Remote OpenMP offloading

Patel, Atmn; Doerfert, Johannes

doi:10.1145/3503221.3508416

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…A low-overhead implementation of Remote OpenMP Offloading is publicly available in the LLVM/OpenMP compiler infrastructure [6]. The work includes detailed studies on microbenchmarks, as well as scaling results for two HPC proxy applications.…”

Section: Related Workmentioning

confidence: 99%

DPU Offloading Programming with the OpenMP API

Usman,

Iserte,

Ferrer

et al. 2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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Supercomputing Center (BSC), Spain Data processing units (DPUs) as network co-processors are an emerging trend in our community, with plenty of opportunities yet to be explored. These have been generally used as domain-specific accelerators transparent to application developers; In the HPC field, DPUs have been used as MPI accelerators, but also to offload some tasks from the general-purpose processor. However, the latter required application developers to deploy MPI ranks in the DPUs, as if they were remote (weak) compute nodes, hence considerably hindering programmability. The wide adoption of OpenMP as the threading model in the HPC arena, along with that of GPU accelerators, is making OpenMP offloading to GPUs a wide trend for HPC applications. In this paper we introduce, for the first time in the literature, OpenMP offloading support for network co-processor DPUs. We present our design in LLVM to support OpenMP standard offloading semantics and discuss the programming productivity advantages with respect to the existing MPI-based programming model. We also provide the corresponding performance analysis demonstrating competitive results in comparison with the MPI baseline.CCS Concepts: • Computing methodologies → Distributed computing methodologies.

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Section: Related Workmentioning

confidence: 99%

DPU Offloading Programming with the OpenMP API

Usman,

Iserte,

Ferrer

et al. 2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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“…Although some interesting results have been demonstrated, the approach also presented some scalability limitations and lacked support for task parallelism [35]. Similarly, Patel et al proposed an extension to the OpenMP accelerator model in LLVM to program remote GPU clusters using RPC on top of UCX [29]. However, their approach requires programmers to manually map the tasks to the GPUs and manually manage the data making it difficult to scale.…”

Section: Related Workmentioning

confidence: 99%

The OpenMP Cluster Programming Model

Yviquel,

Pereira,

Francesquini

et al. 2022

Preprint

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Despite the various research initiatives and proposed programming models, efficient solutions for parallel programming in HPC clusters still rely on a complex combination of different programming models (e.g., OpenMP and MPI), languages (e.g., C++ and CUDA), and specialized runtimes (e.g., Charm++ and Legion). On the other hand, task parallelism has shown to be an efficient and seamless programming model for clusters. This paper introduces OpenMP Cluster (OMPC), a task-parallel model that extends OpenMP for cluster programming. OMPC leverages OpenMP's offloading standard to distribute annotated regions of code across the nodes of a distributed system. To achieve that it hides MPI-based data distribution and load-balancing mechanisms behind OpenMP task dependencies. Given its compliance with OpenMP, OMPC allows applications to use the same programming model to exploit intra-and internode parallelism, thus simplifying the development process and maintenance. We evaluated OMPC using Task Bench, a synthetic benchmark focused on task parallelism, comparing its performance against other distributed runtimes. Experimental results show that OMPC can deliver up to 1.53x and 2.43x better performance than Charm++ on CCR and scalability experiments, respectively. Experiments also show that OMPC performance weakly scales for both Task Bench and a real-world seismic imaging application.

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