Mapreduce does not seem to use all available cores

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I am using mapreduce on a machine with 16 cores. I make a pool with 15 workers (cores) which works fine. When I run mapreduce though, it only utilizes one or two workers: sometimes one for the mapper and one for the reducer. This is how I check which worker is processing the data (in addition to using a system monitor to watch CPU/core activities):
There are tens of files to be processed and each mapper is called with one file to process. Each time a mapper is called it loads and processes one file. I expect that during the first call to the mapper and while it is loading and processing the first file on one worker (core), there are other parallel calls to mapper to process the next files on other workers. However, this is not how it happens; it just sequentially calls the mapper on the same worker. Sometimes it uses a second worker for the reducer calls. So at most it uses two workers, while there are 15 available in the pool.
What would be a simple code to check if mapreduce is making use of all the available cores?
EDIT: Actually now I can confirm that the mapper is always run by a single worker, but the reducer may be run by a few different workers, as expected.
Your help is appreciated, Mehrdad
Mehrdad Oveisi
Mehrdad Oveisi on 19 Nov 2014
Thank you Rick! I found your reply here useful. So I thought it's good to have a separate thread for this tip.

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Accepted Answer

Rick Amos
Rick Amos on 24 Nov 2014
In R2014b, there are some limitations with the minimum size of data that can be parallelized. To avoid this limitation, the input datastore must contain at least one of the following:
  1. Multiple files, where each file will be handled in parallel.
  2. Files that are larger than 32 MB, where each 32 MB will be handled in parallel.
If the input datastore contains a single small file, you will need to find a way to split that file into multiple files. For example, if the input datastore contains a single file listing many filenames (to the actual data), you can split this up into many files each containing a single or small number of filenames to ensure parallelism.

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