  The explosion of Big Data was followed by the proliferation of numerous
complex parallel software stacks whose aim is to tackle the challenges of data
deluge. A drawback of a such multi-layered hierarchical deployment is the
inability to maintain and delegate vital semantic information between layers in
the stack. Software abstractions increase the semantic distance between an
application and its generated code. However, parallel software frameworks
contain inherent semantic information that general purpose compilers are not
designed to exploit.
  This paper presents a case study demonstrating how the specific semantic
information of the MapReduce paradigm can be exploited on multicore
architectures. MR4J has been implemented in Java and evaluated against
hand-optimized C and C++ equivalents. The initial observed results led to the
design of a semantically aware optimizer that runs automatically without
requiring 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 generated
code, during the map phase, but also reduces the pressure on the garbage
collector. This demonstrates how semantic information can be harnessed without
sacrificing sound software engineering practices when using parallel software
frameworks.
