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An investigation of hybrid CPU-GPU solvers for supersonic reacting flow simulation with detailed chemical kinetics

AEROSPACE SCIENCE AND TECHNOLOGY 2022年 126卷

作者： Rao, Sihang Xu, Xu Chen, Bing Yang, Qingchun Beihang Univ Sch Astronaut Beijing 100191 Peoples R China

Accurate simulations of supersonic reacting flow require the use of detailed chemical kinetics to capture the complex phenomena such as auto-ignition and broken, thickened flamelets. However, the detailed chemical mechanisms for hydrocarbon fuels used in supersonic combustion typically have a great number of species and reactions. This paper reports on the implementation of a hybrid CPU-GPU solver by using the graphics processing unit (GPU) as a coprocessor to accelerate the computation of chemical source terms in modeling supersonic reacting flows with detailed chemical kinetics. The chemical kinetics are solved explicitly via a fully-coupled method with an implicit scheme. Three parallel strategies with different storage schemes were explored in implementing the highly parallel GPU algorithms for accelerating combustion modeling. Significant acceleration was obtained by using these parallel algorithms for chemical source terms evaluation. In order to verify the viability of the solver, two supersonic combustion flow test cases were conducted. The performance of these algorithms is evaluated by modeling four chemical reaction mechanisms on different grids compared with the CPU-only solver. The computation cost of these algorithms is basically proportional to the size of the grids. The performance gain of these parallel algorithms depends on the size of the reaction mechanism. A maximum speedup factor of over 80 was obtained by modeling the mechanism consisting of 348 species and 2163 reactions. The significant performance improvement provided by these parallel algorithms can provide a significant perspective for designing GPU-accelerated algorithms for applications in simulating supersonic reacting flows. (C) 2022 Elsevier Masson SAS. All rights reserved.

关键词： Supersonic reacting flow Computational fluid dynamics GPU Detailed chemical kinetics parallel algorithm

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Simulation of Seismic Wave Propagation in a Multicomponent Oil Deposit Model

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INTERNATIONAL JOURNAL OF APPLIED MECHANICS 2020年第8期12卷

作者： Golubev, Vasily Shevchenko, Alexey Petrov, Igor Moscow Inst Phys & Technol Lab Appl Numer Geophys Inst Sky Lane 9 Dolgoprudnyi 141700 Moscow Region Russia

A seismic survey is perhaps the most common geophysical technique used to locate potential oil and natural gas deposits in the geologic structures. Thanks to the rapid development of modern high-performance computing systems, the computer simulation technology plays a crucial role in processing the field data. The precision of the full-waveform inversion (FWI) essentially depends on the quality of the direct problem solver. This paper introduces a new approach to the numerical simulation of wave processes in complex heterogeneous media. The linear elasticity theory is applied to simulate the dynamic behavior of curvilinear geological layers. In contrast to the conventional approach, the producing oil formation is described in the frame of a porous fluid-filled model. It allows us to explicitly take into account the porosity, oil density, and other physical parameters. The method of setting the physically correct contact conditions between the reservoir and the geological massif based on the transport equation solution for Riemann invariants was successfully implemented. The grid-characteristic method, previously thoroughly verified on acoustic and elastic problems, was adopted. The explicit time-stepping procedure was derived for a two-dimensional case with a method of splitting along coordinate axes. This method guarantees the preservation of the scheme approximation order. The potential application of the new method to a complex model based on the data from the famous Russian oil deposit - the Bazhen Formation - is demonstrated. The seismic responses were registered on the wave fields and synthetic seismograms. The novelty of this paper relates to a uniform approach to the wave propagation simulation in the heterogeneous medium containing contacting subdomains with different rheology types.

