Solving the inverse heat conduction problem using NVLink capable Power architecture

The accurate knowledge of Heat Transfer Coefficients is essential for the design of precise heat transfer operations. The determination of these values requires Inverse Heat Transfer Calculations, which are usually based on heuristic optimisation techniques, like Genetic Algorithms or Particle Swarm...

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Vydáno v:	PeerJ. Computer science Ročník 3; s. e138
Hlavní autor:	Szénási, Sándor
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	San Diego PeerJ. Ltd 20.11.2017 PeerJ, Inc PeerJ Inc
Témata:	Algorithms Applied research Boundary conditions Chromosomes Computer simulation Conduction heating Conductive heat transfer Continuous casting Cooling CUDA Data transfer (computers) Data-parallel algorithm Genetic algorithms GPU Heat conduction Heat transfer Heat transfer coefficients Informatics Inverse heat conduction problem Inverse problems Iterative methods Knowledge management Mathematical analysis Methods Optimization Parallelisation Parameter estimation Particle accelerators Particle swarm optimization Simulation Thermal conductivity
ISSN:	2376-5992, 2376-5992
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Abstract	The accurate knowledge of Heat Transfer Coefficients is essential for the design of precise heat transfer operations. The determination of these values requires Inverse Heat Transfer Calculations, which are usually based on heuristic optimisation techniques, like Genetic Algorithms or Particle Swarm Optimisation. The main bottleneck of these heuristics is the high computational demand of the cost function calculation, which is usually based on heat transfer simulations producing the thermal history of the workpiece at given locations. This Direct Heat Transfer Calculation is a well parallelisable process, making it feasible to implement an efficient GPU kernel for this purpose. This paper presents a novel step forward: based on the special requirements of the heuristics solving the inverse problem (executing hundreds of simulations in a parallel fashion at the end of each iteration), it is possible to gain a higher level of parallelism using multiple graphics accelerators. The results show that this implementation (running on 4 GPUs) is about 120 times faster than a traditional CPU implementation using 20 cores. The latest developments of the GPU-based High Power Computations area were also analysed, like the new NVLink connection between the host and the devices, which tries to solve the long time existing data transfer handicap of GPU programming.
AbstractList	The accurate knowledge of Heat Transfer Coefficients is essential for the design of precise heat transfer operations. The determination of these values requires Inverse Heat Transfer Calculations, which are usually based on heuristic optimisation techniques, like Genetic Algorithms or Particle Swarm Optimisation. The main bottleneck of these heuristics is the high computational demand of the cost function calculation, which is usually based on heat transfer simulations producing the thermal history of the workpiece at given locations. This Direct Heat Transfer Calculation is a well parallelisable process, making it feasible to implement an efficient GPU kernel for this purpose. This paper presents a novel step forward: based on the special requirements of the heuristics solving the inverse problem (executing hundreds of simulations in a parallel fashion at the end of each iteration), it is possible to gain a higher level of parallelism using multiple graphics accelerators. The results show that this implementation (running on 4 GPUs) is about 120 times faster than a traditional CPU implementation using 20 cores. The latest developments of the GPU-based High Power Computations area were also analysed, like the new NVLink connection between the host and the devices, which tries to solve the long time existing data transfer handicap of GPU programming.
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Author	Szénási, Sándor
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SubjectTerms	Algorithms Applied research Boundary conditions Chromosomes Computer simulation Conduction heating Conductive heat transfer Continuous casting Cooling CUDA Data transfer (computers) Data-parallel algorithm Genetic algorithms GPU Heat conduction Heat transfer Heat transfer coefficients Informatics Inverse heat conduction problem Inverse problems Iterative methods Knowledge management Mathematical analysis Methods Optimization Parallelisation Parameter estimation Particle accelerators Particle swarm optimization Simulation Thermal conductivity
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Title	Solving the inverse heat conduction problem using NVLink capable Power architecture
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