Titlebook: OpenMP in a Modern World: From Multi-device Support to Meta Programming; 18th International W Michael Klemm,Bronis R. de Supinski,Brandon N

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https://doi.org/10.1007/978-3-031-15922-0compiler; computer systems; distributed computer systems; distributed systems; embedded systems; Field Pr

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978-3-031-15921-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Enhancing MPI+OpenMP Task Based Applications for?Heterogeneous Architectures with?GPU Supportprogramming models are a promising way to tackle this challenge. Since OpenMP?4.0 and 4.5, the . directives enable to offload pieces of code to GPUs and to express it as tasks with dependencies. Therefore, heterogeneous machines can be programmed using MPI+OpenMP(task+target) to exhibit a very high

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Towards Efficient Remote OpenMP OffloadingCUDA/etc.), as it has been proven to perform well with various scientific applications. However, application developers prefer to use a single coherent programming model over a hybrid model, as maintainability and portability decrease per additional model. Recent work?[.] has shown that the OpenMP d

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Using Off-the-Shelf Hardware Transactional Memory to?Implement Speculative While in?OpenMProposed . construct and the . clause. With SWh, the speculative tasks are generated by the OpenMP . construct in . loops (from linear algebra or goal finding algorithms) where control dependencies between iterations can be speculated. This paper also presents a detailed analysis of the application o

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Feasibility Studies in?Multi-GPU Target Offloadingte node. While many APIs for GPU programming are vendor-specific, OpenMP offers a portable alternative. Therefore OpenMP target offloading is advantageous in terms of long-term code sustainability. Further, many applications have already been parallelized with OpenMP. Hence the amount of work needed

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