Problem domains are commonly decomposed hierarchically to fully utilize parallel resources in modern microprocessors. Such decompositions can be provided as library routines, written by experienced experts, for general algorithmic patterns. But such APIs tend to be constrained to certain architectures or data sizes. Integrating them with application code is often an unnecessarily daunting task, especially when these routines need to be closely coupled with user code to achieve better

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... . This paper contributes HiDP, a hierarchical data parallel language. The purpose of HiDP is to improve the coding productivity of integrating hierarchical data parallelism without significant loss of performance. HiDP is a sourceto-source compiler that converts a very concise data parallel language into CUDA C++ source code. Internally, it performs necessary analysis to compose user code with efficient and architecture-aware code snippets. This paper discusses various aspects of HiDP systematically: the language, the compiler and the run-time system with built-in tuning capabilities. They enable HiDP users to express algorithms in less code than low-level SDKs require for native platforms. HiDP also exposes abundant computing resources of modern parallel architectures. Improved coding productivity tends to come with a sacrifice in performance. Yet, experimental results show that the generated code delivers performance very close to handcrafted native GPU code.