Numerical treatment of engineering application problems often eventually results in a solution of systems of linear or nonlinear equations.The solution process using digital computational devices usually takes tremend...Numerical treatment of engineering application problems often eventually results in a solution of systems of linear or nonlinear equations.The solution process using digital computational devices usually takes tremendous time due to the extremely large size encountered in most real-world engineering applications.So,practical solvers for systems of linear and nonlinear equations based on multi graphic process units（GPUs）are proposed in order to accelerate the solving process.In the linear and nonlinear solvers,the preconditioned bi-conjugate gradient stable（PBi-CGstab）method and the Inexact Newton method are used to achieve the fast and stable convergence behavior.Multi-GPUs are utilized to obtain more data storage that large size problems need.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
The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the fi...The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the first research aimed at FPGA-based as well as GPGPU-based accelerator design.This paper quantitatively analyzes the workload,computational intensity and memory performance of a single-particle 3D reconstruction application,called EMAN,and parallelizes it on CUDA GPGPU architectures and decouples the memory operations from the computing flow and orchestrates the thread-data mapping to reduce the overhead of off-chip memory operations.Then it exploits the trend towards FPGA-based accelerator design,which is achieved by offloading computingintensive kernels to dedicated hardware modules.Furthermore,a customized memory subsystem is also designed to facilitate the decoupling and optimization of computing dominated data access patterns.This paper evaluates the proposed accelerator design strategies by comparing it with a parallelized program on a 4-cores CPU.The CUDA version on a GTX480 shows a speedup of about 6 times.The performance of the stream architecture implemented on a Xilinx Virtex LX330 FPGA is justified by the reported speedup of 2.54 times.Meanwhile,measured in terms of power efficiency,the FPGA-based accelerator outperforms a 4-cores CPU and a GTX480 by 7.3 times and 3.4 times,respectively.展开更多
An efficient computing framework,namely PFlows,for fully resolved-direct numerical simulations of particle-laden flows was accelerated on NVIDIA General Processing Units(GPUs)and GPU-like accelerator(DCU)cards.The fra...An efficient computing framework,namely PFlows,for fully resolved-direct numerical simulations of particle-laden flows was accelerated on NVIDIA General Processing Units(GPUs)and GPU-like accelerator(DCU)cards.The framework is featured as coupling the lattice Boltzmann method for fluid flow with the immersed boundary method for fluid-particle interaction,and the discrete element method for particle collision,using two fixed Eulerian meshes and one moved Lagrangian point mesh,respectively.All the parts are accelerated by a fine-grained parallelism technique using CUDA on GPUs,and further using HIP on DCU cards,i.e.,the calculation on each fluid grid,each immersed boundary point,each particle motion,and each pair-particle collision is responsible by one computer thread,respectively.Coalesced memory accesses to LBM distribution functions with the data layout of Structure of Arrays are used to maximize utilization of hardware bandwidth.Parallel reduction with shared memory for data of immersed boundary points is adopted for the sake of reducing access to global memory when integrate particle hydrodynamic force.MPI computing is further used for computing on heterogeneous architectures with multiple CPUs-GPUs/DCUs.The communications between adjacent processors are hidden by overlapping with calculations.Two benchmark cases were conducted for code validation,including a pure fluid flow and a particle-laden flow.The performances on a single accelerator show that a GPU V100 can achieve 7.1–11.1 times speed up,while a single DCU can achieve 5.6–8.8 times speed up compared to a single Xeon CPU chip(32 cores).The performances on multi-accelerators show that parallel efficiency is 0.5–0.8 for weak scaling and 0.68–0.9 for strong scaling on up to 64 DCU cards even for the dense flow(φ=20%).The peak performance reaches 179 giga lattice updates per second(GLUPS)on 256 DCU cards by using 1 billion grids and 1 million particles.At last,a large-scale simulation of a gas-solid flow with 1.6 billion grids and 1.6 million particles was conducted using only 32 DCU cards.This simulation shows that the present framework is prospective for simulations of large-scale particle-laden flows in the upcoming exascale computing era.展开更多
