Accelerating phase-change heat conduction simulations on GPUs

Numerical simulation of the phase-change heat conduction process plays an important role in numerous engineering applications such as metal and alloy production and casting. Because of the increasing demands on the efficiency of the computational models utilized in advanced process control and optim...
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Autor*in:	Xiao-Yu Liu [verfasserIn] Zhi Xie [verfasserIn] Jian Yang [verfasserIn] Hong-Ji Meng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Phase change Heat transfer Parallel computation Solidification Performance optimization GPU

Übergeordnetes Werk:	In: Case Studies in Thermal Engineering - Elsevier, 2015, 39(2022), Seite 102410-
Übergeordnetes Werk:	volume:39 ; year:2022 ; pages:102410-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.csite.2022.102410

Katalog-ID:	DOAJ023024321

Internformat


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allfieldsSound	10.1016/j.csite.2022.102410 doi (DE-627)DOAJ023024321 (DE-599)DOAJd1648356c7dc4767a82c5b4a9cd1d571 DE-627 ger DE-627 rakwb eng TA1-2040 Xiao-Yu Liu verfasserin aut Accelerating phase-change heat conduction simulations on GPUs 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Numerical simulation of the phase-change heat conduction process plays an important role in numerous engineering applications such as metal and alloy production and casting. Because of the increasing demands on the efficiency of the computational models utilized in advanced process control and optimization, high-performance computational models are becoming a necessary building block of practical applications. Thus, in this paper, we focus on accelerating the phase-change heat conduction simulations on GPUs. In addition to taking the advantage of thousands of GPU cores, the current paper presents a set of optimization methods to improve the resource usage of GPUs. The proposed methods include thread rescheduling, global function design, and a data caching mechanism, which helps establish an appropriate mapping from the numerical algorithm to the GPU hardware feature, thus remarkably enhancing the solving efficiency. We have validated the accuracy of the accelerated model with the Stefan problem and evaluated its performance with a three-dimensional steel solidification problem. A performance boost has been demonstrated, where the optimized GPU solver achieves a 333.6x speed-up compared to the non-parallel CPU solver. The performance characteristic of the accelerated solver makes it a powerful and promising tool for practical industrial usage. Phase change Heat transfer Parallel computation Solidification Performance optimization GPU Engineering (General). Civil engineering (General) Zhi Xie verfasserin aut Jian Yang verfasserin aut Hong-Ji Meng verfasserin aut In Case Studies in Thermal Engineering Elsevier, 2015 39(2022), Seite 102410- (DE-627)76809299X (DE-600)2732684-6 2214157X nnns volume:39 year:2022 pages:102410- https://doi.org/10.1016/j.csite.2022.102410 kostenfrei https://doaj.org/article/d1648356c7dc4767a82c5b4a9cd1d571 kostenfrei http://www.sciencedirect.com/science/article/pii/S2214157X22006463 kostenfrei https://doaj.org/toc/2214-157X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 39 2022 102410-
language	English
source	In Case Studies in Thermal Engineering 39(2022), Seite 102410- volume:39 year:2022 pages:102410-
sourceStr	In Case Studies in Thermal Engineering 39(2022), Seite 102410- volume:39 year:2022 pages:102410-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Phase change Heat transfer Parallel computation Solidification Performance optimization GPU Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	Case Studies in Thermal Engineering
authorswithroles_txt_mv	Xiao-Yu Liu @@aut@@ Zhi Xie @@aut@@ Jian Yang @@aut@@ Hong-Ji Meng @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	76809299X
id	DOAJ023024321
language_de	englisch
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callnumber-first	T - Technology
author	Xiao-Yu Liu
spellingShingle	Xiao-Yu Liu misc TA1-2040 misc Phase change misc Heat transfer misc Parallel computation misc Solidification misc Performance optimization misc GPU misc Engineering (General). Civil engineering (General) Accelerating phase-change heat conduction simulations on GPUs
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topic_title	TA1-2040 Accelerating phase-change heat conduction simulations on GPUs Phase change Heat transfer Parallel computation Solidification Performance optimization GPU
topic	misc TA1-2040 misc Phase change misc Heat transfer misc Parallel computation misc Solidification misc Performance optimization misc GPU misc Engineering (General). Civil engineering (General)
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title	Accelerating phase-change heat conduction simulations on GPUs
