Monte Carlo Simulations of Ionised Nebulae

Mocassin is the Monte Carlo Simulations of Ionised Nebulae. MOCASSIN is a fully 3D or 2D photoionisation and dust radiative transfer code which employs a Monte Carlo approach to the transfer of radiation through media of arbitrary geometry and density distribution.

To run this test with the Phoronix Test Suite, the basic command is: phoronix-test-suite benchmark mocassin.

Test Created

18 September 2020

Last Updated

18 September 2020

Test Maintainer

Michael Larabel 

Test Type

Processor

Average Install Time

19 Seconds

Average Run Time

15 Minutes, 9 Seconds

Test Dependencies

Fortran + OpenMPI + C/C++ Compiler Toolchain

Supported Platforms


Public Result UploadsReported Installs*Test Completions*OpenBenchmarking.orgEventsMonte Carlo Simulations of Ionised Nebulae Popularity Statisticspts/mocassin2020.092020.102020.112020.122021.012021.022021.0380160240320400
* Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting while running benchmarks from an Internet-connected platform.
Data current as of Mon, 08 Mar 2021 21:14:51 GMT.

Revision History

pts/mocassin-1.0.0   [View Source]   Fri, 18 Sep 2020 17:47:28 GMT
Initial commit of Mocassin - Monte Carlo Simulations of Ionised Nebulae

Suites Using This Test

Scientific Computing

MPI Benchmarks

HPC - High Performance Computing


Performance Metrics

Analyze Test Configuration:

Monte Carlo Simulations of Ionised Nebulae 2019-03-24

Input: Dust 2D tau100.0

OpenBenchmarking.org metrics for this test profile configuration based on 488 public results since 18 September 2020 with the latest data as of 28 February 2021.

Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.

Component
Percentile Rank
# Matching Public Results
Seconds (Average)
99th
9
167 +/- 2
88th
4
191 +/- 1
87th
11
192 +/- 3
84th
8
195 +/- 1
84th
4
195 +/- 7
76th
4
206 +/- 2
Mid-Tier
75th
> 206
74th
3
208 +/- 1
72nd
6
212 +/- 2
67th
7
213 +/- 1
67th
5
213 +/- 1
61st
7
217 +/- 1
57th
7
222 +/- 1
55th
11
227 +/- 7
Median
50th
234
48th
7
237 +/- 1
47th
7
238 +/- 1
44th
3
241 +/- 1
42nd
7
244 +/- 1
38th
5
251 +/- 1
37th
3
252 +/- 1
34th
3
259 +/- 1
31st
4
265 +/- 1
30th
3
271 +/- 1
27th
3
276 +/- 1
Low-Tier
25th
> 276
24th
3
279 +/- 12
23rd
3
283 +/- 1
23rd
3
283 +/- 1
20th
3
293 +/- 1
18th
3
299 +/- 2
17th
3
302 +/- 1
16th
4
304 +/- 18
16th
3
304 +/- 1
15th
3
313 +/- 34
14th
3
322 +/- 5
12th
3
331 +/- 2
11th
3
341 +/- 1
10th
5
348 +/- 8
8th
3
377 +/- 4
7th
4
381 +/- 52
7th
3
388 +/- 57
5th
3
415 +/- 1
4th
3
428 +/- 1
3rd
3
530 +/- 1
2nd
3
568 +/- 2
1st
3
710 +/- 2
OpenBenchmarking.orgDistribution Of Public Results - Input: Dust 2D tau100.0488 Results Range From 106 To 725 Seconds106131156181206231256281306331356381406431456481506531556581606631656681706731306090120150

Based on OpenBenchmarking.org data, the selected test / test configuration (Monte Carlo Simulations of Ionised Nebulae 2019-03-24 - Input: Dust 2D tau100.0) has an average run-time of 13 minutes. By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.

OpenBenchmarking.orgMinutesTime Required To Complete BenchmarkInput: Dust 2D tau100.0Run-Time612182430Min: 5 / Avg: 12.54 / Max: 27

Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.1%.

OpenBenchmarking.orgPercent, Fewer Is BetterAverage Deviation Between RunsInput: Dust 2D tau100.0Deviation246810Min: 0 / Avg: 0.11 / Max: 1

Does It Scale Well With Increasing Cores?

No, based on the automated analysis of the collected public benchmark data, this test / test settings does not generally scale well with increasing CPU core counts. Data based on publicly available results for this test / test settings, separated by vendor, result divided by the reference CPU clock speed, grouped by matching physical CPU core count, and normalized against the smallest core count tested from each vendor for each CPU having a sufficient number of test samples and statistically significant data.

AMDIntelOpenBenchmarking.orgRelative Core Scaling To BaseMonte Carlo Simulations of Ionised Nebulae CPU Core ScalingInput: Dust 2D tau100.046812162432640.79381.58762.38143.17523.969

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