n", " # square root and return\n", " return np.sqrt(rog_sq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parallelization with multiprocessing\n", "\n", "The native parallelisation module in Python is called [multiprocessing](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing). It contains useful tools to build a pool of working cores, map the function into different workers, and gather and order the results from all the workers.\n", "\n", "Below we use `Pool` from `multiprocessing` as a context manager. We can define how many cores (or workers) we want to use with `Pool(n_jobs)`. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import multiprocessing\n", "from multiprocessing import Pool\n", "from functools import partial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use `functools.partial` to create a new method by supplying every argument needed for `radgyr_per_frame` *except* `frame_index`. We can do this because the `atomgroup`, `masses` etc. will not change when we iterate the function over each frame, but the `frame_index` will. We create a list of jobs where we use the `worker_pool` to map each `frame_index` to each job." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "run_per_frame = partial(radgyr_per_frame, \n", " atomgroup=protein,\n", " masses=protein.masses,\n", " total_mass=np.sum(protein.masses))\n", "\n", "frame_values = np.arange(u.trajectory.n_frames)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "with Pool(n_jobs) as worker_pool:\n", " result = worker_pool.map(run_per_frame, frame_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `result` will be a list of arrays containing the result for each frame.\n", "Finally the results can be plotted along time." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result = np.asarray(result).T\n", "\n", "labels = ['all', 'x-axis', 'y-axis', 'z-axis']\n", "for col, label in zip(result, labels):\n", " plt.plot(col, label=label)\n", "plt.legend()\n", "plt.ylabel('Radius of gyration (Å)')\n", "plt.xlabel('Frame');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parallelization with dask\n", "\n", "[Dask](https://docs.dask.org/en/latest/) is a flexible library for parallel computing in Python. It provides advanced parallelism for analytics and has been integrated or utilized in many scientific softwares. It can be scaled from one single computer to a cluster of computers inside a HPC center.\n", "\n", "Dask has a dynamic task scheduling system with synchronous (single-threaded), threaded, multiprocessing and distributed schedulers. The wrapping function in dask, `dask.delayed`, mimics for loops and wraps Python code into a Dask graph. This code can then be easily run in parallel, and visualized with `dask.visualize()` to examine if the task is well distributed. The code inside `dask.delayed` is not run immediately on execution, but pushed into a job queue waiting for submission. You can read more on [dask website](https://docs.dask.org/en/latest/delayed.html).\n", "\n", "\n", "Comaring to `multiprocssing`, the downside of `multiprocessing` is that it is mostly focused on single-machine multicore parallelism (without extra manager). It is hard to operate on multimachine conditions. Below are two simple examples to use Dask to achieve the same task as `multiprocessing` does.\n", "\n", "The API of `dask` is similar to `multiprocessing`. It also creates a pool of workers for your single machine with the given resources.\n", "\n", "Note: The threaded scheduler in Dask (similar to `threading` in Python) should not be used as it will mess up with the state (timestep) of the trajectory." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dask\n", "import dask.multiprocessing\n", "dask.config.set(scheduler='processes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is how you can utilize `dask.distributed` module to build a local cluster.\n", "\n", "Note: this is not really needed for your laptop/desktop. Using dask.distributed may even slow down the performance, but it provides a diagnostic dashboard that can provide valuable insight on performance and progress.\n", "\n", "See limitations here: https://distributed.dask.org/en/latest/limitations.html" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "\n", "client = Client(n_workers=n_jobs)\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we have to create a list of jobs and transform them with `dask.delayed()` so they can be processed by Dask." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "job_list = []\n", "for frame_index in range(u.trajectory.n_frames):\n", " job_list.append(dask.delayed(radgyr_per_frame(frame_index,\n", " atomgroup=protein,\n", " masses=protein.masses,\n", " total_mass=np.sum(protein.masses))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we simply use `dask.compute()` to get a list of ordered results." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "result = dask.compute(job_list)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result = np.asarray(result).T\n", "\n", "labels = ['all', 'x-axis', 'y-axis', 'z-axis']\n", "for col, label in zip(result, labels):\n", " plt.plot(col, label=label)\n", "plt.legend()\n", "plt.ylabel('Radius of gyration (Å)')\n", "plt.xlabel('Frame');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use the old `radgyr` function because `dask` is more flexible on the input arguments.\n", "\n", "Note: the associated timestamp of `protein` will change during the trajectory iteration, so the processes are always aware which timestep the trajectory is in and change the `protein` (e.g. its coordinates) accordingly." