An updated set of basic linear algebra subprograms (BLAS)
ACM Transactions on Mathematical Software (TOMS)
Measuring High Performance Computing Productivity
International Journal of High Performance Computing Applications
Scalable Parallel Programming with CUDA
Queue - GPU Computing
Heterogeneous multicore parallel programming for graphics processing units
Scientific Programming - Software Development for Multi-core Computing Systems
Implementing the PGI Accelerator model
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads
IISWC '10 Proceedings of the IEEE International Symposium on Workload Characterization (IISWC'10)
Optimization strategies in different CUDA architectures using llCoMP
Microprocessors & Microsystems
accULL: an OpenACC implementation with CUDA and OpenCL support
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
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During the last few years, the availability of hardware accelerators, such as GPUs, has rapidly increased. However, the entry cost to GPU programming is high and requires a considerable porting and tuning effort. Some research groups and vendors have made attempts to ease the situation by defining APIs and languages that simplify these tasks. In the wake of the success of OpenMP, industria and academia are working toward defining a new standard of compiler directives to leverage the GPU programming effort. Support from vendors and similarities with the upcoming OpenMP 4.0 standard lead us to believe that OpenACC is a good alternative for developers who want to port existing codes to accelerators. In this paper, we evaluate three OpenACC implementations: two commercial implementations (PGI and CAPS) and our own research implementation, accULL, to evaluate the current status and future directions of the standard.