The Partitioned Global Address Space (PGAS) model, increases programmer productivity by presenting a flat memory space with locality awareness. However, the abstract representation of memory incurs overheads especially when global data is accessed. As a PGAS programming language, Chapel provides language structures to alleviate such overheads. In this work, we examine such optimizations on a set of benchmarks using multiple locales and analyze their impact on programmer productivity quantitatively. The optimization methods that we study achieved improvements over non-optimized versions ranging from 1.1 to 68.1 times depending on the benchmark characteristic.
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