The purpose of this study was to promote the development of large-scale agricultural machines in China and meet the demand of air seeder localization.This study investigated the relationship between the working parame...The purpose of this study was to promote the development of large-scale agricultural machines in China and meet the demand of air seeder localization.This study investigated the relationship between the working parameters and the performance of pneumatic seeding system,Cangmai 6004 wheat seed was used.A test platform for pneumatic seeding systems was developed,and then a series of experiments were performed based on the quadratic general rotary unitized design and response surface methodology(RSM).The seeding rate and the air flow rate were selected as affecting factors,coefficient of variation(CV)of evenness of feeding rate between rows and CV of seeding stability of total rows were assigned as the test indexes.Regression models between factors and indexes were established,and finally,an optimal equation based on this pneumatic seeding system were established as well,which can determine the proper air flow rate once the seeding rate was set based on the practical agronomic requirements.For example,when the seeding rate is set as 250 kg/hm^(2),the proper air flow rate of 7.53 m3/min can be obtained.The verification experiment results showed that the predicted working parameters obtained by RSM were feasible,which might provide a theoretical basis for further research of pneumatic seed metering systems.展开更多
Axial air-assisted sprayers can distribute pesticides efficiently in kiwifruit orchards.Because of improper parameter settings,most sprayers deliver either too much or too little pesticide.To identify appropriate spra...Axial air-assisted sprayers can distribute pesticides efficiently in kiwifruit orchards.Because of improper parameter settings,most sprayers deliver either too much or too little pesticide.To identify appropriate sprayer parameters for kiwifruit trees,the vertical distribution profiles of the applied liquid spray were examined in this study.The effects of spray fan speed(SFS),spray pressure(SP)and spray distance(SD)on the distributions of the sprayed liquid in the vertical profiles were studied.Combined actions of the above parameters were systematically analysed using the quadratic general rotary design test method.Regression equations for the spray liquid distributions and working factors are presented.Field confirmation experiments were carried out to optimize the parameters.Data analysis showed that the optional sprayer working parameters are those of Group 3,with an SFS equal to 1900 r/min and SP equal to 3.25 MPa.The results of this study provide a reference for future applications of this type of axial air-assisted sprayer in kiwifruit orchards.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
General purpose graphics processing units(GPGPUs)can be used to improve computing performance considerably for regular applications.However,irregular memory access exists in many applications,and the benefits of graph...General purpose graphics processing units(GPGPUs)can be used to improve computing performance considerably for regular applications.However,irregular memory access exists in many applications,and the benefits of graphics processing units(GPUs)are less substantial for irregular applications.In recent years,several studies have presented some solutions to remove static irregular memory access.However,eliminating dynamic irregular memory access with software remains a serious challenge.A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access,especially for indirect memory access.Data reordering and index redirection are suggested to reduce the number of memory transactions,thereby improving the performance of GPU kernels.To improve the efficiency of data reordering,an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data.Through concurrently executing the compute unified device architecture(CUDA)streams of data reordering and the data processing kernel,the overhead of data reordering can be reduced.After these optimizations,the volume of memory transactions can be reduced by 16.7%-50%compared with CUSPARSE-based benchmarks,and the performance of irregular kernels can be improved by 9.64%-34.9%using an NVIDIA Tesla P4 GPU.展开更多
This paper describes a parallel fast convolution back-projection algorithm design for radar image reconstruction. State-of-the-art general purpose graphic processing units （GPGPU） were utilized to accelerate the pro...This paper describes a parallel fast convolution back-projection algorithm design for radar image reconstruction. State-of-the-art general purpose graphic processing units （GPGPU） were utilized to accelerate the processing. The implementation achieves much better performance than conventional processing systems, with a speedup of more than 890 times on NVIDIA Tesla C1060 supercomputing cards compared to an Intel P4 2.4 GHz CPU. 256×256 pixel images could be reconstructed within 6.3 s, which makes real-time imaging possible. Six platforms were tested and compared. The results show that the GPGPU super-computing system has great potential for radar image processing.展开更多
文摘Numerical treatment of engineering application problems often eventually results in a solution of systems of linear or nonlinear equations.The solution process using digital computational devices usually takes tremendous time due to the extremely large size encountered in most real-world engineering applications.So,practical solvers for systems of linear and nonlinear equations based on multi graphic process units（GPUs）are proposed in order to accelerate the solving process.In the linear and nonlinear solvers,the preconditioned bi-conjugate gradient stable（PBi-CGstab）method and the Inexact Newton method are used to achieve the fast and stable convergence behavior.Multi-GPUs are utilized to obtain more data storage that large size problems need.
