Multi-GPU Support on Single Node Using Directive-Based Programming Model

Xu, Rengan; Tian, Xiaonan; Chandrasekaran, Sunita; Chapman, Barbara

doi:10.1155/2015/621730

Cited by 13 publications

(12 citation statements)

References 20 publications

(47 reference statements)

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“…The GeoDock algorithm implementation is similar to the one described in Section 4.1. In particular, the data replication (lines 12-15) and the alignment phase (lines [16][17][18][19][20][21] are almost the same. The only difference is in the data structure implementation, due to the limited support of C++ standard libraries from OpenACC.…”

Section: Hybrid Openmp/openacc Implementationmentioning

confidence: 84%

Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes

et al. 2019

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In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used to perform screening of a large library of molecules, in the early phase of the process. Given the amount of candidate molecules and the complexity of the application, this task is usually performed using High-Performance Computing (HPC) platforms. In modern HPC systems, heterogeneous platforms provide a better throughput with respect to homogeneous platforms.In this work, we ported and optimized a molecular docking application to a heterogeneous system, with one or more GPU accelerators, leveraging a hybrid OpenMP and OpenACC approach. We prove that our approach has a better exploitation of the node compared to pure CPU/GPU data splitting approaches, reaching a throughput improvement up to 36% while considering the same computing node.

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Section: Hybrid Openmp/openacc Implementationmentioning

confidence: 84%

Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes

et al. 2019

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“…Xu et al [14] present new directives to extend OpenACC to support multiple accelerators. The proposal is based on an evaluation using the hybrid model of OpenACC and OpenMP.…”

Section: Related Workmentioning

confidence: 99%

MACC: An OpenACC Transpiler for Automatic Multi-GPU Use

Matsumura

Sato

Boku

et al. 2018

Supercomputing Frontiers

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Abstract. Graphics Processing Units (GPUs) perform the majority of computations in state-of-the-art supercomputers. Programming these GPUs is often assisted using a programming model such as (amongst others) the directive-driven OpenACC. Unfortunately, OpenACC (and other similar models) are incapable of automatically targeting and distributing work across several GPUs, which decreases productivity and forces needless manual labor upon programmers. We propose a method that enables OpenACC applications to target multi-GPU. Workload distribution, data transfer and inter-GPU communication (including modern GPU-to-GPU links) are automatically and transparently handled by our compiler with no user intervention and no changes to the program code. Our method leverages existing OpenMP and OpenACC backends, ensuring easy integration into existing HPC infrastructure. Empirically we quantify performance gains and losses in our data coherence method compared to similar approaches, and also show that our approach can compete with the performance of hand-written MPI code.

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“…27,28 However, as discussed in Xu et al, 28 the lack of certain directives often makes the exploitation of multiple accelerators under this paradigm challenging for programmers. 27,28 However, as discussed in Xu et al, 28 the lack of certain directives often makes the exploitation of multiple accelerators under this paradigm challenging for programmers.…”

Section: Related Workmentioning

confidence: 99%

Facilitating the development of stencil applications using the Heterogeneous Programming Library

Viñas

Fraguela

Andrade

et al. 2017

Concurrency and Computation

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Stencil computations are very common in scientific codes. Heterogeneous systems achieve good results solving these problems, but their programming is complex because of the ghost regions required in multi-device implementations and the difficulty to properly exploit their hardware.The Heterogeneous Programming Library (HPL) is a recent framework that improves the programmability of heterogeneous devices. This paper describes two extensions of HPL focused on stencil computations. The first one allows to automatically update the ghost regions they involve.The second one automates the implementation of the computational kernels of these algorithms.In our evaluation, the first mechanism reduces on average the number of lines of code and the Halstead programming effort of the host code of comparable HPL baselines by 34% and 64.2%, respectively, while the second contribution reduces these metrics by 72% and 79% in the computational kernels, respectively. Also, the first technique has negligible performance overheads, while the second one matches the performance of manually developed kernels. As an added benefit, the facilitation of the development of these codes thanks to these techniques helps programmers experiment with optimizations suited for this applications such as the ghost cell expansion technique, which provides speedups of up to 13% in our experiments.

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Multi-GPU Support on Single Node Using Directive-Based Programming Model

Cited by 13 publications

References 20 publications

Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes

Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes

MACC: An OpenACC Transpiler for Automatic Multi-GPU Use

Facilitating the development of stencil applications using the Heterogeneous Programming Library

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