Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated var...Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated variables at the same time.However,existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable,not dealing with the redundant information among these variables.For space environment volume data with multi-correlated variables,based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables.The volume data associated with each variable is divided into disjoint blocks of size 43 initially.The blocks are represented as two levels,a mean level and a detail level.The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels,a splitting based on a principal component analysis is applied to compute initial codebooks.Then,LBG algorithm is conducted for codebook refinement and quantization.We further take advantage of progressive rendering based on GPU for real-time interactive visualization.Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data,including>5,>10,>30 and>50 MeV integrated proton flux.The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression,achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.展开更多
Graphics processing units （GPU） have taken an important role in the general purpose computing market in recent years. At present, the common approach to programming GPU units is to write CPU specific code with low l...Graphics processing units （GPU） have taken an important role in the general purpose computing market in recent years. At present, the common approach to programming GPU units is to write CPU specific code with low level GPU APIs such as CUDA. Although this approach can achieve good performance, it creates serious portability issues as programmers are required to write a specific version of the code for each potential target architecture. This results in high development and maintenance costs. We believe it is desirable to have a programming model which provides source code portability between CPUs and GPUs, as well as different GPUs. This would allow programmers to write one version of the code, which can be compiled and executed on either CPUs or GPUs efficiently without modification. In this paper, we propose MapCG, a MapReduce framework to provide source code level portability between CPUs and GPUs. In contrast to other approaches such as OpenCL, our framework, based on MapReduce, provides a high level programming model and makes programming much easier. We describe the design of MapCG, including the MapReduce-style high-level programming framework and the runtime system on the CPU and GPU. A prototype of the MapCG runtime, supporting multi-core CPUs and NVIDIA GPUs, was implemented. Our experimental results show that this implementation can execute the same source code efficiently on multi-core CPU platforms and GPUs, achieving an average speedup of 1.6-2.5x over previous implementations of MapReduce on eight commonly used applications.展开更多
The accurate simulation of turbulence and the implementation of corresponding turbulence models are both critical to the understanding of the complex physics behind turbulent flows in a variety of science and engineer...The accurate simulation of turbulence and the implementation of corresponding turbulence models are both critical to the understanding of the complex physics behind turbulent flows in a variety of science and engineering applications.Despite the tremendous increase in the computing power of central processing units(CPUs),direct numerical simulation of highly turbulent flows is still not feasible due to the need for resolving the smallest length scale,and today’s CPUs cannot keep pace with demand.The recent development of graphics processing units(GPU)has led to the general improvement in the performance of various algorithms.This study investigates the applicability of GPU technology in the context of fast-Fourier transform(FFT)-based pseudo-spectral methods for DNS of turbulent flows for the Taylor–Green vortex problem.They are implemented on a single GPU and a speedup of unto 31x is obtained in comparison to a single CPU.展开更多
基金the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated variables at the same time.However,existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable,not dealing with the redundant information among these variables.For space environment volume data with multi-correlated variables,based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables.The volume data associated with each variable is divided into disjoint blocks of size 43 initially.The blocks are represented as two levels,a mean level and a detail level.The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels,a splitting based on a principal component analysis is applied to compute initial codebooks.Then,LBG algorithm is conducted for codebook refinement and quantization.We further take advantage of progressive rendering based on GPU for real-time interactive visualization.Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data,including>5,>10,>30 and>50 MeV integrated proton flux.The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression,achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.
基金supported by the National Natural Science Foundation of China under Grant No. 60973143the National High Technology Research and Development 863 Program of China under Grant No. 2008AA01A201the National Basic Research 973 Program of China under Grant No. 2007CB310900
文摘Graphics processing units （GPU） have taken an important role in the general purpose computing market in recent years. At present, the common approach to programming GPU units is to write CPU specific code with low level GPU APIs such as CUDA. Although this approach can achieve good performance, it creates serious portability issues as programmers are required to write a specific version of the code for each potential target architecture. This results in high development and maintenance costs. We believe it is desirable to have a programming model which provides source code portability between CPUs and GPUs, as well as different GPUs. This would allow programmers to write one version of the code, which can be compiled and executed on either CPUs or GPUs efficiently without modification. In this paper, we propose MapCG, a MapReduce framework to provide source code level portability between CPUs and GPUs. In contrast to other approaches such as OpenCL, our framework, based on MapReduce, provides a high level programming model and makes programming much easier. We describe the design of MapCG, including the MapReduce-style high-level programming framework and the runtime system on the CPU and GPU. A prototype of the MapCG runtime, supporting multi-core CPUs and NVIDIA GPUs, was implemented. Our experimental results show that this implementation can execute the same source code efficiently on multi-core CPU platforms and GPUs, achieving an average speedup of 1.6-2.5x over previous implementations of MapReduce on eight commonly used applications.
文摘The accurate simulation of turbulence and the implementation of corresponding turbulence models are both critical to the understanding of the complex physics behind turbulent flows in a variety of science and engineering applications.Despite the tremendous increase in the computing power of central processing units(CPUs),direct numerical simulation of highly turbulent flows is still not feasible due to the need for resolving the smallest length scale,and today’s CPUs cannot keep pace with demand.The recent development of graphics processing units(GPU)has led to the general improvement in the performance of various algorithms.This study investigates the applicability of GPU technology in the context of fast-Fourier transform(FFT)-based pseudo-spectral methods for DNS of turbulent flows for the Taylor–Green vortex problem.They are implemented on a single GPU and a speedup of unto 31x is obtained in comparison to a single CPU.
