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The explosion of Big Data was followed by the proliferation of numerouscomplex parallel software stacks whose aim is to tackle the challenges of datadeluge. A drawback of a such multi-layered hierarchical deployment is theinability to maintain and delegate vital semantic information between layers inthe stack. Software abstractions increase the semantic distance between anapplication and its generated code. However, parallel software frameworkscontain inherent semantic information that general purpose compilers are notdesigned to exploit.  This paper presents a case study demonstrating how the specific semanticinformation of the MapReduce paradigm can be exploited on multicorearchitectures. MR4J has been implemented in Java and evaluated againsthand-optimized C and C++ equivalents. The initial observed results led to thedesign of a semantically aware optimizer that runs automatically withoutrequiring modification to application code.  The optimizer is able to speedup the execution time of MR4J by up to 2.0x.The introduced optimization not only improves the performance of the generatedcode, during the map phase, but also reduces the pressure on the garbagecollector. This demonstrates how semantic information can be harnessed withoutsacrificing sound software engineering practices when using parallel softwareframeworks.

Towards co-designed optimizations in parallel frameworks: A MapReduce case study