Performance Test

Introduction

This page summarises some results for the performance test of YADE (yade --performance) on multicore machines. It should give an idea on how good YADE scales.

Test 1

Two versions of YADE are compared to each other and two different machines are used. The test was conducted on the computing grid of the University of Newcastle by Klaus.

YADE versions:

version1 (trunk): 2014-01-25.git-22c2441
version2 (see https://lists.launchpad.net/yade-dev/msg10498.html): 2014-02-24.git-b60d388

Machines:

AMD: AMD Opteron(tm) Processor 6282 SE (64 cores)
Intel: Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz (16 cores)

System:

Red Hat Enterprise Linux Server release 6.4 (Santiago)
gcc version 4.4.7 20120313 (Red Hat 4.4.7-3) (GCC)

Other:

Number of tests per data point in plots: 10

Performance of Parallel Collider

Fig. 1.1: Scaling on Intel machine (left) and AMD machine (right.)

Fig. 1.1 shows how version2 (parallel collider) scales in relation to version1 (NOTE: version1/version2 indicates how much faster the parallel collider is). It is interesting to note that for simulations with less than 100000 particles the scaling is almost not depending on the number of threads and scaling is slightly bigger than one only. For simulations with more than 100000 particles things are looking differently.

Using the total number of cores on the machine is not recommended, e.g. -j12 (and probably -j14) scales better than -j16 on the Intel and -j24 scales better than -j32 on the AMD.

Fig. 1.2: Number of particle vs. ierations per seconds for Intel

Fig. 1.3:Number of particle vs. ierations per seconds for AMD

Figs. 1.2-1.3 show the absolute speed in iterations per seconds for both versions on both machines. It can clearly be seen that the lines for the parallel collider on the right are almost shifted parallel whereas the lines for version1 converge at the 500000 particle point. This means that the parallel collider does not just allow for better scaling (i.e. faster calculation) but as well for more particle to be used in a simulation.

Sample output from Timing

Sample output for timing for -j12 on Intel

version1:

version2 (parallel collider):

Comparison AMD/Intel

Fig. 1.4: Comparison of Intel vs. AMD for both versions

Fig. 1.4 shows the difference between running version1 and version2 on an Intel or AMD machine. The AMD is generally slower (Intel/AMD>1).

Conclusions

The new parallel collider scales good for the --performance test with more than 100000 particles. The scaling for 500000 particles is really good, i.e. -j12 scales by a factor of 6 for both machines. Intel machines perform better (similar observations have been made here [1]). Finally, I would say that there is an optimum number of threads you should use per simulation. Many cores doesn't always mean much faster. So use your resources wisely.

Finally, it should be mentioned that the results for 500000 particles are extrapolated from 10 iterations since this is the value in the current script. This is far too optimistic. I will post more realistic results soon.

Test 2

Same as Test 1 but with more iterations (1x, 3x and 12x the number of iterations specified in checkPerf.py) and more particles (up to 10^6). However, 3 simulations per data point only.