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "job_list = []\n", "for frame in u.trajectory:\n", " job_list.append(dask.delayed(radgyr(atomgroup=protein,\n", " masses=protein.masses,\n", " total_mass=np.sum(protein.masses))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we simply use `dask.compute()` to get a list of ordered results." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "result = dask.compute(job_list)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result = np.asarray(result).T\n", "\n", "labels = ['all', 'x-axis', 'y-axis', 'z-axis']\n", "for col, label in zip(result, labels):\n", " plt.plot(col, label=label)\n", "plt.legend()\n", "plt.ylabel('Radius of gyration (Å)')\n", "plt.xlabel('Frame');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use Dask dashboard (with dask.distributed.Client) to examine how jobs are distributed along all the workers. Each green bar below represents one job, i.e. running `radgyr` on one frame of the trajectory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\n", " \n", "![Dask task stream](../images/parallel_analysis/per_frame_dask.png)\n", "\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parallelization in a split-apply-combine fashion\n", "\n", "The aforementioned per-frame approach should normally be **avoided** because in **each** task, all the attributes (`AtomGroup`, `Universe`, and etc) need to be pickled. This pickling may take even more time than your lightweight analysis! Besides, in Dask, a significant amount of overhead time is needed to build a comprehensive Dask graph with thousands of tasks.\n", "\n", "Therefore, we should normally use a split-apply-combine scheme for parallel trajectory analysis. Here, the trajectory is **split** into blocks, analysis is performed separately and in parallel on each block (\\\"apply\\\"), and then results from each block are gathered and **combined**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\n", " \n", "![Split-apply-combine approach](../images/parallel_analysis/dask_sac.png)\n", "
\n", "
\n", "\n", "Image from (Shujie *et al.*, 2019) used under CC-BY license." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will show a simple illustration of split-apply-combine approach with dask below:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Block analysis function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`@dask.delayed` is a common syntax to decorate a function into delayed-enabled. It is the same as delaying the function by `dask.delayed(analyze_block)(bs, radgyr, ...)` later on." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "@dask.delayed\n", "def analyze_block(blockslice, func, *args, **kwargs): \n", " result = [] \n", " for ts in u.trajectory[blockslice.start:blockslice.stop]: \n", " A = func(*args, **kwargs) \n", " result.append(A) \n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split the trajectory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a very simple way to split the trajectory. It splits the trajectory into defined `n_blocks` which is normally the same as the number of cores you want to use.\n", "\n", "Since it is achieved by evenly dividing the `n_frames` by `n_blocks`, and setting the last block to end at the last frame, sometime it is not really balanced (e.g. the last block)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "n_frames = u.trajectory.n_frames\n", "n_blocks = n_jobs # it can be any realistic value (0 < n_blocks <= n_jobs)\n", "\n", "n_frames_per_block = n_frames // n_blocks\n", "blocks = [range(i * n_frames_per_block, (i + 1) * n_frames_per_block) for i in range(n_blocks-1)]\n", "blocks.append(range((n_blocks - 1) * n_frames_per_block, n_frames))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[range(0, 348),\n", " range(348, 696),\n", " range(696, 1044),\n", " range(1044, 1392),\n", " range(1392, 1740),\n", " range(1740, 2088),\n", " range(2088, 2436),\n", " range(2436, 2784),\n", " range(2784, 3132),\n", " range(3132, 3480),\n", " range(3480, 3828),\n", " range(3828, 4187)]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "blocks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply the analysis per block" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "jobs = []\n", "for bs in blocks:\n", " jobs.append(analyze_block(bs,\n", " radgyr,\n", " protein,\n", " protein.masses,\n", " total_mass=np.sum(protein.masses)))\n", "jobs = dask.delayed(jobs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `visualize()` we can see that the trajectory is split into a few blocks instead of ~4000 jobs." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jobs.visualize()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "results = jobs.compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combine the results" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "result = np.concatenate(results)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result = np.asarray(result).T\n", "\n", "labels = ['all', 'x-axis', 'y-axis', 'z-axis']\n", "for col, label in zip(result, labels):\n", " plt.plot(col, label=label)\n", "plt.legend()\n", "plt.ylabel('Radius of gyration (Å)')\n", "plt.xlabel('Frame');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you look at the Dask dashboard (with dask.distributed.Client) this time, you will see each green bar below represents a per-block analysis for `radgyr`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\n", " \n", "![Dask task stream with blocks](../images/parallel_analysis/block_dask.png)\n", "\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other possible parallelism approaches for multiple analyses\n", "\n", "You may want to perform multiple analyses (or analyze multiple trajectories). In this case, you can use some high-level parallelism, i.e. running all the serial analyses in parallel.