Organic reefs, the targets of deep-water petro- leum exploration, developed widely in Xisha area. However, there are concealed igneous rocks undersea, to which organic rocks have nearly equal wave impedance. So the ig...Organic reefs, the targets of deep-water petro- leum exploration, developed widely in Xisha area. However, there are concealed igneous rocks undersea, to which organic rocks have nearly equal wave impedance. So the igneous rocks have become interference for future explo- ration by having similar seismic reflection characteristics. Yet, the density and magnetism of organic reefs are very different from igneous rocks. It has obvious advantages to identify organic reefs and igneous rocks by gravity and magnetic data. At first, frequency decomposition was applied to the free-air gravity anomaly in Xisha area to obtain the 2D subdivision of the gravity anomaly and magnetic anomaly in the vertical direction. Thus, the dis- tribution of igneous rocks in the horizontal direction can be acquired according to high-frequency field, low-frequency field, and its physical properties. Then, 3D forward model- ing of gravitational field was carried out to establish the density model of this area by reference to physical properties of rocks based on former researches. Furthermore, 3D inversion of gravity anomaly by genetic algorithm method of the graphic processing unit （GPU） parallel processing in Xisha target area was applied, and 3D density structure of this area was obtained. By this way, we can confine the igneous rocks to the certain depth according to the density of the igneous rocks. The frequency decomposition and 3D inversion of gravity anomaly by genetic algorithm method of the GPU parallel processing proved to be a useful method for recognizing igneous rocks to its 3D geological position. So organic reefs and igneous rocks can be identified, which provide a prescient information for further exploration.展开更多
Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining perform...Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.展开更多
在图形处理器(GPU)上实现对角稀疏矩阵向量乘法(SpMV)可以充分利用GPU的并行计算能力,并加速矩阵向量乘法;然而,相关主流算法存在零元填充数据多、计算效率低的问题。针对上述问题,提出一种对角SpMV算法DIA-Dynamic(DIAgonal-Dynamic)...在图形处理器(GPU)上实现对角稀疏矩阵向量乘法(SpMV)可以充分利用GPU的并行计算能力,并加速矩阵向量乘法;然而,相关主流算法存在零元填充数据多、计算效率低的问题。针对上述问题,提出一种对角SpMV算法DIA-Dynamic(DIAgonal-Dynamic)。首先,设计一种全新的动态划分策略,根据矩阵的不同特征进行分块,在保证GPU高计算效率的同时大幅减少零元填充,去除冗余计算量;其次,提出一种对角稀疏矩阵存储格式BDIA(Block DIAgonal)存储分块数据,并调整数据布局,提高GPU上的访存性能;最后,基于GPU的底层进行条件分支优化,以减少分支判断,并使用动态共享内存解决向量的不规则访问问题。DIA-Dynamic与前沿Tile SpMV算法相比,平均加速比达到了1.88;与前沿BRCSD(Diagonal Compressed Storage based on Row-Blocks)-Ⅱ算法相比,平均零元填充减少了43%,平均加速比达到了1.70。实验结果表明,DIA-Dynamic能够有效提高GPU上对角SpMV的计算效率,缩短计算时间,提升程序性能。展开更多
基金financially supported by the National Natural Science Foundation of China (No.41174085)
文摘Organic reefs, the targets of deep-water petro- leum exploration, developed widely in Xisha area. However, there are concealed igneous rocks undersea, to which organic rocks have nearly equal wave impedance. So the igneous rocks have become interference for future explo- ration by having similar seismic reflection characteristics. Yet, the density and magnetism of organic reefs are very different from igneous rocks. It has obvious advantages to identify organic reefs and igneous rocks by gravity and magnetic data. At first, frequency decomposition was applied to the free-air gravity anomaly in Xisha area to obtain the 2D subdivision of the gravity anomaly and magnetic anomaly in the vertical direction. Thus, the dis- tribution of igneous rocks in the horizontal direction can be acquired according to high-frequency field, low-frequency field, and its physical properties. Then, 3D forward model- ing of gravitational field was carried out to establish the density model of this area by reference to physical properties of rocks based on former researches. Furthermore, 3D inversion of gravity anomaly by genetic algorithm method of the graphic processing unit （GPU） parallel processing in Xisha target area was applied, and 3D density structure of this area was obtained. By this way, we can confine the igneous rocks to the certain depth according to the density of the igneous rocks. The frequency decomposition and 3D inversion of gravity anomaly by genetic algorithm method of the GPU parallel processing proved to be a useful method for recognizing igneous rocks to its 3D geological position. So organic reefs and igneous rocks can be identified, which provide a prescient information for further exploration.
基金This work was supported by the National Natural Science Foundation of China(62073155,62002137,62106088,62206113)the High-End Foreign Expert Recruitment Plan(G2023144007L)the Fundamental Research Funds for the Central Universities(JUSRP221028).
文摘Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.
文摘在图形处理器(GPU)上实现对角稀疏矩阵向量乘法(SpMV)可以充分利用GPU的并行计算能力,并加速矩阵向量乘法;然而,相关主流算法存在零元填充数据多、计算效率低的问题。针对上述问题,提出一种对角SpMV算法DIA-Dynamic(DIAgonal-Dynamic)。首先,设计一种全新的动态划分策略,根据矩阵的不同特征进行分块,在保证GPU高计算效率的同时大幅减少零元填充,去除冗余计算量;其次,提出一种对角稀疏矩阵存储格式BDIA(Block DIAgonal)存储分块数据,并调整数据布局,提高GPU上的访存性能;最后,基于GPU的底层进行条件分支优化,以减少分支判断,并使用动态共享内存解决向量的不规则访问问题。DIA-Dynamic与前沿Tile SpMV算法相比,平均加速比达到了1.88;与前沿BRCSD(Diagonal Compressed Storage based on Row-Blocks)-Ⅱ算法相比,平均零元填充减少了43%,平均加速比达到了1.70。实验结果表明,DIA-Dynamic能够有效提高GPU上对角SpMV的计算效率,缩短计算时间,提升程序性能。
