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It has been widely shown that GPGPU architectures offer large performance gains compared to their traditional CPU counterparts for many applications. The downside to these architectures is that the current programming models present numerous challenges to the programmer: lower-level languages, explicit data movement, loss of portability, and challenges in performance optimization. In this paper, the authors present novel methods and compiler transformations that increase productivity by enabling users to easily program GPGPU architectures using the high productivity programming language Chapel. Rather than resorting to different parallel libraries or annotations for a given parallel platform, they leverage a language that has been designed from first principles to address the challenge of programming for parallelism and locality.
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