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Big data analytics requires high programmer productivity and high performancesimultaneously on large-scale clusters. However, current big data analyticsframeworks (e.g. Apache Spark) have prohibitive runtime overheads since theyare library-based. We introduce a novel auto-parallelizing compiler approachthat exploits the characteristics of the data analytics domain such as themap/reduce parallel pattern and is robust, unlike previous auto-parallelizationmethods. Using this approach, we build High Performance Analytics Toolkit(HPAT), which parallelizes high-level scripting (Julia) programs automatically,generates efficient MPI/C++ code, and provides resiliency. Furthermore, itprovides automatic optimizations for scripting programs, such as fusion ofarray operations. Thus, HPAT is 369x to 2033x faster than Spark on the Corisupercomputer and 20x to 256x times on Amazon AWS.

HPAT: High Performance Analytics with Scripting Ease-of-Use