Power consumption-aware parallel application scheduling system and method for hybrid computing environment

Abstract

The invention discloses a power consumption sensing scheduling system and a power consumption sensing scheduling method for parallel application for hybrid computation environments. The power consumption sensing scheduling system comprises a user layer, a scheduling layer and a resource layer. The user layer transmits user requests to the scheduling layer, the scheduling layer transmits execution tasks and data required by the execution tasks to the resource layer and comprises an analysis module, a task clustering module, a processing unit selection analysis module and a task distribution module, analysis results of the analysis module are transmitted to the task clustering module, clustering results of the task clustering module are transmitted to the processing unit selection analysis module, the processing unit selection analysis module comprises a time computation module and a power consumption computation module, selection analysis results of the processing unit selection analysis module are transmitted to the task distribution module, and the resource layer comprises a plurality of DVS (dynamic voltage scaling) processing units and a plurality of non-DVS processing units. The power consumption sensing scheduling system and the power consumption sensing scheduling method have the advantages that DVS and non-DVS hybrid characteristics of the system are taken into consideration on the premise that application execution time minimization is a scheduling task, and execution power consumption of the application is reduced to the greatest extent.

Description

technical field [0001] The invention relates to the technical field of energy-saving of high-performance computing software, in particular to a power consumption-aware parallel application scheduling system and method for hybrid computing environments. Background technique [0002] With the sharp reduction of computer hardware costs and the increasingly prominent advantages of Linux clusters, the scale of deployment of high-performance computing systems is increasing, but its huge energy consumption is far beyond people's imagination. According to statistics, the electricity cost of a supercomputing center that runs 1 billion times per second is nearly 4 million yuan per year; the cost of electricity consumed by a server within 3 years may exceed the original purchase cost of the server. The increase in power consumption not only increases the operating cost, but also shortens the life of the device directly due to the increase in the temperature of the device, reducing the ...

AI Technical Summary

Problems solved by technology

Even if some methods take into account the DVS / non-DVS hybrid nature of the system, they are oriented towards independent real-time tasks with arrival time, deadline and utilization constraints, rather than parallel applications with data dependencies

[0008] (2) Most methods ignore the optimization of communication energy consumption or the further reduction of computing energy consumption in the communication time period

[0009] (3) Most methods do not consider the static energy consumption optimization of the processing unit

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