In [2]:
import util
import os
from IPython.core.display import Markdown
benchmark = 'depletion'
In [3]:
Markdown(open(os.path.join(benchmark, 'README.md'), 'r').read())
Out[3]:

depletion benchmark

The depletion benchmark runs a system of $1000$ cuboctahedra, with depletants at a size ratio $q=0.25$ and a reservoir density of $\phi_{dep}^r=0.80$.

Under these conditions, the cuboctahedra forms a dense sheared BCC crystal. The depletion method was described in: Glaser, J et al. A parallel algorithm for implicit depletant simulations. Journal of Chemical Physics, 2015. The cuboctahedra with depletion system was studied in the research article: Karas AS et al. Using depletion to control colloidal crystal assemblies of hard cuboctahedra. Soft Matter, 2015

Parameters:

  • $N = 1000$
  • Hard particle Monte Carlo
    • 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]]
    • Polyhedron sweep radius: 0
    • Depletant vertices: []
    • Depletant sweep radius: $0.7515*0.25 = 0.1879$
    • $d = 0.0351 $
    • $a = 0.0544 $
    • implicit = True
    • $nR = 28.8 $
    • ntrial = 0
  • Log file period: 10000 time steps

Performance data

Performance results are reported in hours to complete ten million Monte Carlo sweeps, where one sweep is N trial moves.

In [4]:
rows = util.read_rows(benchmark)
table = util.make_table(rows)
Markdown(table)
Out[4]:
Date System Compiler CUDA HOOMD Precision N CPU GPU Ranks Time for 10e6 steps (hours)
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
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
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
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
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
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
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
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
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
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
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
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
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
In [5]:
from IPython.display import HTML

#Hide code blocks
HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
The raw code for this IPython notebook is by default hidden for easier reading.To toggle on/off the raw code, click <a href="javascript:code_toggle()">here</a>.''')
Out[5]:
The raw code for this IPython notebook is by default hidden for easier reading.To toggle on/off the raw code, click here.
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