气象资料是开展气象预报、气象服务的根基。针对省级气象资料传输切换到CIMISS(China Integrated Meteorological Information Service System)系统后市县级缺乏有效资料监控手段的问题,在分析气象资料传输流程的基础上,本文给出气象资...气象资料是开展气象预报、气象服务的根基。针对省级气象资料传输切换到CIMISS(China Integrated Meteorological Information Service System)系统后市县级缺乏有效资料监控手段的问题,在分析气象资料传输流程的基础上,本文给出气象资料传输监控平台的总体设计方案,提出利用时间单元偏移的方法来进行监控日志采集,完成数据库设计方案以及平台展示方案设计。监控平台采用MVC框架以及HTML、AJAX等技术实现气象资料的传输监控和质量统计等功能。平台面向省市县三级用户,为改进省级气象资料的传输质量提供可靠的技术保障。展开更多
The most popular hardware used for parallel depth migration is the PC-Cluster but its application is limited due to large space occupation and high power consumption. In this paper, we introduce a new hardware archite...The most popular hardware used for parallel depth migration is the PC-Cluster but its application is limited due to large space occupation and high power consumption. In this paper, we introduce a new hardware architecture, based on which the finite difference （FD） wavefield-continuation depth migration can be conducted using the Graphics Processing Unit （GPU） as a CPU coprocessor. We demonstrate the program module and three key optimization steps for implementing FD depth migration： memory, thread structure, and instruction optimizations and consider evaluation methods for the amount of optimization. 2D and 3D models are used to test depth migration on the GPU. The tested results show that the depth migration computational efficiency greatly increased using the general-purpose GPU, increasing by at least 25 times compared to the AMD 2.5 GHz CPU.展开更多
文摘气象资料是开展气象预报、气象服务的根基。针对省级气象资料传输切换到CIMISS(China Integrated Meteorological Information Service System)系统后市县级缺乏有效资料监控手段的问题,在分析气象资料传输流程的基础上,本文给出气象资料传输监控平台的总体设计方案,提出利用时间单元偏移的方法来进行监控日志采集,完成数据库设计方案以及平台展示方案设计。监控平台采用MVC框架以及HTML、AJAX等技术实现气象资料的传输监控和质量统计等功能。平台面向省市县三级用户,为改进省级气象资料的传输质量提供可靠的技术保障。
基金supported by the National Natural Science Foundation of China (Nos. 41104083 and 40804024) Fundamental Research Funds for the Central Universities (No, 2011YYL022)
文摘The most popular hardware used for parallel depth migration is the PC-Cluster but its application is limited due to large space occupation and high power consumption. In this paper, we introduce a new hardware architecture, based on which the finite difference （FD） wavefield-continuation depth migration can be conducted using the Graphics Processing Unit （GPU） as a CPU coprocessor. We demonstrate the program module and three key optimization steps for implementing FD depth migration： memory, thread structure, and instruction optimizations and consider evaluation methods for the amount of optimization. 2D and 3D models are used to test depth migration on the GPU. The tested results show that the depth migration computational efficiency greatly increased using the general-purpose GPU, increasing by at least 25 times compared to the AMD 2.5 GHz CPU.
