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Research has proved that machine learning and iterative compilation techniques can be profitable when applied to compilers to improve the optimizations they perform on programs. Unfortunately, these techniques are hampered by the long training times they require. This paper shows that parallel execution of multiple training runs can be naturally mapped on the MapReduce programming model and is effective in reducing execution times for iterative compilation. The presented technique allows parallel execution on multiple identical worker nodes or on a single machine by splitting its resources. Experimental results show that an almost-linear speedup can be obtained.