Performance modeling of 3D MPDATA simulations on GPU cluster

Rojek, Krzysztof; Wyrzykowski, Roman

doi:10.1007/s11227-016-1774-z

Cited by 7 publications

(9 citation statements)

References 20 publications

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“…The developed implementation of MPDATA utilizes the CUDA+MPI programming model . The Piz Daint supercomputer contains Cray Linux operating system with CCE compiler version 8.5.5 and CUDA SDK 8.0.…”

Section: Resultsmentioning

confidence: 99%

Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU‐based supercomputers

Rojek

2018

Concurrency and Computation

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Summary In this work, we propose a method that allows us to reduce energy consumption of an application executed on supercomputing centers. The proposed method is based on a mixed precision arithmetic where the precision of data is calibrated at runtime. For this reason, we develop a modified version of the random forest algorithm. The effectiveness of the proposed approach is validated with a real‐life scientific application called MPDATA, which is part of the numerical model used in weather forecasting. The energy efficiency of the proposed method is examined using two GPU‐based clusters. The first of them is the Piz Daint supercomputer, currently ranked 3rd at the TOP500 list (November 2017). It is equipped with NVIDIA Tesla P100 GPU accelerators based on the Pascal architecture. The second is the MICLAB cluster containing NVIDIA Tesla K80 based on the Kepler architecture. The achieved results show that the proposed machine learning method allows us to provide the accuracy of computation comparable with that achieved double precision and reduce the energy consumption up to 36% compared to the double precision version of MPDATA.

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Section: Resultsmentioning

confidence: 99%

Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU‐based supercomputers

Rojek

2018

Concurrency and Computation

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“…The capability to utilize efficiently modern heterogeneous HPC systems becomes indispensable for improving the overall performance. 3,20,21 An example of research in this area is the approach proposed in our articles 2,22 for a stencil-based computational fluid dynamics application. The proposed methodology permits us to utilize the available resources effectively by dispersing computations across the entire CPU-GPU platform.…”

Section: Related Workmentioning

confidence: 99%

“…Heterogeneous computing architectures have become more and more popular in many application areas 1‐4 . Today, heterogeneous platforms are deployed in many setups, ranging from low‐power mobile systems to high‐performance computing systems 5 .…”

Section: Introductionmentioning

confidence: 99%

Exploration of OpenCL Heterogeneous Programming for Porting Solidification Modeling to CPU‐GPU Platforms

Halbiniak

Szustak

Olas

et al. 2020

Concurrency and Computation

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Summary This article provides a comprehensive study of OpenCL heterogeneous programming for porting applications to CPU–GPU computing platforms, with a real‐life application for the solidification modeling. The aim is to achieve a flexible workload distribution between available CPU–GPU resources and optimize application performance. Considering the solidification application as a use case, we explore the necessary steps required for (i) adaptation of an application to CPU–GPU platforms, and (ii) mapping the application workload onto the OpenCL programming model. The adaptation is based on a reformulation of steps developed previously for CPU–MIC architectures. The mapping process allows us to utilize OpenCL for harnessing CPU and GPU cores using data parallelism, as well as for the management of available compute devices with task parallelism. The resulting OpenCL code's performance and energy efficiency is experimentally studied for two platforms with powerful GPUs of various generations (with Kepler and Volta architectures). The experiments confirm the performance advantage of using computing resources of both GPUs and CPUs. The achieved benefit depends on the relationship between the computing power of CPUs and GPUs. Moreover, this gain entails the growth of the average power that increases the energy consumed during the application execution.

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“…The adaptation of the MPDATA algorithm to the GPU architecture is presented in Rojek and Wyrzykowski (2017) and Rojek et al (2017b). However, although both GPU and FPGA can be programmed using OpenCL, the code is not portable.…”

Section: Related Workmentioning

confidence: 99%

CFD code adaptation to the FPGA architecture

Rojek¹,

Halbiniak²,

Kuczynski

2020

The International Journal of High Performance Computing Applica

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For the last years, we observe the intensive development of accelerated computing platforms. Although current trends indicate a well-established position of GPU devices in the HPC environment, FPGA (Field-Programmable Gate Array) aspires to be an alternative solution to offload the CPU computation. This paper presents a systematic adaptation of four various CFD (Computational Fluids Dynamic) kernels to the Xilinx Alveo U250 FPGA. The goal of this paper is to investigate the potential of the FPGA architecture as the future infrastructure able to provide the most complex numerical simulations in the area of fluid flow modeling. The selected kernels are customized to a real-scientific scenario, compatible with the EULAG (Eulerian/semi-Lagrangian) fluid solver. The solver is used to simulate thermo-fluid flows across a wide range of scales and is extensively used in numerical weather prediction. The proposed adaptation is focused on the analysis of the strengths and weaknesses of the FPGA accelerator, considering performance and energy efficiency. The proposed adaptation is compared with a CPU implementation that was strongly optimized to provide realistic and objective benchmarks. The performance results are compared with a set of server CPUs containing various Intel generations, including Intel SkyLake-based CPUs as Xeon Gold 6148 and Xeon Platinum 8168, as well as Intel Xeon E5-2695 CPU based on the IvyBridge architecture. Since all the kernels belong to the group of memory-bound algorithms, our main challenge is to saturate global memory bandwidth and provide data locality with the intensive BRAM (Block RAM) reusing. Our adaptation allows us to reduce the performance per watt up to 80% compared to the CPUs.

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Performance modeling of 3D MPDATA simulations on GPU cluster

Cited by 7 publications

References 20 publications

Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU‐based supercomputers

Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU‐based supercomputers

Exploration of OpenCL Heterogeneous Programming for Porting Solidification Modeling to CPU‐GPU Platforms

CFD code adaptation to the FPGA architecture

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