NAS Parallel Benchmarks for GPGPUs Using a Directive-Based Programming Model

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

doi:10.1007/978-3-319-17473-0_5

Cited by 20 publications

(38 citation statements)

References 11 publications

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“…We could easily recognize various suboptimal performance trends. For the OpenACC port of the NPB [19], we observe that LU and FT do not significantly improve with the increase in SM count, while MG improves and then drop in performance at high SM count. The performance does not improve significantly with the increase of problem size from Class A to B or C. For the CUDA variant of NPB [9], LU has a significant improvement with SM count.…”

Section: Motivationmentioning

confidence: 87%

Performance Analysis of GPU Programming Models Using the Roofline Scaling Trajectories

Ibrahim

Williams

Oliker

2020

Benchmarking, Measuring, and Optimizing

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Performance analysis is a daunting job, especially for the rapid-evolving accelerator technologies. The Roofline Scaling Trajectories technique aims at diagnosing various performance bottlenecks for GPU programming models through the visually intuitive Roofline plots. In this work, we introduce the use of the Roofline Scaling Trajectories to capture major performance bottlenecks on NVIDIA Volta GPU architectures, such as warp efficiency, occupancy, and locality. Using this analysis technique, we explain the performance characteristics of the NAS Parallel Benchmarks (NPB) written with two programming models, CUDA and OpenACC. We present the influence of the programming model on the performance and scaling characteristics. We also leverage the insights of the Roofline Scaling Trajectory analysis to tune some of the NAS Parallel Benchmarks, achieving up to 2× speedup.

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Section: Motivationmentioning

confidence: 87%

Performance Analysis of GPU Programming Models Using the Roofline Scaling Trajectories

Ibrahim

Williams

Oliker

2020

Benchmarking, Measuring, and Optimizing

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“…Xu et al [13] focused on directive-based parallelization of NPB benchmarks. After analyzing and profiling the OpenMP version of NPB, they annotate the source code with OpenACC directives to automatically generate GPU versions of the benchmarks.…”

Section: Related Workmentioning

confidence: 99%

Multi-GPU Parallelization of the NAS Multi-Zone Parallel Benchmarks

González

Morancho

2021

IEEE Trans. Parallel Distrib. Syst.

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GPU-based computing systems have become a widely accepted solution for the high-performance-computing (HPC) domain. GPUs have shown highly competitive performance-per-watt ratios and can exploit an astonishing level of parallelism. However, exploiting the peak performance of such devices is a challenge, mainly due to the combination of two essential aspects of multi-GPU execution. On one hand, the workload should be distributed evenly among the GPUs. On the other hand, communications between GPU devices are costly and should be minimized. Therefore, a trade-of between work-distribution schemes and communication overheads will condition the overall performance of parallel applications run on multi-GPU systems. In this paper we present a multi-GPU implementation of NAS Multi-Zone Parallel Benchmarks (which execution alternate communication and computational phases). We propose several work-distribution strategies that try to evenly distribute the workload among the GPUs. Our evaluations show that performance is highly sensitive to this distribution strategy, as the the communication phases of the applications are heavily affected by the work-distribution schemes applied in computational phases. In particular, we consider Static, Dynamic and Guided schedulers to find a trade-off between both phases to maximize the overall performance. In addition, we compare those schedulers with an optimal scheduler computed offline using IBM CPLEX. On an evaluation environment composed of 2 x IBM Power9 8335-GTH and 4 x GPU NVIDIA V100 (Volta), our multi-GPU parallelization outperforms single-GPU execution from 1.48x to 1.86x (2 GPUs) and from 1.75x to 3.54x (4 GPUs). This paper analyses these improvements in terms of the relationship between the computational and communication phases of the applications as the number of GPUs is increased. We prove that Guided schedulers perform at similar level as optimal schedulers.

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“…The NPB has been implemented for diverse parallel programming models over the years, which include OpenMP [22], MPI [23], [24], and Multi-Zone [25]. In particular, parallel versions for accelerators include implementations in OpenCL [26] and OpenACC [27]. The CUDA version for the complete NPB kernels and applications has not been reported yet.…”

Section: ) Nas Parallel Benchmarks (Npb)mentioning

confidence: 99%

A Taste of Scientific Computing on the GPU-Accelerated Edge Device

Kang

Lim

2020

IEEE Access

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The computing power provided by the edge device is becoming increasingly improved in recent days, largely due to the technological development that realizes accelerators with a great amount of hardware parallelism. Thanks to the enhanced computing power, the application domain of the edge device is greatly expanding so that it is capable of performing new tasks previously inconceivable. In this paper, we investigate the potentials of the state-of-the-art edge devices in the context of scientific computing. We implement and perform stochastic biochemical simulation on a small cluster of modern, GPU-accelerated edge systems to examine the computational capability of the device. In addition, we perform and analyze the NAS Parallel Benchmarks (NPB) performance on the edge device. By comparing the performance of the edge device with other GPU (graphics processing unit) and CPU based systems across different platforms, we evaluate the applicability and usefulness of the modern hardware-accelerated edge devices in the scientific computing domain. INDEX TERMS edge computing, scientific computing, GPU (graphics processing unit)

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NAS Parallel Benchmarks for GPGPUs Using a Directive-Based Programming Model

Cited by 20 publications

References 11 publications

Performance Analysis of GPU Programming Models Using the Roofline Scaling Trajectories

Performance Analysis of GPU Programming Models Using the Roofline Scaling Trajectories

Multi-GPU Parallelization of the NAS Multi-Zone Parallel Benchmarks

A Taste of Scientific Computing on the GPU-Accelerated Edge Device

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