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.
基金Supported by the National Basic Research Program of China(No.2012CB316502)the National High Technology Research and DevelopmentProgram of China(No.2009AA01A129)the National Natural Science Foundation of China(No.60921002)
文摘The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the first research aimed at FPGA-based as well as GPGPU-based accelerator design.This paper quantitatively analyzes the workload,computational intensity and memory performance of a single-particle 3D reconstruction application,called EMAN,and parallelizes it on CUDA GPGPU architectures and decouples the memory operations from the computing flow and orchestrates the thread-data mapping to reduce the overhead of off-chip memory operations.Then it exploits the trend towards FPGA-based accelerator design,which is achieved by offloading computingintensive kernels to dedicated hardware modules.Furthermore,a customized memory subsystem is also designed to facilitate the decoupling and optimization of computing dominated data access patterns.This paper evaluates the proposed accelerator design strategies by comparing it with a parallelized program on a 4-cores CPU.The CUDA version on a GTX480 shows a speedup of about 6 times.The performance of the stream architecture implemented on a Xilinx Virtex LX330 FPGA is justified by the reported speedup of 2.54 times.Meanwhile,measured in terms of power efficiency,the FPGA-based accelerator outperforms a 4-cores CPU and a GTX480 by 7.3 times and 3.4 times,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.51876075)supported by Wuhan Supercomputer Center in China。
文摘An efficient computing framework,namely PFlows,for fully resolved-direct numerical simulations of particle-laden flows was accelerated on NVIDIA General Processing Units(GPUs)and GPU-like accelerator(DCU)cards.The framework is featured as coupling the lattice Boltzmann method for fluid flow with the immersed boundary method for fluid-particle interaction,and the discrete element method for particle collision,using two fixed Eulerian meshes and one moved Lagrangian point mesh,respectively.All the parts are accelerated by a fine-grained parallelism technique using CUDA on GPUs,and further using HIP on DCU cards,i.e.,the calculation on each fluid grid,each immersed boundary point,each particle motion,and each pair-particle collision is responsible by one computer thread,respectively.Coalesced memory accesses to LBM distribution functions with the data layout of Structure of Arrays are used to maximize utilization of hardware bandwidth.Parallel reduction with shared memory for data of immersed boundary points is adopted for the sake of reducing access to global memory when integrate particle hydrodynamic force.MPI computing is further used for computing on heterogeneous architectures with multiple CPUs-GPUs/DCUs.The communications between adjacent processors are hidden by overlapping with calculations.Two benchmark cases were conducted for code validation,including a pure fluid flow and a particle-laden flow.The performances on a single accelerator show that a GPU V100 can achieve 7.1–11.1 times speed up,while a single DCU can achieve 5.6–8.8 times speed up compared to a single Xeon CPU chip(32 cores).The performances on multi-accelerators show that parallel efficiency is 0.5–0.8 for weak scaling and 0.68–0.9 for strong scaling on up to 64 DCU cards even for the dense flow(φ=20%).The peak performance reaches 179 giga lattice updates per second(GLUPS)on 256 DCU cards by using 1 billion grids and 1 million particles.At last,a large-scale simulation of a gas-solid flow with 1.6 billion grids and 1.6 million particles was conducted using only 32 DCU cards.This simulation shows that the present framework is prospective for simulations of large-scale particle-laden flows in the upcoming exascale computing era.
