Declarative Parallel Programming for GPUs

Eric Holk, William Byrd, Nilesh Mahajan, Jeremiah Willcock, Arun Chauhan, Andrew Lumsdaine
School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA
International Conference on Parallel Computing (ParCo), 2011


   title={Declarative Parallel Programming for GPUs},

   author={HOLK, E. and BYRD, W. and MAHAJAN, N. and WILLCOCK, J. and CHAUHAN, A. and LUMSDAINE, A.},



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The recent rise in the popularity of Graphics Processing Units (GPUs) has been fueled by software frameworks, such as NVIDIA’s Compute Unified Device Architecture (CUDA) and Khronos Group’s OpenCL that make GPUs available for general purpose computing. However, CUDA and OpenCL are still lowlevel approaches that require users to handle details about data layout and movement across levels of memory hierarchy. We propose a declarative approach to coordinating computation and data movement between CPU and GPU, through a domain-specific language that we called Harlan. Not only does a declarative language obviate the need for the programmer to write low-level error-prone boilerplate code, by raising the abstraction of specifying GPU computation it also allows the compiler to optimize data movement and overlap between CPU and GPU computation. By focusing on the "what", and not the "how", of data layout, data movement, and computation scheduling, the language eliminates the sources of many programming errors related to correctness and performance.
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