With the latest advances in computing technology, a huge amount of efforts have gone into simulation of a range of scientific phenomena in engineering fields. One such case is the simulation of heat and mass transfer ...With the latest advances in computing technology, a huge amount of efforts have gone into simulation of a range of scientific phenomena in engineering fields. One such case is the simulation of heat and mass transfer in capillary porous media, which is becoming more and more necessary in analyzing a number of eventualities in science and engineering applications. However, this procedure of numerical solution of heat and mass transfer equations for capillary porous media is very time consuming. Therefore, this paper pursuit is at making use of one of the acceleration methods developed in the graphics community that exploits a graphical processing unit (GPU), which is applied to the numerical solutions of such heat and mass transfer equations. The nVidia Compute Unified Device Architecture (CUDA) programming model offers a correct approach of applying parallel computing to applications with graphical processing unit. This paper suggests a true improvement in the performance while solving the heat and mass transfer equations for capillary porous radially composite cylinder with the first type of boundary conditions. This heat and mass transfer simulation is carried out through the usage of CUDA platform on nVidia Quadro FX 4800 graphics card. Our experimental outcomes exhibit the drastic overall performance enhancement when GPU is used to illustrate heat and mass transfer simulation. GPU can considerably accelerate the performance with a maximum found speedup of more than 5-fold times. Therefore, the GPU is a good strategy to accelerate the heat and mass transfer simulation in porous media.展开更多
With the recent developments in computing technology, increased efforts have gone into simulation of various scientific methods and phenomenon in engineering fields. One such case is the simulation of heat and mass tr...With the recent developments in computing technology, increased efforts have gone into simulation of various scientific methods and phenomenon in engineering fields. One such case is the simulation of heat and mass transfer in capillary porous media, which is becoming more and more important in analysing various scenarios in engineering applications. Analysing such heat and mass transfer phenomenon in a given environment requires us to simulate it. This entails simulation of coupled heat mass transfer equations. However, this process of numerical solution of heat and mass transfer equations is very much time consuming. Therefore, this paper aims at utilizing one of the acceleration techniques developed in the graphics community that exploits a graphics processing unit (GPU) which is applied to the numerical solutions of heat and mass transfer equations. The nVidia Compute Unified Device Architecture (CUDA) programming model caters a good method of applying parallel computing to program the graphical processing unit. This paper shows a good improvement in the performance while solving the heat and mass transfer equations for capillary porous composite cylinder with the second kind of boundary conditions numerically running on GPU. This heat and mass transfer simulation is implemented using CUDA platform on nVidia Quadro FX 4800 graphics card. Our experimental results depict the drastic performance improvement when GPU is used to perform heat and mass transfer simulation. GPU can significantly accelerate the performance with a maximum observed speedup of more than 7-fold times. Therefore, the GPU is a good approach to accelerate the heat and mass transfer simulation.展开更多
A multi-scale hardware and software architecture implementing the EMMS （energy-minimization multi-scale） paradigm is proven to be effective in the simulation of a two-dimensional gas-solid suspension. General purpos...A multi-scale hardware and software architecture implementing the EMMS （energy-minimization multi-scale） paradigm is proven to be effective in the simulation of a two-dimensional gas-solid suspension. General purpose CPUs are employed for macro-scale control and optimization, and many integrated cores （MlCs） operating in multiple-instruction multiple-data mode are used for a molecular dynamics simulation of the solid particles at the meso-scale. Many cores operating in single-instruction multiple- data mode, such as general purpose graphics processing units （GPGPUs）, are employed for direct numerical simulation of the fluid flow at the micro-scale using the lattice Boltzmann method. This architecture is also expected to be efficient for the multi-scale simulation of other comolex systems.展开更多
The simulation is an important means of performance evaluation of the computer architecture. Nowadays, the serial simulation of general purpose graphics processing unit(GPGPU) architecture is the main bottleneck for t...The simulation is an important means of performance evaluation of the computer architecture. Nowadays, the serial simulation of general purpose graphics processing unit(GPGPU) architecture is the main bottleneck for the simulation speed. To address this issue, we propose the intra-kernel parallelization on a multicore processor and the inter-kernel parallelization on a multiple-machine platform. We apply these two methods to the GPGPU-sim simulator. The intra-kernel parallelization method firstly parallelizes the serial simulation of multiple compute units in one cycle. Then it parallelizes the timing and functional simulation to reduce the performance loss caused by the synchronization between different compute units. The inter-kernel parallelization method divides multiple kernels of a CUDA program into several groups and distributes these groups across multiple simulation hosts to perform the simulation. Experimental results show that the intra-kernel parallelization method achieves a speed-up of up to 12 with a maximum error rate of 0.009 4% on a 32-core machine, and the inter-kernel parallelization method can accelerate the simulation by a factor of up to 3.9 with a maximum error rate of 0.11% on four simulation hosts. The orthogonality between these two methods allows us to combine them together on multiple multi-core hosts to get further performance improvements.展开更多
