Starting and executing code in parallel is slower than expected
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I installed MATLAB 2016a on three systems with different hard- and software and would like to use the Parallel Computing Toolbox. On one system I observed inexplicably low performance. Now I'm looking for hints for possible causes and would be very grateful for hints. The systems are as follows:
- System 1: CentOS 7, 24/24 cores/threads, 256GB RAM
- System 2: Ubuntu 16.04, 16/32 cores/threads, 64GB RAM
- System 3: Ubuntu 16.04, 12/24 cores/threads, 96GB RAM
On System 1 and and System 2, the initialisation and parallel execution of parallel code runs as expected. For instance, initialization of the parallel pool
parpool('local',6);
takes about 15 seconds and submitting parallel jobs such as
pev = [];
for i=1:50
pev(i) = parfeval(@(A), ...); % code
end
takes less than a second and during the asynchronous computation the N cores are well occupied. However, on System 3 (that has the same Ubuntu Version as System 2) submitting the parallel jobs takes almost 3 minutes and execution of the jobs consumes much more RAM than on the other systems. To exclude performance issues related to copying of complicated environments (e.g. file handles, anonymous functions, etc.), I created the pool right after launching Matlab and avoided any inclusion of additional paths (i.e. path contained only the Matlab Toolboxes). The call
parpool('local',6);
took 150 seconds. The server was otherwise idle.
How can I approach this problem?
2 Kommentare
Edric Ellis
am 30 Okt. 2017
Hm, is there anything different about where MATLAB is installed on System 3? Usually, local pool creation time with only 6 workers is dominated by the time taken to launch the worker MATLAB processes. What happens if you try and launch 6 copies of MATLAB simultaneously on System 3?
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