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title_full	Accelerating phase-change heat conduction simulations on GPUs
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title_sort	accelerating phase-change heat conduction simulations on gpus
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title_auth	Accelerating phase-change heat conduction simulations on GPUs
abstract	Numerical simulation of the phase-change heat conduction process plays an important role in numerous engineering applications such as metal and alloy production and casting. Because of the increasing demands on the efficiency of the computational models utilized in advanced process control and optimization, high-performance computational models are becoming a necessary building block of practical applications. Thus, in this paper, we focus on accelerating the phase-change heat conduction simulations on GPUs. In addition to taking the advantage of thousands of GPU cores, the current paper presents a set of optimization methods to improve the resource usage of GPUs. The proposed methods include thread rescheduling, global function design, and a data caching mechanism, which helps establish an appropriate mapping from the numerical algorithm to the GPU hardware feature, thus remarkably enhancing the solving efficiency. We have validated the accuracy of the accelerated model with the Stefan problem and evaluated its performance with a three-dimensional steel solidification problem. A performance boost has been demonstrated, where the optimized GPU solver achieves a 333.6x speed-up compared to the non-parallel CPU solver. The performance characteristic of the accelerated solver makes it a powerful and promising tool for practical industrial usage.
abstractGer	Numerical simulation of the phase-change heat conduction process plays an important role in numerous engineering applications such as metal and alloy production and casting. Because of the increasing demands on the efficiency of the computational models utilized in advanced process control and optimization, high-performance computational models are becoming a necessary building block of practical applications. Thus, in this paper, we focus on accelerating the phase-change heat conduction simulations on GPUs. In addition to taking the advantage of thousands of GPU cores, the current paper presents a set of optimization methods to improve the resource usage of GPUs. The proposed methods include thread rescheduling, global function design, and a data caching mechanism, which helps establish an appropriate mapping from the numerical algorithm to the GPU hardware feature, thus remarkably enhancing the solving efficiency. We have validated the accuracy of the accelerated model with the Stefan problem and evaluated its performance with a three-dimensional steel solidification problem. A performance boost has been demonstrated, where the optimized GPU solver achieves a 333.6x speed-up compared to the non-parallel CPU solver. The performance characteristic of the accelerated solver makes it a powerful and promising tool for practical industrial usage.
abstract_unstemmed	Numerical simulation of the phase-change heat conduction process plays an important role in numerous engineering applications such as metal and alloy production and casting. Because of the increasing demands on the efficiency of the computational models utilized in advanced process control and optimization, high-performance computational models are becoming a necessary building block of practical applications. Thus, in this paper, we focus on accelerating the phase-change heat conduction simulations on GPUs. In addition to taking the advantage of thousands of GPU cores, the current paper presents a set of optimization methods to improve the resource usage of GPUs. The proposed methods include thread rescheduling, global function design, and a data caching mechanism, which helps establish an appropriate mapping from the numerical algorithm to the GPU hardware feature, thus remarkably enhancing the solving efficiency. We have validated the accuracy of the accelerated model with the Stefan problem and evaluated its performance with a three-dimensional steel solidification problem. A performance boost has been demonstrated, where the optimized GPU solver achieves a 333.6x speed-up compared to the non-parallel CPU solver. The performance characteristic of the accelerated solver makes it a powerful and promising tool for practical industrial usage.
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title_short	Accelerating phase-change heat conduction simulations on GPUs
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Accelerating phase-change heat conduction simulations on GPUs

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