矩阵主特征向量(principal eigenvectors computing,PEC)的求解是科学与工程计算中的一个重要问题。随着图形处理单元通用计算(general-purpose computing on graphics pro cessing unit,GPGPU)的兴起,利用GPU来优化大规模稀疏矩阵的图...矩阵主特征向量(principal eigenvectors computing,PEC)的求解是科学与工程计算中的一个重要问题。随着图形处理单元通用计算(general-purpose computing on graphics pro cessing unit,GPGPU)的兴起,利用GPU来优化大规模稀疏矩阵的图形处理单元求解得到了广泛关注。分别从应用特征和GPU体系结构特征两方面分析了PEC运算的性能瓶颈,提出了一种面向GPU的稀疏矩阵存储格式——GPU-ELL和一个针对GPU的线程优化映射策略,并设计了相应的PEC优化执行算法。在ATI HD Radeon5850上的实验结果表明,相对于传统CPU,该方案获得了最多200倍左右的加速,相对于已有GPU上的实现,也获得了2倍的加速。展开更多
Alias – Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors.However, as the mesh complexity gets higher and denser, the files become larger an...Alias – Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors.However, as the mesh complexity gets higher and denser, the files become larger and slower to import.This paper explores the use of GPUs to accelerate the importing and parsing of OBJ files by studying file read-time, runtime, and load resistance. We propose a new method of reading and parsing that circumvents GPU architecture limitations and improves performance, seeing the new GPU method outperforms CPU methods with a 6×– 8× speedup. When running on a heavily loaded system, the new method only received an 80% performance hit, compared to the160% that the CPU methods received. The loaded GPU speedup compared to unloaded CPU methods was3.5×, and, when compared to loaded CPU methods,8×. These results demonstrate that the time is right for further research into the use of data-parallel GPU acceleration beyond that of computer graphics and high performance computing.展开更多
文摘With the latest advances in computing technology, a huge amount of efforts have gone into simulation of a range of scientific phenomena in engineering fields. One such case is the simulation of heat and mass transfer in capillary porous media, which is becoming more and more necessary in analyzing a number of eventualities in science and engineering applications. However, this procedure of numerical solution of heat and mass transfer equations for capillary porous media is very time consuming. Therefore, this paper pursuit is at making use of one of the acceleration methods developed in the graphics community that exploits a graphical processing unit (GPU), which is applied to the numerical solutions of such heat and mass transfer equations. The nVidia Compute Unified Device Architecture (CUDA) programming model offers a correct approach of applying parallel computing to applications with graphical processing unit. This paper suggests a true improvement in the performance while solving the heat and mass transfer equations for capillary porous radially composite cylinder with the first type of boundary conditions. This heat and mass transfer simulation is carried out through the usage of CUDA platform on nVidia Quadro FX 4800 graphics card. Our experimental outcomes exhibit the drastic overall performance enhancement when GPU is used to illustrate heat and mass transfer simulation. GPU can considerably accelerate the performance with a maximum found speedup of more than 5-fold times. Therefore, the GPU is a good strategy to accelerate the heat and mass transfer simulation in porous media.
文摘With the recent developments in computing technology, increased efforts have gone into simulation of various scientific methods and phenomenon in engineering fields. One such case is the simulation of heat and mass transfer in capillary porous media, which is becoming more and more important in analysing various scenarios in engineering applications. Analysing such heat and mass transfer phenomenon in a given environment requires us to simulate it. This entails simulation of coupled heat mass transfer equations. However, this process of numerical solution of heat and mass transfer equations is very much time consuming. Therefore, this paper aims at utilizing one of the acceleration techniques developed in the graphics community that exploits a graphics processing unit (GPU) which is applied to the numerical solutions of heat and mass transfer equations. The nVidia Compute Unified Device Architecture (CUDA) programming model caters a good method of applying parallel computing to program the graphical processing unit. This paper shows a good improvement in the performance while solving the heat and mass transfer equations for capillary porous composite cylinder with the second kind of boundary conditions numerically running on GPU. This heat and mass transfer simulation is implemented using CUDA platform on nVidia Quadro FX 4800 graphics card. Our experimental results depict the drastic performance improvement when GPU is used to perform heat and mass transfer simulation. GPU can significantly accelerate the performance with a maximum observed speedup of more than 7-fold times. Therefore, the GPU is a good approach to accelerate the heat and mass transfer simulation.
