Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Gan, Lin; Fu, Haohuan; Luk, Wayne; Yang, Chao; Xue, Wei; Zhang, Youhui; Yang, Guangwen

doi:10.1145/2629581

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…Corresponding surface level distribution of MIC and GPU is provided in our previous works [ 27 , 28 ]. For FPGA solution, as mixed precision brings loss of accuracy, the conservation property is relaxed to 10 −11 [ 45 ]. Related work [ 46 , 47 ] demonstrates the feasibility of employing mixed-precision while ensuring a reasonable simulation accuracy.…”

Section: Resultsmentioning

confidence: 99%

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Solving global shallow water equations on heterogeneous supercomputers

Gan

Yang

et al. 2017

PLoS ONE

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The scientific demand for more accurate modeling of the climate system calls for more computing power to support higher resolutions, inclusion of more component models, more complicated physics schemes, and larger ensembles. As the recent improvements in computing power mostly come from the increasing number of nodes in a system and the integration of heterogeneous accelerators, how to scale the computing problems onto more nodes and various kinds of accelerators has become a challenge for the model development. This paper describes our efforts on developing a highly scalable framework for performing global atmospheric modeling on heterogeneous supercomputers equipped with various accelerators, such as GPU (Graphic Processing Unit), MIC (Many Integrated Core), and FPGA (Field Programmable Gate Arrays) cards. We propose a generalized partition scheme of the problem domain, so as to keep a balanced utilization of both CPU resources and accelerator resources. With optimizations on both computing and memory access patterns, we manage to achieve around 8 to 20 times speedup when comparing one hybrid GPU or MIC node with one CPU node with 12 cores. Using a customized FPGA-based data-flow engines, we see the potential to gain another 5 to 8 times improvement on performance. On heterogeneous supercomputers, such as Tianhe-1A and Tianhe-2, our framework is capable of achieving ideally linear scaling efficiency, and sustained double-precision performances of 581 Tflops on Tianhe-1A (using 3750 nodes) and 3.74 Pflops on Tianhe-2 (using 8644 nodes). Our study also provides an evaluation on the programming paradigm of various accelerator architectures (GPU, MIC, FPGA) for performing global atmospheric simulation, to form a picture about both the potential performance benefits and the programming efforts involved.

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Section: Resultsmentioning

confidence: 99%

“…Details to validate our DFE-based algorithms can be found in previous work [ 29 , 45 ], which shows an acceptable discrepancy caused by the reduced data precision. More details to prove the feasibility of using mixed-precision approach can be found in some related works [ 46 – 49 ].…”

Section: Resultsmentioning

confidence: 99%

Solving global shallow water equations on heterogeneous supercomputers

Gan

Yang

et al. 2017

PLoS ONE

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“…Some researchers have previously investigated acceleration effects using low-precision FPNs in a numerical weather/climate model (e.g. Düben & Palmer, 2014;Gan et al, 2015;Lingamneni et al, 2011). Düben and Palmer (2014) reported a 25% reduction in computing time for a 10-day weather simulation using SP FPNs.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Time Evolution of Numerical Errors When Using Floating Point Numbers in Shallow‐Water Models

Yamaura

Nishizawa

Tomita

2019

J Adv Model Earth Syst

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We carried out a theoretical investigation of the impact of the numerical errors caused by using floating point numbers (FPNs) in simulations, such as rounding errors. Under the presupposition that model variables can be written as the linear sum of the true value and the numerical error, equations governing the time evolution of numerical errors due to FPNs (FPN errors) are obtained by considering the total errors of the results of simulations of shallow‐water models and estimating the errors incurred by using FPNs with varying precision. We can use the time evolution equations to estimate the behavior of the FPN errors, then confirm these estimations by carrying out numerical simulations. In a geostrophic wind balance state, the FPN error oscillates and gradually increases in proportion to the square root of the number of time steps, like a random walk. We found that the error introduced by using FPNs can be considered as stochastic forcing. In a state of barotropic instability, the FPN error initially evolves as stochastic forcing, as in the case of the geostrophic wind balance state. However, it then begins to increase exponentially, like a barotropic instability wave. These numerical results are obtained by using a staggered‐grid arrangement and stable time‐integration method to retain near‐neutral numerical stability in the simulations. The FPN error tends to behave as theoretically predicted if the numerical stability is close to neutral.

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“…Grull and Kebschull [ 23 ] showed acceleration of 200 compared to an Intel i5 450 CPU for localization microscopy and acceleration of 5 over an Nvidia Tesla C1060 for electron tomography while maintaining full accuracy using Maxeler's DFEs. Gan et al [ 24 ] used Maxeler's DFEs to find the solution of global shallow water equations (SWEs), which are one of the most essential equation sets describing atmospheric dynamics. They achieved speedup of 20 over a fully optimized design on a CPU rack with two eight-core processors and speedup of 8 over the fully optimized Kepler GPU design.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

Milankovic

Mijailović

Filipović

et al. 2017

Computational and Mathematical Methods in Medicine

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Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

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Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Cited by 15 publications

References 26 publications

Solving global shallow water equations on heterogeneous supercomputers

Solving global shallow water equations on heterogeneous supercomputers

Theoretical Time Evolution of Numerical Errors When Using Floating Point Numbers in Shallow‐Water Models

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

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