The proceedings contain 8 papers. The topics discussed include: acceleration of element-by-element kernel in unstructured implicit low-order finite-element earthquake simulation using OpenACC on Pascal GPUs;towards ac...
ISBN:
(纸本)9781509061525
The proceedings contain 8 papers. The topics discussed include: acceleration of element-by-element kernel in unstructured implicit low-order finite-element earthquake simulation using OpenACC on Pascal GPUs;towards achieving performance portability usingdirectives for accelerators;a modern memory management system for OpenMP;an extension of OpenACC directives for out-of-core stencil computation with temporal blocking;OpenACC cache directive: opportunities and optimizations;identifying and scheduling loop chains usingdirectives;exploring compiler optimization opportunities for the OpenMP 4.× accelerator model on a POWER8+GPU platform;and a portable, high-level graph analytics framework targeting distributed, heterogeneous systems.
Recently, there has been growing interest in using standard language constructs (e.g. C++'s Parallel Algorithms and Fortran's do concurrent) for accelerated computing as an alternative to directive-based APIs ...
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ISBN:
(纸本)9783030977597;9783030977580
Recently, there has been growing interest in using standard language constructs (e.g. C++'s Parallel Algorithms and Fortran's do concurrent) for accelerated computing as an alternative to directive-based APIs (e.g. OpenMP and OpenACC). These constructs have the potential to be more portable, and some compilers already (or have plans to) support such standards. Here, we look at the current capabilities, portability, and performance of replacing directives with Fortran's do concurrent using a mini-app that currently implements OpenACC for GPU-acceleration and OpenMP for multi-core CPU parallelism. We replace as many directives as possible with do concurrent, testing various configurations and compiler options within three major compilers: GNU's gfortran, NVIDIA's nvfortran, and Intel's ifort. We find that with the right compiler versions and flags, many directives can be replaced without loss of performance or portability, and, in the case of nvfortran, they can all be replaced. We discuss limitations that may apply to more complicated codes and future language additions that may mitigate them. The software and Singularity/Apptainer containers are publicly provided to allow the results to be reproduced.
The latest production version of the fusion particle simulation code, Gyrokinetic Toroidal Code (GTC), has been ported to and optimized for the next generation exascale GPU supercomputing platform. Heterogeneous progr...
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ISBN:
(纸本)9783030122744;9783030122737
The latest production version of the fusion particle simulation code, Gyrokinetic Toroidal Code (GTC), has been ported to and optimized for the next generation exascale GPU supercomputing platform. Heterogeneous programmingusingdirectives has been utilized to balance the continuously implemented physical capabilities and rapidly evolving software/hardware systems. The original code has been refactored to a set of unified functions/calls to enable the acceleration for all the species of particles. Extensive GPU optimization has been performed on GTC to boost the performance of the particle push and shift operations. In order to identify the hotspots, the code was the first benchmarked on up to 8000 nodes of the Titan supercomputer, which shows about 2-3 times overall speedup comparing NVidia M2050 GPUs to Intel Xeon X5670 CPUs. This Phase I optimization was followed by further optimizations in Phase II, where single-node tests show an overall speedup of about 34 times on SummitDev and 7.9 times on Titan. The real physics tests on Summit machine showed impressive scaling properties that reaches roughly 50% efficiency on 928 nodes of Summit. The GPU + CPU speed up from purely CPU is over 20 times, leading to an unprecedented speed.
using the GPUs embedded in mobile devices allows for increasing the performance of the applications running on them while reducing the energy consumption of their execution. This article presents a task-based solution...
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ISBN:
(纸本)9783319748962;9783319748955
using the GPUs embedded in mobile devices allows for increasing the performance of the applications running on them while reducing the energy consumption of their execution. This article presents a task-based solution for adaptative, collaborative heterogeneous computing on mobile cloud environments. To implement our proposal, we extend the COMPSs-Mobile framework - an implementation of the COMPSs programming model for building mobile applications that offload part of the computation to the Cloud - to support offloading computation to GPUs through OpenCL. To evaluate our solution, we subject the prototype to three benchmark applications representing different application patterns.
Modern computers with multi-/many-core processors and accelerators feature a sophisticated and deep memory hierarchy, potentially including distinct main memory, high-bandwidth memory, texture memory and scratchpad me...
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ISBN:
(纸本)9781509061525
Modern computers with multi-/many-core processors and accelerators feature a sophisticated and deep memory hierarchy, potentially including distinct main memory, high-bandwidth memory, texture memory and scratchpad memory. The performance characteristics of these memories are varied, and studies have demonstrated the importance of using them effectively. In this paper, we propose an extension of the OpenMP API to address the needs of programmers to efficiently optimize their applications to use new memory technologies in a platform-agnostic and portable fashion. Our proposal separately exposes the characteristics of memory resources (such as kind) and the characteristics of allocations (such as alignment), and is fully compatible with existing OpenMP constructs.
While GPUs are increasingly popular for highperformance computing, optimizing the performance of GPU programs is a time-consuming and non-trivial process in general. This complexity stems from the low abstraction leve...
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ISBN:
(纸本)9781509061525
While GPUs are increasingly popular for highperformance computing, optimizing the performance of GPU programs is a time-consuming and non-trivial process in general. This complexity stems from the low abstraction level of standard GPU programming models such as CUDA and OpenCL: programmers are required to orchestrate low-level operations in order to exploit the full capability of GPUs. In terms of software productivity and portability, a more attractive approach would be to facilitate GPU programming by providing high-level abstractions for expressing parallel algorithms. OpenMP is a directive-based shared memory parallel programming model and has been widely used for many years. From OpenMP 4.0 onwards, GPU platforms are supported by extending OpenMP's high-level parallel abstractions with acceleratorprogramming. This extension allows programmers to write GPU programs in standard C/C++ or Fortran languages, without exposing too many details of GPU architectures. However, such high-level parallel programming strategies generally impose additional program optimizations on compilers, which could result in lower performance than fully hand-tuned code with low-level programming models. To study potential performance improvements by compiling and optimizing high-level GPU programs, in this paper, we 1) evaluate a set of OpenMP 4.x benchmarks on an IBM POWER8 and NVIDIA Tesla GPU platform and 2) conduct a comparable performance analysis among hand-written CUDA and automatically-generated GPU programs by the IBM XL and clang/LLVM compilers.
In this paper we explore the performance portability of directives provided by OpenMP 4 and OpenACC to program various types of node architectures with attached accelerators, both self-hosted multicore and offload mul...
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ISBN:
(纸本)9781509061525
In this paper we explore the performance portability of directives provided by OpenMP 4 and OpenACC to program various types of node architectures with attached accelerators, both self-hosted multicore and offload multicore/GPU. Our goal is to examine how successful OpenACC and the newer offload features of OpenMP 4.5 are for moving codes between architectures, how much tuning might be required and what lessons we can learn from this experience. To do this, we use examples of algorithms with varying computational intensities for our evaluation, as both compute and data access efficiency are important considerations for overall application performance. We implement these kernels using various methods provided by newer OpenACC and OpenMP implementations, and we evaluate their performance on various platforms including both X86_64 with attached NVIDIA GPUs, self-hosted Intel Xeon Phi KNL, as well as an X86_64 host system with Intel Xeon Phi coprocessors. In this paper, we explain what factors affected the performance portability such as how to pick the right programming model, its programming style, its availability on different platforms, and how well compilers can optimize and target to multiple platforms.
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