关键词： Mathematical modeling numerical method parallel algorithm seismic wave seismic survey process porous medium Biot model

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PMFFRC: a large-scale genomic short reads compression optimizer via memory modeling and redundant clustering

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BMC BIOINFORMATICS 2023年第1期24卷 454-454页

作者： Sun, Hui Zheng, Yingfeng Xie, Haonan Ma, Huidong Liu, Xiaoguang Wang, Gang Nankai Univ Coll Comp Sci Nankai Baidu Joint Lab Tianjin Peoples R China Guangxi Univ Sch Elect Engn Inst Artificial Intelligence Nanning Peoples R China

BackgroundGenomic sequencing reads compressors are essential for balancing high-throughput sequencing short reads generation speed, large-scale genomic data sharing, and infrastructure storage expenditure. However, most existing short reads compressors rarely utilize big-memory systems and duplicative information between diverse sequencing files to achieve a higher compression ratio for conserving reads data storage *** employ compression ratio as the optimization objective and propose a large-scale genomic sequencing short reads data compression optimizer, named PMFFRC, through novelty memory modeling and redundant reads clustering technologies. By cascading PMFFRC, in 982 GB fastq format sequencing data, with 274 GB and 3.3 billion short reads, the state-of-the-art and reference-free compressors HARC, SPRING, Mstcom, and FastqCLS achieve 77.89%, 77.56%, 73.51%, and 29.36% average maximum compression ratio gains, respectively. PMFFRC saves 39.41%, 41.62%, 40.99%, and 20.19% of storage space sizes compared with the four unoptimized *** rational usage big-memory of compression server, effectively saving the sequencing reads data storage space sizes, which relieves the basic storage facilities costs and community sharing transmitting overhead. Our work furnishes a novel solution for improving sequencing reads compression and saving storage space. The proposed PMFFRC algorithm is packaged in a same-name Linux toolkit, available un-limited at https://***/fahaihi/PMFFRC.

关键词： Short reads data Data compression Fastq parallel algorithm

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Multiswarm Artificial Bee Colony algorithm based on spark cloud computing platform for medical image registration

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 192卷 105432-105432页

作者： Wen, Tingxi Liu, Haotian Lin, Luxin Wang, Bin Hou, Jigong Huang, Chuanbo Pan, Ting Du, Yu Huaqiao Univ Coll Engn Quanzhou 362021 Peoples R China Fujian Prov Key Lab Data Intens Comp Quanzhou 362021 Peoples R China Fujian Key Lab Autonomous Controllable Software Quanzhou 362000 Peoples R China Postdoctoral Workstn Linewell Software Co Ltd Quanzhou 362000 Peoples R China

Background Over the years, medical image registration has been widely used in various fields. However, different application characteristics, such as scale, computational complexity, and optimization goals, can cause problems. Therefore, developing an optimization algorithm based on clustering calculation is *** To solve the aforementioned problem, a multiswarm artificial bee colony (MS-ABC) multi-objective optimization algorithm based on clustering calculation is proposed. This algorithm can accelerate the resolution of complex problems on the Spark platform. Experiments show that the algorithm can optimize certain conventional complex problems and perform medical image registration *** Results show that the MS-ABC algorithm demonstrates excellent performance in medical image registration tests. The optimization results of the MS-ABC algorithm for conventional problems are similar to those of existing algorithms; however, its performance is more time efficient for complex problems, especially when additional goals are *** The MS-ABC algorithm is applied to the Spark platform to accelerate the resolution of complex application problems. It can solve the problem of traditional algorithms regarding long calculation time, especially in the case of highly complex and large amounts of data, which can substantially improve data-processing efficiency.

关键词： parallel algorithm Mapreduce Spark platform Medical image registration Multiswarm artificial bee colony

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Planar Graph Perfect Matching Is in NC

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JOURNAL OF THE ACM 2020年第4期67卷 21-21页

作者： Anari, Nima Vazirani, Vijay V. Stanford Univ Comp Sci Dept 353 Serra Mall Stanford CA 94305 USA Univ Calif Irvine Donald Bren Sch Informat & Comp Sci 6210 Donald Bren Hall Irvine CA 92697 USA

Is perfect matching in NC? That is, is there a deterministic fast parallel algorithm for it? This has been an outstanding open question in theoretical computer science for over three decades, ever since the discovery of RNC perfect matching algorithms. Within this question, the case of planar graphs has remained an enigma: On the one hand, counting the number of perfect matchings is far harder than finding one (the former is #P-complete and the latter is in P), and on the other, for planar graphs, counting has long been known to be in NC whereas finding one has resisted a solution. In this article, we give an NC algorithm for finding a perfect matching in a planar graph. Our algorithm uses the above-stated fact about counting perfect matchings in a crucial way. Our main new idea is an NC algorithm for finding a face of the perfect matching polytope at which a set (which could be polynomially large) of conditions, involving constraints of the polytope, are simultaneously satisfied. Several other ideas are also needed, such as finding, in NC, a point in the interior of the minimum-weight face of this polytope and finding a balanced tight odd set.

