Parallel Join Excessively Slow
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Last seen 4 days ago
New York, NY

Within the curatedMetagenomicData package parallelization was used to increase performance. However, after some profiling, it was found that parallelization actually slowed processes down, as compared to similar tasks done in serial. The result is difficult to make sense of and a small example has been constructed here to reproduce the scenario. Any helpful comments would be welcomed.

biocparallel curatedmetagenomicdata parallel dplyr • 1.3k views
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Last seen 3 days ago
United States

Here are several examples that illustrate the cost of parallel evaluation

> library(BiocParallel)
> v = integer(1e8)
> system.time(lapply(1:8, function(i, v) i, v))
   user  system elapsed 
  0.004   0.000   0.001 

Cost of starting up the nodes

> system.time(bplapply(1:8, function(i, v) i))
   user  system elapsed 
  0.148   0.012   0.481 

Cost of transferring data to the workers

> system.time(bplapply(1:8, function(i, v) i, v))
   user  system elapsed 
  0.092   0.476   1.727 

Cost of retrieving data from the workers

> system.time(bplapply(1:8, function(i, v) v, v))
   user  system elapsed 
  0.600   1.704   3.378 

and of course the dominant cost, iteration instead of vectorization

> system.time(1:8)
   user  system elapsed 
      0       0       0 

It seems likely that you've replaced a vectorized calculation with an interation, and are moving large amounts of data to and from the workers.

bpvec() might be a better fit to your needs. And generally, the iteration over n assays implies potentially polynomial scaling, where the first assay is copied in the first iteration, then the first and second assays in the second iteration, then the first, second, and third assays in the third iteration, etc; one would rather develop a more efficient algorithm.



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