StreamIt: A Language for Streaming Applications
CC '02 Proceedings of the 11th International Conference on Compiler Construction
Software Pipelined Execution of Stream Programs on GPUs
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
SkePU: a multi-backend skeleton programming library for multi-GPU systems
Proceedings of the fourth international workshop on High-level parallel programming and applications
SkelCL - A Portable Skeleton Library for High-Level GPU Programming
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
Algorithmic skeletons for multi-core, multi-GPU systems and clusters
International Journal of High Performance Computing and Networking
Compiling a high-level language for GPUs: (via language support for architectures and compilers)
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Performance Portability with the Chapel Language
IPDPS '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium
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The Graphics Processing Unit (GPU) is gaining popularity as a co-processor to the Central Processing Unit (CPU). However, harnessing its capabilities is a non-trivial exercise that requires good knowledge of parallel programming, more so when the complexity of these applications is increasingly rising. Languages such as StreamIt [1] and Lime [2] have addressed the offloading of composed computations to GPUs. However, to the best of our knowledge, no support exists at library level. To this extent, we propose Marrow, an algorithmic skeleton framework for the orchestration of OpenCL computations. Marrow expands the set of skeletons currently available for GPU computing, and enables their combination, through nesting, into complex structures. Moreover, it introduces optimizations that overlap communication and computation, thus conjoining programming simplicity with performance gains in many application scenarios. We evaluated the framework from a performance perspective, comparing it against hand-tuned OpenCL programs. The results are favourable, indicating that Marrow's skeletons are both flexible and efficient in the context of GPU computing.