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  1. Proceedings of the 8th International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM'17)
  2. Assessing One-to-One Parallelism Levels Mapping for OpenMP Offloading to GPUs
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Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioner Generation on GPUs
TaskInsight: Understanding Task Schedules Effects on Memory and Performance
A high-performance portable abstract interface for explicit SIMD vectorization
PETRAS: Performance, Energy and Thermal Aware Resource Allocation and Scheduling for Heterogeneous Systems
Reduction to Tridiagonal Form for Symmetric Eigenproblems on Asymmetric Multicore Processors
High Performance Detection of Strongly Connected Components in Sparse Graphs on GPUs
Towards Composable GPU Programming: Programming GPUs with Eager Actions and Lazy Views
Assessing One-to-One Parallelism Levels Mapping for OpenMP Offloading to GPUs
A Framework for Developing Parallel Applications with high level Tasks on Heterogeneous Platforms

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Assessing One-to-One Parallelism Levels Mapping for OpenMP Offloading to GPUs

Content Provider ACM Digital Library
Author Khaldi, Dounia Shen, Chen Chapman, Barbara Tian, Xiaonan
Abstract The proliferation of accelerators in modern clusters makes efficient coprocessor programming a key requirement if application codes are to achieve high levels of performance with acceptable energy consumption on such platforms. This has led to considerable effort to provide suitable programming models for these accelerators, especially within the OpenMP community. While OpenMP 4.5 offers a rich set of directives, clauses and runtime calls to fully utilize accelerators, an efficient implementation of OpenMP 4.5 for GPUs remains a non-trivial task, given their multiple levels of thread parallelism. In this paper, we describe a new implementation of the corresponding features of OpenMP 4.5 for GPUs based on a one-to-one mapping of its loop hierarchy parallelism to the GPU thread hierarchy. We assess the impact of this mapping, in particular the use of GPU warps to handle innermost loop execution, on the performance of GPU execution via a set of benchmarks that include a version of the NAS parallel benchmarks specifically developed for this research; we also used the Matrix-Matrix multiplication, Jacobi, Gauss and Laplacian kernels.
Starting Page 68
Ending Page 73
Page Count 6
File Format PDF
ISBN 9781450348836
DOI 10.1145/3026937.3026945
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-02-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Llvm Openmp Openuh Gpus
Content Type Text
Resource Type Article
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