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Scientists who want to exploit the computing power of the latest parallel architectures are faced with a diverse set of architectures and a number of programming languages, models and approaches. Among several such programming techniques are directive-based programming models, OpenMP and OpenACC. This paper explores the similarities and the functionality gaps between both models and presents insights...
As the HPC and Big Data communities continue to converge, heterogeneous and distributed systems are becoming commonplace. In order to take advantage of the immense computing power of these systems, distributing data efficiently and leveraging specialized hardware (e.g. accelerators) is critical. MapReduce is a popular paradigm that provides automatic data distribution to the programmer. CUDA and OpenCL...
Accelerators offer the potential to significantly improve the performance of scientific applications when offloading compute intensive portions of programs to the accelerators. However, effectively tapping their full potential is difficult owing to the programmability challenges faced by the users when mapping computation algorithms to the massively parallel architectures such as GPUs.Directive-based...
Directive-based programming models provide high-level of abstraction thus hiding complex low-level details of the underlying hardware from the programmer. One such model is OpenACC that is also a portable programming model allowing programmers to write applications that offload portions of work from a host CPU to an attached accelerator (GPU or a similar device). The model is gaining popularity and...
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