UPC performance and potential: a NPB experimental study
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
OpenMP to GPGPU: a compiler framework for automatic translation and optimization
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
Message passing on data-parallel architectures
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing
CLUSTER '10 Proceedings of the 2010 IEEE International Conference on Cluster Computing
Unified parallel C for GPU clusters: language extensions and compiler implementation
LCPC'10 Proceedings of the 23rd international conference on Languages and compilers for parallel computing
Scaling scientific applications on clusters of hybrid multicore/GPU nodes
Proceedings of the 8th ACM International Conference on Computing Frontiers
Hybrid PGAS runtime support for multicore nodes
Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model
Towards efficient GPU sharing on multicore processors
Proceedings of the second international workshop on Performance modeling, benchmarking and simulation of high performance computing systems
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Scalable systems employing a mix of GPUs with CPUs are becoming increasingly prevalent in high-performance computing. The presence of such accelerators introduces significant challenges and complexities to both language developers and end users. This paper provides a close study of efficient coordination mechanisms to handle parallel requests from multiple hosts of control to a GPU under hybrid programming. Using a set of microbenchmarks and applications on a GPU cluster, we show that thread and process-based context hosting have different tradeoffs. Experimental results on application benchmarks suggest that both thread-based context funneling and process-based context switching natively perform similarly on the latest Fermi GPUs, while manually guided context funneling is currently the best way to achieve optimal performance.