This exploration aims to improve the measurement accuracy of the electric energy meter under dynamic load conditions, and try to minimize the dynamic error. With the popularization of energy-saving and environmental p...
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This exploration aims to improve the measurement accuracy of the electric energy meter under dynamic load conditions, and try to minimize the dynamic error. With the popularization of energy-saving and environmental protection concepts, exploring the dynamic power error of electric meters in the electric field becomes the basis for the reliable, economical, and efficient operation of smart grids. According to the actual dynamic load working mode in the electric power field, a Ternary Amplitude Shift Keying (task) dynamic test current and dynamic test power model are designed. Then, a test system for smart meter dynamic error is constructed based on the task algorithm and the model designed above. Furthermore, different sample meters are used to simulate and analyze the dynamic power error test performance of the error test system. The unidirectional dynamic power error of each sample meter is analyzed. Results demonstrate that the unidirectional power error of the foreign factory's sample meter is less than 0.2% when the test current is 2.5A. The bi-directional power analysis suggests that only the dynamic error of the foreign factory's sample meter is within the error range of 0.2%. The construction scheme of the dynamic error test system for electric energy meters is achieved to solve the dynamic error test problem under the bidirectional power test model oriented to time process. This system provides the basis for the development of the power field.
task-based programming models are considered one of the most promising programming model approaches for exascale supercomputers because of their ability to dynamically react to changing conditions and reassign work to...
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ISBN:
(纸本)9781467365987
task-based programming models are considered one of the most promising programming model approaches for exascale supercomputers because of their ability to dynamically react to changing conditions and reassign work to processing elements. One question, however, remains unsolved: what should the task granularity of task-based applications be? Finegrained tasks offer more opportunities to balance the system and generally result in higher system utilization. However, they also induce in large scheduling overhead. The impact of scheduling overhead on coarse-grained tasks is lower, but large systems may result imbalanced and underutilized. In this work we propose a methodology to analyze the interplay between application task granularity and scheduling overhead. Our methodology is based on three main points: 1) a novel task algorithm that analyzes an application directed acyclic graph (DAG) and aggregates tasks;2) a fast and precise emulator to analyze the application behavior on systems with up to 1,024 cores;3) a comprehensive sensitivity analysis of application performance and scheduling overhead breakdown. Our results show that there is an optimal task granularity between 1.2x10(4) and 10x10(4) cycles for the representative schedulers. Moreover, our analysis indicates that a suitable scheduler for exascale task-based applications should employ a best-effort local scheduler and a sophisticated remote scheduler to move tasks across worker threads.
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