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Titlebook: OpenMP: Advanced Task-Based, Device and Compiler Programming; 19th International W Simon McIntosh-Smith,Michael Klemm,Jannis Klinkenb Confe

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樓主: gingerly
51#
發(fā)表于 2025-3-30 09:27:40 | 只看該作者
52#
發(fā)表于 2025-3-30 12:50:30 | 只看該作者
OpenMP Advisor: A Compiler Tool for?Heterogeneous Architecturesough challenge. In this paper, we present OpenMP Advisor, a novel compiler tool that enables code offloading to a GPU with OpenMP using Machine Learning. Although the tool is currently limited to GPUs, it can be extended to support other OpenMP-capable devices. The tool has two modes: Training and P
53#
發(fā)表于 2025-3-30 17:00:34 | 只看該作者
54#
發(fā)表于 2025-3-30 22:12:17 | 只看該作者
Suspending OpenMP Tasks on?Asynchronous Events: Extending the?Taskwait Constructities. As a shared-memory parallel programming model, OpenMP has the responsibility of orchestrating the suspension and progression of asynchronous operations occurring on a compute node, such as MPI communications or CUDA/HIP streams. Yet, specifications only come with the . API to suspend tasks un
55#
發(fā)表于 2025-3-31 01:57:53 | 只看該作者
How to?Efficiently Parallelize Irregular DOACROSS Loops Using Fine Granularity and?OpenMP Tasks: TheS loops) and that are also irregular, that is, the dependencies between iterations vary depending on the context. Many techniques have been studied before to be able to parallelize this type of loops, however in OpenMP standard there is no efficient way to parallelize them. From the literature, it i
56#
發(fā)表于 2025-3-31 06:29:13 | 只看該作者
The Kokkos OpenMPTarget Backend: Implementation and?Lessons Learnedkkos provides a programming model designed for performance portability, which allows developers to write a single source implementation that can run efficiently on various architectures. At its heart, Kokkos maps parallel algorithms to architecture and vendor specific backends written in lower level
57#
發(fā)表于 2025-3-31 11:16:37 | 只看該作者
OpenMP Target Offload Utilizing GPU Shared Memoryl processing units, GPUs, yet this is not something trivially done using current OpenMP target offloading. In this paper, we examine methods for implementing parallel programs running on GPUs, which rely on locally shared memory resources and intricate synchronization. Employing the methods, we show
58#
發(fā)表于 2025-3-31 16:53:41 | 只看該作者
Improving a?Multigrid Poisson Solver with?Peer-to-Peer Communication and?Task Dependenciesequations. The logarithmically decaying resolution of the grids in the multigrid hierarchy poses a challenge to achieving high parallel efficiency on highly heterogeneous systems. At the same time, supercomputers have become increasingly heterogeneous with the advent of general-purpose graphics proc
59#
發(fā)表于 2025-3-31 18:43:55 | 只看該作者
Multipurpose Cacheing to?Accelerate OpenMP Target Regions on?FPGAsity to exploit their potential. As a remedy, our OpenMP-to-FPGA compiler fully automatically inserts optimized multipurpose cache blocks into the generated FPGA hardware. We exploit characteristics of OpenMP target regions to both avoid costly bus snooping hardware and to achieve cache consistency.
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