关键词： parallel algorithm planar graph perfect matching Tutte matrix Pfaffian

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parallelized maximization of nonmonotone one-sided σ-smooth function

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COMPUTERS & ELECTRICAL ENGINEERING 2023年 105卷

作者： Zhang, Hongxiang Xu, Dachuan Zhang, Yapu Zhang, Zhenning Beijing Univ Technol Inst Operat Res & Informat Engn Beijing 100124 Peoples R China

In this paper, we study the problem of maximizing a nonmonotone nonnegative one-sided sigma-smooth (OSS) function G(x). For a downwards-closed basis polyhedron constraint, we propose two classes of parallel algorithms under deterministic and random settings. For the deterministic nonmonotone OSS problem, we design the Jump-Start parallel Frank Wolfe (JSPFW) algorithm and obtain an approximation solution of (e(-1) - e(-2) - O(epsilon)). The JSPFW algorithm has (O(log n/epsilon(2))) adaptive rounds and O(n log n/epsilon(2)) queries about function and its gradient value. For the stochastic OSS problem, we design the Stochastic parallel Frank Wolfe (SPFW) algorithm and get the (e(-1) - e(-2))(1 - O(epsilon))(1 - o(1)) approximation solution. The SPFW algorithm needs O(n(2)) adaptive rounds and consumes O(n(2)) queries about gradient function value.

关键词： OSS function parallel algorithm Down-closed Frank Wolfe

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Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC

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COMPUTER PHYSICS COMMUNICATIONS 2020年 254卷 107330-107330页

作者： Liou, Kai-Hsin Yang, Chao Chelikowsky, James R. Univ Texas Austin McKetta Dept Chem Engn Austin TX 78712 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA Univ Texas Austin Dept Phys Austin TX 78712 USA Univ Texas Austin Ctr Computat Mat Oden Inst Computat Engn & Sci Austin TX 78712 USA

We present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn-Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration. (C) 2020 Elsevier B.V. All rights reserved.

关键词： parallel algorithm Electronic structure Density functional theory Eigenvalue problem Polynomial filtering Spectrum slicing

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A Complementary Topographic Feature Detection algorithm Based on Surface Curvature for Three-Dimensional Level-Set Functions

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JOURNAL OF SCIENTIFIC COMPUTING 2023年第3期94卷 71-71页

作者： Lenz, Christoph Aguinsky, Luiz Felipe Hossinger, Andreas Weinbub, Josef TU Wien Inst Microelect Christian Doppler Lab High Performance TCAD Gusshausstr 27-29 A-1040 Vienna Austria Silvaco Europe Ltd Compass Point St Ives Cambridge PE27 5JL England

The level-set method is widely used in expanding front simulations in numerous fields of computational research, such as computer graphics, physics, or microelectronics. In the latter, the level-set method is employed for topography simulations of semiconductor device fabrication processes, being driven by complicated physical and chemical models. These models tend to produce surfaces with critical points where accuracy is paramount. To efficiently increase the accuracy in regions neighboring these critical points, automatic hierarchical domain refinement is required, guided by robust feature detection. Feature detection has to be computationally efficient and sufficiently accurate to reliably detect the critical points. To that end, we present a fast parallel geometric feature detection algorithm for three-dimensional level-set functions. Our approach is based on two different, complementary curvature calculation methods of the zero level-set and an optimized feature detection parameter to detect features. For performance reasons, our algorithm can be in principal linked to different curvature calculation methods, however, as will be discussed, two particularly attractive options are available: (i) A novel extension of the standard curvature calculation method for level-set functions, and (ii) an often disregarded method for calculating the curvature due to its purported low numerical accuracy. We show, however, that the latter is still a viable option, and that our algorithm is able to reliably detect features on geometries stemming from complicated, practically relevant geometries. Our algorithm and findings are applicable to other fields of applications such as surface simplification.