基金supported by the National Science Foundation for Distinguished Young Scholars of China under Grant No.21225628the Science Fund for Creative Research Groups of the National Natural Science Foundation of China under Grant No.20821092+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No.XDA07080100the National Natural Science Foundation of China under Grant No. 21206167
文摘A multi-scale hardware and software architecture implementing the EMMS （energy-minimization multi-scale） paradigm is proven to be effective in the simulation of a two-dimensional gas-solid suspension. General purpose CPUs are employed for macro-scale control and optimization, and many integrated cores （MlCs） operating in multiple-instruction multiple-data mode are used for a molecular dynamics simulation of the solid particles at the meso-scale. Many cores operating in single-instruction multiple- data mode, such as general purpose graphics processing units （GPGPUs）, are employed for direct numerical simulation of the fluid flow at the micro-scale using the lattice Boltzmann method. This architecture is also expected to be efficient for the multi-scale simulation of other comolex systems.
基金the National Natural Science Foundation of China(Nos.61572508,61272144,61303065and 61202121)the National High Technology Research and Development Program(863)of China(No.2012AA010905)+2 种基金the Research Project of National University of Defense Technology(No.JC13-06-02)the Doctoral Fund of Ministry of Education of China(No.20134307120028)the Research Fund for the Doctoral Program of Higher Education of China(No.20114307120013)
文摘The simulation is an important means of performance evaluation of the computer architecture. Nowadays, the serial simulation of general purpose graphics processing unit(GPGPU) architecture is the main bottleneck for the simulation speed. To address this issue, we propose the intra-kernel parallelization on a multicore processor and the inter-kernel parallelization on a multiple-machine platform. We apply these two methods to the GPGPU-sim simulator. The intra-kernel parallelization method firstly parallelizes the serial simulation of multiple compute units in one cycle. Then it parallelizes the timing and functional simulation to reduce the performance loss caused by the synchronization between different compute units. The inter-kernel parallelization method divides multiple kernels of a CUDA program into several groups and distributes these groups across multiple simulation hosts to perform the simulation. Experimental results show that the intra-kernel parallelization method achieves a speed-up of up to 12 with a maximum error rate of 0.009 4% on a 32-core machine, and the inter-kernel parallelization method can accelerate the simulation by a factor of up to 3.9 with a maximum error rate of 0.11% on four simulation hosts. The orthogonality between these two methods allows us to combine them together on multiple multi-core hosts to get further performance improvements.
文摘矩阵主特征向量(principal eigenvectors computing,PEC)的求解是科学与工程计算中的一个重要问题。随着图形处理单元通用计算(general-purpose computing on graphics pro cessing unit,GPGPU)的兴起,利用GPU来优化大规模稀疏矩阵的图形处理单元求解得到了广泛关注。分别从应用特征和GPU体系结构特征两方面分析了PEC运算的性能瓶颈,提出了一种面向GPU的稀疏矩阵存储格式——GPU-ELL和一个针对GPU的线程优化映射策略,并设计了相应的PEC优化执行算法。在ATI HD Radeon5850上的实验结果表明,相对于传统CPU,该方案获得了最多200倍左右的加速,相对于已有GPU上的实现,也获得了2倍的加速。
文摘Alias – Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors.However, as the mesh complexity gets higher and denser, the files become larger and slower to import.This paper explores the use of GPUs to accelerate the importing and parsing of OBJ files by studying file read-time, runtime, and load resistance. We propose a new method of reading and parsing that circumvents GPU architecture limitations and improves performance, seeing the new GPU method outperforms CPU methods with a 6×– 8× speedup. When running on a heavily loaded system, the new method only received an 80% performance hit, compared to the160% that the CPU methods received. The loaded GPU speedup compared to unloaded CPU methods was3.5×, and, when compared to loaded CPU methods,8×. These results demonstrate that the time is right for further research into the use of data-parallel GPU acceleration beyond that of computer graphics and high performance computing.