基金the Chinese Academy of Agricultural Mechanization Sciences,State Key Laboratory of Soil Plant Machinery System Technology,and the supports of the Rural Areas National Key Technology R&D Program during the Twelfth Five-year Plan Period-Development of Farm Work Equipment Matched on Large Horsepower Tractors(2011BAD20B03).
文摘The purpose of this study was to promote the development of large-scale agricultural machines in China and meet the demand of air seeder localization.This study investigated the relationship between the working parameters and the performance of pneumatic seeding system,Cangmai 6004 wheat seed was used.A test platform for pneumatic seeding systems was developed,and then a series of experiments were performed based on the quadratic general rotary unitized design and response surface methodology(RSM).The seeding rate and the air flow rate were selected as affecting factors,coefficient of variation(CV)of evenness of feeding rate between rows and CV of seeding stability of total rows were assigned as the test indexes.Regression models between factors and indexes were established,and finally,an optimal equation based on this pneumatic seeding system were established as well,which can determine the proper air flow rate once the seeding rate was set based on the practical agronomic requirements.For example,when the seeding rate is set as 250 kg/hm^(2),the proper air flow rate of 7.53 m3/min can be obtained.The verification experiment results showed that the predicted working parameters obtained by RSM were feasible,which might provide a theoretical basis for further research of pneumatic seed metering systems.
基金The authors acknowledge that this work was financially supported by the National Key Research and Development Program(No.2016YFD0200700)the National Key R&D Program of China“the 13th Five-Year Plan”(Grant No.2016YFD0700503)the Science and Technology Project of Shaanxi Province.Also,we thank the critical comments and suggestions from the anonymous reviewers for improving the manuscript.
文摘Axial air-assisted sprayers can distribute pesticides efficiently in kiwifruit orchards.Because of improper parameter settings,most sprayers deliver either too much or too little pesticide.To identify appropriate sprayer parameters for kiwifruit trees,the vertical distribution profiles of the applied liquid spray were examined in this study.The effects of spray fan speed(SFS),spray pressure(SP)and spray distance(SD)on the distributions of the sprayed liquid in the vertical profiles were studied.Combined actions of the above parameters were systematically analysed using the quadratic general rotary design test method.Regression equations for the spray liquid distributions and working factors are presented.Field confirmation experiments were carried out to optimize the parameters.Data analysis showed that the optional sprayer working parameters are those of Group 3,with an SFS equal to 1900 r/min and SP equal to 3.25 MPa.The results of this study provide a reference for future applications of this type of axial air-assisted sprayer in kiwifruit orchards.
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Project supported by the National Key Research and Development Program of China(No.2018YFB1003500)。
文摘General purpose graphics processing units(GPGPUs)can be used to improve computing performance considerably for regular applications.However,irregular memory access exists in many applications,and the benefits of graphics processing units(GPUs)are less substantial for irregular applications.In recent years,several studies have presented some solutions to remove static irregular memory access.However,eliminating dynamic irregular memory access with software remains a serious challenge.A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access,especially for indirect memory access.Data reordering and index redirection are suggested to reduce the number of memory transactions,thereby improving the performance of GPU kernels.To improve the efficiency of data reordering,an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data.Through concurrently executing the compute unified device architecture(CUDA)streams of data reordering and the data processing kernel,the overhead of data reordering can be reduced.After these optimizations,the volume of memory transactions can be reduced by 16.7%-50%compared with CUSPARSE-based benchmarks,and the performance of irregular kernels can be improved by 9.64%-34.9%using an NVIDIA Tesla P4 GPU.
文摘This paper describes a parallel fast convolution back-projection algorithm design for radar image reconstruction. State-of-the-art general purpose graphic processing units （GPGPU） were utilized to accelerate the processing. The implementation achieves much better performance than conventional processing systems, with a speedup of more than 890 times on NVIDIA Tesla C1060 supercomputing cards compared to an Intel P4 2.4 GHz CPU. 256×256 pixel images could be reconstructed within 6.3 s, which makes real-time imaging possible. Six platforms were tested and compared. The results show that the GPGPU super-computing system has great potential for radar image processing.
