title: Utilize multi-core with LocalMRJobRunner for MRJob
body: <p>I am using the python yelp/mrjob framework for my mapreduce jobs. There are only about 4G of data and I don't want to go through the trouble of setting up Hadoop or EMR. I have a 64 core machine and it takes about 2 hours to process the data with mrjob. I notice mrjob assigns 54 mappers for my job, but it seems to run only one at a time. Is there a way to make mrjob run all tasks in parallel with all my cpu cores?</p>
  
  <p>I manually changed number of tasks but didn't help much.
  <code>--jobconf mapred.map.tasks=10 --jobconf mapred.reduce.tasks=10</code></p>
  
  <p>EDIT:
  I have <code>-r local</code> when I execute the job, however, looking at the <a href="https://github.com/Yelp/mrjob/blob/master/mrjob/local.py#L33" rel="nofollow">code</a>, it seems it defaults to run one process at a time. Please tell me I am wrong.</p>