In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularl...In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.展开更多
The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In...The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In this paper, we present an improved sequential algorithm which is based on a strict alternation of Generation and Exploration execution modes as well as Depth-First/Best-First hybrid strategies. The experimental results show that the proposed scheme exhibits improved performance compared with the algorithm in [1]. More importantly, our method can be easily extended and implemented with lightweight threads to speed up the execution times. Good speedups can be obtained on shared-memory multicore systems.展开更多
A Variable-driven model of AND-parallelism of logic programs isprcscntcd.It statically analyses the values of variables in clauses and picks out the varia.blcs contributing to the parallel execution and then generates...A Variable-driven model of AND-parallelism of logic programs isprcscntcd.It statically analyses the values of variables in clauses and picks out the varia.blcs contributing to the parallel execution and then generates the variable-driving graphsfor clauses.According to the variable-driving graph and the analysis of the instantiationsof variables at run,literals are driven to execute.With binding conflicts of shared variablesprevented,the variable-driven model fully develops the AND-parallelism.Based on thevariable-driving graph,somc models of AND-parallelism already put forward can beavailable if cquipcd with appropriate driving algorithms.展开更多
In this paper, according to the parallel environment of ELXSI computer, a parallel solving process of substructure method in static and dynamic analyses of large-scale and complex structure has been put forward, and t...In this paper, according to the parallel environment of ELXSI computer, a parallel solving process of substructure method in static and dynamic analyses of large-scale and complex structure has been put forward, and the corresponding parallel computational program has been developed.展开更多
A neruon-oriented programming system based on parallel neural information processing has been presented. With the neural programming system built upon 4～8 process elements(TMS C30), the system has thus provided users...A neruon-oriented programming system based on parallel neural information processing has been presented. With the neural programming system built upon 4～8 process elements(TMS C30), the system has thus provided users high speed, general purpose and large scale neural network application development platforms etc.展开更多
With many cores driven by high memory bandwidth, today＇s graphics processing unit （GPU） has involved into an absolute computing workhorse. More and more scientists, researchers and software developers are using GPU...With many cores driven by high memory bandwidth, today＇s graphics processing unit （GPU） has involved into an absolute computing workhorse. More and more scientists, researchers and software developers are using GPUs to accelerate their algorithms and ap- plications. Developing complex programs and software on the GPU, however, is still far from easy with ex- isting tools provided by hardware vendors. This article introduces our recent research efforts to make GPU soft- ware development much easier. Specifically, we designed BSGP, a high-level programming language for general- purpose computation on the GPU. A BSGP program looks much the same as a sequential C program, and is thus easy to read, write and maintain. Its performance on the GPU is guaranteed by a well-designed compiler that converts the program to native GPU code. We also developed an effective debugging system for BSGP pro- grams based on the GPU interrupt, a unique feature of BSGP that allows calling CPU functions from inside GPU code. Moreover, using BSGP, we developed GPU algorithms for constructing several widely-used spatial hierarchies for high-performance graphics applications.展开更多
文摘In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.
文摘The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In this paper, we present an improved sequential algorithm which is based on a strict alternation of Generation and Exploration execution modes as well as Depth-First/Best-First hybrid strategies. The experimental results show that the proposed scheme exhibits improved performance compared with the algorithm in [1]. More importantly, our method can be easily extended and implemented with lightweight threads to speed up the execution times. Good speedups can be obtained on shared-memory multicore systems.
文摘A Variable-driven model of AND-parallelism of logic programs isprcscntcd.It statically analyses the values of variables in clauses and picks out the varia.blcs contributing to the parallel execution and then generates the variable-driving graphsfor clauses.According to the variable-driving graph and the analysis of the instantiationsof variables at run,literals are driven to execute.With binding conflicts of shared variablesprevented,the variable-driven model fully develops the AND-parallelism.Based on thevariable-driving graph,somc models of AND-parallelism already put forward can beavailable if cquipcd with appropriate driving algorithms.
文摘In this paper, according to the parallel environment of ELXSI computer, a parallel solving process of substructure method in static and dynamic analyses of large-scale and complex structure has been put forward, and the corresponding parallel computational program has been developed.
文摘A neruon-oriented programming system based on parallel neural information processing has been presented. With the neural programming system built upon 4～8 process elements(TMS C30), the system has thus provided users high speed, general purpose and large scale neural network application development platforms etc.
文摘With many cores driven by high memory bandwidth, today＇s graphics processing unit （GPU） has involved into an absolute computing workhorse. More and more scientists, researchers and software developers are using GPUs to accelerate their algorithms and ap- plications. Developing complex programs and software on the GPU, however, is still far from easy with ex- isting tools provided by hardware vendors. This article introduces our recent research efforts to make GPU soft- ware development much easier. Specifically, we designed BSGP, a high-level programming language for general- purpose computation on the GPU. A BSGP program looks much the same as a sequential C program, and is thus easy to read, write and maintain. Its performance on the GPU is guaranteed by a well-designed compiler that converts the program to native GPU code. We also developed an effective debugging system for BSGP pro- grams based on the GPU interrupt, a unique feature of BSGP that allows calling CPU functions from inside GPU code. Moreover, using BSGP, we developed GPU algorithms for constructing several widely-used spatial hierarchies for high-performance graphics applications.
文摘为研究同轴并联式混合动力汽车的能量管理策略,建立了同轴并联式动力系统动态方程,分析了转矩需求无后效性的马尔可夫特性.在维持电池容量不变的条件下,以燃油消耗最小为优化目标,采用马尔可夫决策实施能量管理策略,并采用策略迭代方法求解了马尔可夫能量管理的转矩决策过程,在J1015工况和昆明工况进行了仿真,实现了能量管理的在线实施.结果表明,与基于动态规划的能量管理策略相比,马尔可夫决策的能量管理策略能在线实施,且电池容量变化更为平稳;在燃料消耗方面是全局次优的,在J1015行驶工况下100 km燃油消耗增加了1.32 L,在昆明行驶工况下100 km燃油消耗增加了1.59 L.
