Hi everyone,
I’ve been working on a food delivery platform for over 1 year and we have some traveling salesman problems in the background. But this problem is a little different one the original problem, we add some time constraints to routing. So for this type of problem we need to make some fast calculations. For this purpose, I’ve researched parallelism and concurrency usage in ruby.
For more detail, you can check this article about the topic https://medium.com/@deepshig/concurrency-vs-parallelism-4a99abe9efb8
This article is about the calculation speed in ruby. I want to show you, how we can increase the speed of calculation with small changes. The following scripts have some different procs for calculation. I’ll use these procs below examples;
In the above runner.rb file I’ve to give you example scripts for testing your calculation methods. concurrent_with_sleep_proc and calculate_with_sleep_proc have sleep in calculation. You can put there some io operation or some long-running calculations. As you see on that file I did not run any calculate_with_sleep_proc with parallel like the following;
parallel_proc.call(arg_array, 24, calculate_with_sleep_proc)
Because it takes time to calculate 10000 items to calculate in 24 processes with waiting 3 seconds.
When we use parallelism and concurrency together, it can be really fast as you see in the following. In this example, I’ve used 10000 items with sleep in the calculation. The following results come from with sleep part of the runner.rb file.
With sleep;
user system total real
p&c10 : 0.004987 0.009114 8.745606 ( 4.926278)
p&c11 : 0.007684 0.015370 9.212766 ( 5.166559)
p&c12 : 0.005729 0.012166 8.855250 ( 5.235328)
p&c13 : 0.007170 0.015991 8.578989 ( 5.590199)
p&c14 : 0.006369 0.014589 7.640638 ( 5.035913)
p&c15 : 0.005920 0.013606 7.590901 ( 4.699597)
p&c16 : 0.006150 0.013080 7.686084 ( 5.187887)
p&c17 : 0.006169 0.013702 7.179024 ( 4.563827)
p&c18 : 0.008542 0.015548 7.505202 ( 4.850113)
p&c19 : 0.007834 0.019239 7.105738 ( 4.515940)
p&c20 : 0.007496 0.017252 6.859975 ( 4.523301)
p&c21 : 0.010529 0.019268 7.220272 ( 4.781848)
p&c22 : 0.011064 0.024596 6.510430 ( 4.539044)
p&c23 : 0.011928 0.020285 6.786807 ( 4.405131)
p&c24 : 0.008574 0.020050 6.672230 ( 4.375151)
p&c25 : 0.009868 0.021247 6.598457 ( 4.338372)
p&c26 : 0.011396 0.021693 6.415795 ( 4.252782)
c : 3.877854 1.743947 5.621801 ( 16.474863)
If you don’t have any long waiting like sleep in your calculation, pure parallelism can be faster; Following results come from without sleep part of the runner.rb file.
Without sleep;
user system total real
p&c10 : 0.004269 0.007311 3.289474 ( 0.901183)
p&c11 : 0.004811 0.009777 3.833186 ( 1.015616)
p&c12 : 0.004785 0.009611 3.402832 ( 0.900057)
p&c13 : 0.006303 0.010684 3.168834 ( 0.872175)
p&c14 : 0.007362 0.011854 3.595352 ( 0.953701)
p&c15 : 0.006461 0.011326 3.381846 ( 0.906724)
p&c16 : 0.007204 0.011498 3.400029 ( 1.056723)
p&c17 : 0.012351 0.022792 3.645585 ( 1.418485)
p&c18 : 0.010182 0.023389 3.846147 ( 1.331361)
p&c19 : 0.008289 0.017057 3.363501 ( 0.936982)
p&c20 : 0.008949 0.015332 3.747166 ( 1.217274)
p&c21 : 0.009793 0.016687 3.752948 ( 1.262270)
p&c22 : 0.012197 0.024683 3.566022 ( 1.227817)
p&c23 : 0.009727 0.019681 3.953732 ( 1.310838)
p&c24 : 0.008423 0.017569 3.603448 ( 1.135166)
p&c25 : 0.009448 0.020335 4.148266 ( 1.468371)
p&c26 : 0.010681 0.023786 4.596350 ( 1.862336)
p10 : 0.004490 0.009256 0.150146 ( 0.065307)
p11 : 0.004768 0.009506 0.182004 ( 0.080961)
p12 : 0.004725 0.009574 0.177207 ( 0.095620)
p13 : 0.005870 0.013676 0.122861 ( 0.103468)
p14 : 0.004806 0.010406 0.117565 ( 0.042819)
p15 : 0.005112 0.011147 0.129349 ( 0.047156)
p16 : 0.005038 0.010570 0.136523 ( 0.047802)
p17 : 0.005091 0.011161 0.144015 ( 0.054687)
p18 : 0.006997 0.015194 0.179129 ( 0.119613)
p19 : 0.007105 0.017206 0.204005 ( 0.105792)
p20 : 0.007530 0.016820 0.214045 ( 0.102703)
p21 : 0.008028 0.018449 0.271488 ( 0.187290)
p22 : 0.008422 0.019139 0.244256 ( 0.149529)
p23 : 0.007691 0.016469 0.259061 ( 0.121158)
p24 : 0.008392 0.019364 0.267913 ( 0.122444)
p25 : 0.013546 0.023487 0.285710 ( 0.165075)
p26 : 0.009482 0.022572 0.209581 ( 0.083306)
c : 2.107816 1.099271 3.207087 ( 2.729089)
So parallelism and concurrency can be really helpful for some calculations. I’ve also tested some bigger arrays and the results are like the following;
1_000_000 items array without sleep in calculation;
user system total real p&c24 : 0.350523 0.154071 311.822112 ( 103.817200) p&c100 : 0.362228 0.252416 291.592356 ( 78.864338) c : 1827.912725 140.046733 1967.959458 (2047.510425) p100 : 0.381085 0.134941 3.518979 ( 1.227611)
10_000_000 items array without sleep in calculation;
user system total real p24 : 25.753854 0.810221 43.390889 ( 28.204278) p100 : 14.243962 0.682502 98.411622 ( 30.706949)
For large arrays, parallelism is best for calculation. According to your problem, you can choose concurrency, parallelism, or both of them for calculation.
For bigger arrays 100_000_000 I’ve tried to split arrays into small parts like 100_000 items for each array element and calculate each group with parallel or concurrent. But for bigger arrays, it takes time to split and merge arrays. So it did not solve a real problem. For 1_000_000_000 elements, it is really a problem to calculate each item or item group with limited time.
I’ve shared my experiences with calculation speed for large arrays in ruby.
Happy coding…
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