关键词： Level-set method Curvature Feature detection parallel algorithm

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SHAPE-RECOGNITION USING A FIXED-SIZE VLSI ARCHITECTURE

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 1995年第1期9卷 1-21页

作者： CHENG, HD CHENG, X CONCORDIA UNIV DEPT COMP SCIMONTREALPQ H3G 1M8CANADA UTAH STATE UNIV DEPT COMP SCILOGANUT 84322

Shape recognition is an important research area in pattern recognition. It also has wide practical applications in many fields. An attribute grammar approach to shape recognition combines both the advantages of syntactic and statistical methods and makes shape recognition more accurate and efficient. However, the time complexity of a sequential shape recognition algorithm using attribute grammar is O(n(3)) where n is the length of an input string. When the problem size is very large it needs much more computing time, therefore a high speed parallel shape recognition is necessary to meet the demands of some real-time applications. This paper presents a parallel shape recognition algorithm and also discusses the algorithm partition problem as well as its implementation on a fixed-size VLSI architecture. The proposed algorithm has time complexity O(n(3)/k(2)) if using k x k processing elements. When k = n, its time complexity is O(n). The experiment has been conducted to verify the performance of the proposed algorithm. The correctness of the algorithm partition and the behavior of the proposed VLSI architecture have also been proved through the experiment. The results indicate that the proposed algorithm and the VLSI architecture could be very useful to imaging processing, pattern recognition and related areas, especially for real-time applications.

关键词： SHAPE RECOGNITION ATTRIBUTE GRAMMAR parallel algorithm VLSI ARCHITECTURE REAL-TIME PROCESSING

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Dual Domain Decomposition Method for High-Resolution 3D Simulation of Groundwater Flow and Transport

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WATER 2024年第13期16卷 1864页

作者： Deng, Hao Li, Jiaxin Huang, Jixian Zou, Yanhong Liu, Yu Chen, Yuxiang Zheng, Yang Mao, Xiancheng Cent South Univ Sch Geosci & Info Phys Changsha 410083 Peoples R China Cent South Univ Key Lab Metallogen Predict Nonferrous Met & Geol E Minist Educ Changsha 410083 Peoples R China

The high-resolution 3D groundwater flow and transport simulation problem requires massive discrete linear systems to be solved, leading to significant computational time and memory requirements. The domain decomposition method is a promising technique that facilitates the parallelization of problems with minimal communication overhead by dividing the computation domain into multiple subdomains. However, directly utilizing a domain decomposition scheme to solve massive linear systems becomes impractical due to the bottleneck in algebraic operations required to coordinate the results of subdomains. In this paper, we propose a two-level domain decomposition method, named dual-domain decomposition, to efficiently solve the massive discrete linear systems in high-resolution 3D groundwater simulations. The first level of domain decomposition partitions the linear system problem into independent linear sub-problems across multiple subdomains, enabling parallel solutions with significantly reduced complexity. The second level introduces a domain decomposition preconditioner to solve the linear system, known as the Schur system, used to coordinate results from subdomains across their boundaries. This additional level of decomposition parallelizes the preconditioning of the Schur system, addressing the bottleneck of the Schur system solution while improving its convergence rates. The dual-domain decomposition method facilitates the partition and distribution of the computation to be solved into independent finely grained computational subdomains, substantially reducing both computational and memory complexity. We demonstrate the scalability of our proposed method through its application to a high-resolution 3D simulation of chromium contaminant transport in groundwater. Our results indicate that our method outperforms both the vanilla domain decomposition method and the algebraic multigrid preconditioned method in terms of runtime, achieving up to 8.617x and 5.515x speedups,

关键词： groundwater simulation domain decomposition parallel algorithm fast solution 3D numeric modeling

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