\n", "\n", "Here we use [joblib](https://joblib.readthedocs.io/en/latest/). It is implemented on `multiprocessing` and provides lightweight pipelining in Python. Compared to `multiprocessing`, it has a simple API and convenient persistence of cached results.\"" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "from joblib import Parallel, delayed\n", "import multiprocessing\n", "num_cores = multiprocessing.cpu_count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we leverage the power of `AnalysisFromFunction` to fast construct a class that will iterate through the trajectory and save the analysis results.\n", "\n", "If you want to know more about how `AnalysisFromFunction` works, you can read it from [Writing your own trajectory](https://mdanalysis.org/UserGuide/examples/analysis/custom_trajectory_analysis.html)." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from MDAnalysis.analysis.base import AnalysisFromFunction " ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "rog_1 = AnalysisFromFunction(radgyr, u.trajectory,\n", " protein, protein.masses,\n", " total_mass=np.sum(protein.masses))\n", "\n", "rog_2 = AnalysisFromFunction(radgyr, u.trajectory,\n", " protein, protein.masses,\n", " total_mass=np.sum(protein.masses))\n", "\n", "rog_3 = AnalysisFromFunction(radgyr, u.trajectory,\n", " protein, protein.masses,\n", " total_mass=np.sum(protein.masses))\n", "\n", "rog_4 = AnalysisFromFunction(radgyr, u.trajectory,\n", " protein, protein.masses,\n", " total_mass=np.sum(protein.masses))\n", "\n", "analysis_ensemble = [rog_1, rog_2, rog_3, rog_4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`run_analysis` is a simple way to run the analysis and retrieve the results." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "def run_analysis(analysis):\n", " analysis.run()\n", " return analysis.results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `joblib.delayed` is different from `dask.delayed`; it cannot be used as a \"pie\" syntax (`@joblib.delayed`), so you have to use it as below.\n", "\n", "Similiar to `dask.delayed`, the code inside `joblib.delayed` will not run immediately but be pushed into a job queue waiting for processing. In this case, `run_anlaysis()` is processed by `Parallel` with defined number of workers--`n_jobs`." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "results_ensemble = Parallel(n_jobs=num_cores)(delayed(run_analysis)(analysis)\n", " for analysis in analysis_ensemble)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `results_ensemble` will be a list of `results` that is returned from `run_analysis`. You can further split and process each analysis." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result_1 = np.asarray(results_ensemble[0]).T\n", "\n", "labels = ['all', 'x-axis', 'y-axis', 'z-axis']\n", "for col, label in zip(result_1, labels):\n", " plt.plot(col, label=label)\n", "plt.legend()\n", "plt.ylabel('Radius of gyration (Å)')\n", "plt.xlabel('Frame');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## See Also\n", "\n", "The parallel version of MDAnalysis is still under development. For existing solutions and some implementations of parallel analysis, go to [PMDA](https://www.mdanalysis.org/pmda/). PMDA (Shujie *et al.*, 2019) applies the aforementioned split-apply-combine scheme with Dask. In the future, it may provide a framework that consolidates all the parallelisation schemes described in this tutorial.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] Oliver Beckstein, Elizabeth J. Denning, Juan R. Perilla, and Thomas B. Woolf.\n", "Zipping and Unzipping of Adenylate Kinase: Atomistic Insights into the Ensemble of OpenClosed Transitions.\n", "Journal of Molecular Biology, 394(1):160–176, November 2009.\n", "00107.\n", "URL: https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164, doi:10.1016/j.jmb.2009.09.009.\n", "\n", "[2] Richard J. Gowers, Max Linke, Jonathan Barnoud, Tyler J. E. Reddy, Manuel N. Melo, Sean L. Seyler, Jan Domański, David L. Dotson, Sébastien Buchoux, Ian M. Kenney, and Oliver Beckstein.\n", "MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations.\n", "Proceedings of the 15th Python in Science Conference, pages 98–105, 2016.\n", "00152.\n", "URL: https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html, doi:10.25080/Majora-629e541a-00e.\n", "\n", "[3] Naveen Michaud-Agrawal, Elizabeth J. Denning, Thomas B. Woolf, and Oliver Beckstein.\n", "MDAnalysis: A toolkit for the analysis of molecular dynamics simulations.\n", "Journal of Computational Chemistry, 32(10):2319–2327, July 2011.\n", "00778.\n", "URL: http://doi.wiley.com/10.1002/jcc.21787, doi:10.1002/jcc.21787.\n", "\n", "[4] Max Linke Shujie Fan, Ioannis Paraskevakos, Richard J. Gowers, Michael Gecht, and Oliver Beckstein.\n", "PMDA - Parallel Molecular Dynamics Analysis.\n", "In Chris Calloway, David Lippa, Dillon Niederhut, and David Shupe, editors, Proceedings of the 18th Python in Science Conference, 134 – 142. 2019.\n", "doi:10.25080/Majora-7ddc1dd1-013." ] } ], "metadata": { "kernelspec": { "display_name": "gsoc", "language": "python", "name": "gsoc" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 4 }