Accelerator Programming Using Directives

Name: Accelerator Programming Using Directives | 6th International Workshop, WACCPD 2019, Denver, CO, USA, November 18, 2019, Revised Selected Papers
Brand: Springer
Price: 53.49 EUR
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6th International Workshop, WACCPD 2019, Denver, CO, USA, November 18, 2019, Revised Selected Papers

Sandra Wienke Sridutt Bhalachandra(Herausgeber*in)

Springer (Verlag)

Erschienen am 24. Juni 2020

IX, 165 Seiten

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978-3-030-49943-3 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents
Porting Scientific Applications to Heterogeneous Architectures Using Directives
GPU Implementation of a Sophisticated Implicit Low-Order Finite Element Solver with FP21-32-64 Computation Using OpenACC
1 Introduction
2 Baseline Solver on CPU-based Computers
2.1 The Target Problem
2.2 The Solver Algorithm
2.3 Implementation of Solver for CPU Systems
3 GPU Implementation Using OpenACC
3.1 Baseline Implementation
3.2 Introduction of Lower-Precision Data Types
3.3 Miscellaneous Optimizations in the Solver
4 Performance Measurement
4.1 Performance Evaluation of FP21 Computation
4.2 Performance Evaluation of the Entire Solver
5 Conclusions
References
Acceleration in Acoustic Wave Propagation Modelling Using OpenACC/OpenMP and Its Hybrid for the Global Monitoring System
1 Introduction
2 Computing Environment
3 3D-SSFPE
3.1 Overview
3.2 Implementation
3.3 Performance Evaluation and Conclusion
4 Global Acoustic Simulation with FDM
4.1 Overview
4.2 Formulation
4.3 Yin-Yang Grid
4.4 Computational Schemes
4.5 Performance Optimization
4.6 Software Evaluation
5 Conclusion
References
Accelerating the Performance of Modal Aerosol Module of E3SM Using OpenACC
1 Introduction
2 OpenACC Programming Model
2.1 Parallelizing Loops
2.2 Data Transfer
3 Experimental Platforms and Approach
4 MAM Algorithms and Kernels
5 Offloading Computations to GPUs
5.1 Kernel: subgrid_mean_updraft
5.2 Kernel: hetfrz_classnuc_cam_calc
5.3 Kernel: ccncalc
5.4 Kernel: nsubmix
6 MAM Kernel Performance Discussion
6.1 Multi-Process Service (MPS)
6.2 Scaling Results
7 Summary and Conclusion
References
Evaluation of Directive-Based GPU Programming Models on a Block Eigensolver with Consideration of Large Sparse Matrices
1 Introduction
2 Background and Related Work
3 Methodology
3.1 The LOBPCG Algorithm
3.2 Baseline CPU Implementation
3.3 A GPU Implementation of LOBPCG
3.4 Tiling LOBPCG Kernels to Fit in GPU Memory Capacity
3.5 Hardware and Software Environment
3.6 Experiments
4 Results
4.1 Performance of the LOBPCG Solver
4.2 Performance of XTY and SpMM Kernels for Large Matrices
4.3 Performance of Tiled and Unified Memory Versions of SpMM
5 Discussion
6 Conclusions
References
Directive-Based Programming for Math Libraries
Performance of the RI-MP2 Fortran Kernel of GAMESS on GPUs via Directive-Based Offloading with Math Libraries
1 Introduction
2 RI-MP2 Kernel of GAMESS
2.1 RI-MP2 Kernel
2.2 Inputs for the RI-MP2 Kernel from GAMESS
3 Employed Systems
3.1 Summit System at Oak Ridge Leadership Computing Facility
3.2 JLSE System at Argonne Leadership Computing Facility
4 Programming Environments
4.1 Employed Compilers
4.2 Math Libraries
5 Offloading the RI-MP2 Kernel
5.1 The RI-MP2 Kernel with OpenMP Threading
5.2 Offloading the RI-MP2 Kernels to GPUs via OpenMP 4.5 and OpenACC 2.6
5.3 Performance Results
6 Offloading the Restructured RI-MP2 Kernel
6.1 Restructuring the RI-MP2 Kernel for an Optimized Performance on a GPU
6.2 Performance Results of the Restructured RI-MP2 Kernel
7 Performance of the Restructured RI-MP2 Kernel on Multiple GPUs via MPI+OpenMP Offloading
8 Concluding Remarks
References
Performance Portability for Heterogeneous Architectures
Performance Portable Implementation of a Kinetic Plasma Simulation Mini-App
1 Introduction
2 Testbed Description
3 GYSELA Mini-App and Baseline OpenMP Implementation
3.1 Four-Dimensional Vlasov-Poisson System
3.2 Algorithm
3.3 Baseline OpenMP Implementation
3.4 Characteristics of Kernels
4 GPU Implementation of GYSELA Mini-App
4.1 Kokkos Implementation of GYSELA Mini-application
4.2 OpenACC Implementation of GYSELA Mini-application
5 Portable Implementation of GYSELA Mini-App with OpenACC/OpenMP and Kokkos
5.1 Portable Implementation with Kokkos
5.2 Portable Implementation with OpenACC/OpenMP
5.3 Performance Comparison of Baseline Versions
5.4 3D MDRange Policy
6 Performance Portability, Readability and Productivity
6.1 Performance Evaluation
6.2 Readability
6.3 Productivity
7 Summary
References
A Portable SIMD Primitive Using Kokkos for Heterogeneous Architectures
1 Introduction
2 Related Work
3 Portable simd Primitive
4 Experiments
4.1 PDE Assembly
4.2 2D Convolution
4.3 Compact gemm
4.4 Embedded Ensemble Propagation
5 Results and Performance Analysis
5.1 PDE Assembly
5.2 2D Convolution
5.3 Compact gemm
5.4 Embedded Ensemble Propagation
6 Assigning the Optimal LVL Value
7 Conclusion and Future Work
References
Author Index

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