{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import util\n", "import os\n", "from IPython.core.display import Markdown\n", "benchmark = 'depletion'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/markdown": [ "# depletion benchmark\n", "\n", "The depletion benchmark runs a system of $1000$ cuboctahedra, with depletants at a size ratio $q=0.25$ and a reservoir density of $\\phi_{dep}^r=0.80$.\n", "\n", "Under these conditions, the cuboctahedra forms a dense sheared BCC crystal. The depletion method was described in:\n", "[Glaser, J et al. A parallel algorithm for implicit depletant simulations. Journal of Chemical Physics, 2015.](http://scitation.aip.org/content/aip/journal/jcp/143/18/10.1063/1.4935175)\n", "The cuboctahedra with depletion system was studied in the research article:\n", "[Karas AS et al. Using depletion to control colloidal crystal assemblies of hard cuboctahedra. Soft Matter, 2015](http://pubs.rsc.org/en/content/articlelanding/2016/sm/c6sm00620e)\n", "\n", "\n", "\n", "Parameters:\n", "\n", "* $N = 1000$\n", "* Hard particle Monte Carlo\n", " * Polyhedron Vertices: [[-0.53139075, -0.53139075, 0], [-0.53139075, 0.53139075, 0], [0.53139075, -0.53139075, 0], [0.53139075, 0.53139075, 0], [0, -0.53139075, -0.531390750], [0, -0.53139075, 0.53139075], [0, 0.53139075, -0.53139075], [0, 0.53139075, 0.53139075], [-0.53139075, 0, -0.53139075], [-0.53139075, 0, 0.53139075], [0.53139075, 0, -0.53139075], [0.53139075, 0, 0.53139075]]\n", " * Polyhedron sweep radius: 0\n", " * Depletant vertices: []\n", " * Depletant sweep radius: $0.7515*0.25 = 0.1879$\n", " * $d = 0.0351 $\n", " * $a = 0.0544 $\n", " * implicit = True\n", " * $nR = 28.8 $\n", " * ntrial = 0\n", "* Log file period: 10000 time steps\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Markdown(open(os.path.join(benchmark, 'README.md'), 'r').read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performance data\n", "\n", "Performance results are reported in hours to complete ten million Monte Carlo sweeps, where one sweep is N trial moves." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/markdown": [ "| Date | System | Compiler | CUDA | HOOMD | Precision | N | CPU | GPU | Ranks | Time for 10e6 steps (hours)|\n", "|------|--------|----------|------|-------|-----------|---|-----|-----|-------|---------------:|\n", "| 2018/01/15 | comet | gcc 4.9.2 | 8.0 | 2.2.2 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz | **Tesla P100-PCIE-16GB** | 1 | 19.92 |\n", "| 2016/10/23 | psg | gcc 4.8.5 | 8.0 | 2.1.1 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla P100-PCIE-16GB** | 1 | 13.44 |\n", "| 2016/10/23 | psg | gcc 4.8.5 | 8.0 | 2.1.1 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla K80** | 1 | 27.39 |\n", "| 2016/10/23 | psg | gcc 4.8.5 | 8.0 | 2.1.1 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla M40 24GB** | 1 | 28.61 |\n", "| 2016/10/23 | psg | gcc 4.8.5 | 8.0 | 2.1.1 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla K40m** | 1 | 39.16 |\n", "| 2016/10/12 | psg | gcc 4.8.5 | 8.0 | 2.1.0 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla P100-PCIE-16GB** | 1 | 13.77 |\n", "| 2016/10/13 | psg | gcc 4.8.5 | 7.5 | 2.1.0 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla M40 24GB** | 1 | 27.69 |\n", "| 2016/10/12 | psg | gcc 4.8.5 | 8.0 | 2.1.0 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla K80** | 1 | 29.23 |\n", "| 2016/10/13 | psg | gcc 4.8.5 | 7.5 | 2.1.0 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz | **Tesla K40m** | 1 | 39.32 |\n", "| 2016/09/13 | collins | gcc 4.8.5 | 7.5 | 2.0.3 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80GHz | **TITAN X** | 1 | 15.95 |\n", "| 2016/09/13 | collins | gcc 4.8.5 | 7.5 | 2.0.3 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80GHz | **Quadro M6000** | 1 | 29.01 |\n", "| 2016/09/13 | collins | gcc 4.8.5 | 7.5 | 2.0.3 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80GHz | **Tesla K40c** | 1 | 33.97 |\n", "| 2016/09/13 | collins | gcc 4.8.5 | 7.5 | 2.0.3 | double | 1,000 | Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80GHz | **GeForce GTX 680** | 1 | 37.28 |\n" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rows = util.read_rows(benchmark)\n", "table = util.make_table(rows)\n", "Markdown(table)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "The raw code for this IPython notebook is by default hidden for easier reading.To toggle on/off the raw code, click here." ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "#Hide code blocks\n", "HTML('''\n", "The raw code for this IPython notebook is by default hidden for easier reading.To toggle on/off the raw code, click here.''')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }