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Reservoir group scheduling parallel dynamic planning method considering computing resource economy and feasibility

A technology of computing resources and dynamic programming, which is applied in computer processing of hydrology and water resources data, computer scheduling of reservoir groups and parallel computing, and can solve problems such as reducing the computing time of DP method

Active Publication Date: 2018-09-28
CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

(2) Most of the research is based on shared storage or small parallel computing environments, and research applications in distributed storage or high-performance parallel computing environments still need to tap the potential
The parallel strategy of the master-slave mode can only reduce the calculation time of the DP method, ignoring the problem that the DP method may not be executed on a stand-alone or shared storage parallel computer due to excessive computing memory

Method used

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  • Reservoir group scheduling parallel dynamic planning method considering computing resource economy and feasibility
  • Reservoir group scheduling parallel dynamic planning method considering computing resource economy and feasibility
  • Reservoir group scheduling parallel dynamic planning method considering computing resource economy and feasibility

Examples

Experimental program
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Embodiment 1

[0073] This embodiment is a parallel dynamic planning method for reservoir group scheduling considering the economic and feasible computing resources. The distributed storage parallel computing system used in the method includes: a plurality of computing units connected through a network, and the computing unit includes There are multiple physical cores, memory and hard drives, such as figure 1 Shown.

[0074] Considering the huge amount of calculation, the method described in this implementation requires hundreds or thousands of processing units to form a distributed storage parallel computing environment. In order to reduce the footprint of the hardware system, when the number of processing units required is large, you can consider using hundreds or thousands of blade servers as processing units and connect them together through a network. See figure 1 .

[0075] In a distributed storage parallel computing environment, each processing unit exchanges information through a message ...

Embodiment 2

[0148] This embodiment is an improvement of the first embodiment, and is a refinement of the steps of the first embodiment regarding the calculation resource economy and feasibility analysis. The clock time τ in the step of calculating resource economy and feasibility analysis described in this embodiment K :

[0149] τ K =(τ′+τ″+τ″′) / K,

[0150] Where: τ K To use the clock time calculated by K peer processes, including calculating the fragment time τ′, τ′=m 2n ×Δτ×T, communication fragmentation time τ″, load imbalance time loss τ″′;

[0151] Memory RAM under the jurisdiction of a single peer process K :

[0152] RAM k =(m n ×3×Φ) / K,

[0153] Where: Φ is the storage space occupied by variables that do not distinguish between variable types;

[0154] Hard disk HDD controlled by a single peer process K :

[0155] HDD K =(m n ×T×Φ) / K.

Embodiment 3

[0157] This embodiment is an improvement of the first embodiment, and is a refinement of the steps performed in the first embodiment regarding the parallel dynamic programming operation. The sub-steps included in the steps of the parallel dynamic programming operation described in this embodiment are as follows:

[0158] The parallel dynamic programming operation includes two processes, the first process and the second process:

[0159] The first process, the process is like Figure 4 Shown:

[0160] (1) According to the principle of equal division, allocate computing memory and hard disk space for K peer processes.

[0161] This step is a key step for realizing distributed memory and hard disk fragmentation and dynamic access. Only through allocation can all memory and hard disk space be reasonably used to make it fully functional.

[0162] (2) When t=1, use k to represent any peer process, according to F 1 * (·)=0 and C(p 1 ,1), initialization with Among them: k=1,2,...,K.

[0163] ...

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Abstract

The invention relates to a reservoir group scheduling parallel dynamic planning method considering computing resource economy and feasibility. The method comprises the steps of starting; computing resource recognition; problem scale recognition; import of known data into a transit process and trial; analysis of computing resource economy and feasibility; judgment of computing resource and problemscale matching; import of the known data into peer processes; execution of parallel dynamic planning operation; export of a reservoir group scheduling result; and stop. According to the method, the "dimension disaster" problem of reservoir group scheduling is solved according to serial dynamic planning, distributed computing is adopted to cope with a "time disaster", distributed storage is adoptedto cope with a "memory disaster", and computing efficiency and availability are improved. From the perspective of economy, distributed memories, hard disk fragments and a dynamic access technology are utilized, hard disks replace memory space, and economical expenditure is substantially saved. From the perspective of feasibility, computing time and memory and hard disk space demands are given, matching between computing resources and the problem scale is prejudged and adjusted, and the situation that overloaded operation causes ineffective computing is avoided.

Description

Technical field [0001] The invention relates to a parallel dynamic planning method for reservoir group scheduling considering the economically feasible computing resources, a computer processing method for hydrology and water resources data, and a method for reservoir group computer scheduling and parallel calculation. Background technique [0002] Reservoir optimal dispatch generally needs to establish a mathematical model of the problem, determine the specific target of the problem, such as flood control, power generation, water supply, ecological dispatch, etc., adopt appropriate optimization solutions, and under the constraints of water balance, storage capacity, flow, and hydraulic and electric power, Do target extreme value calculations or multi-target analysis. Dynamic programming (DP) was proposed by Bellman (1957) to optimize multi-stage decision process problems. If the return value of each stage of the multi-stage decision-making process problem is independent and sat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/04G06Q50/06G06F17/16
CPCG06F17/16G06Q10/04G06Q10/06313G06Q50/06
Inventor 李想尹冬勤司源鲍军刘荣华范哲刘家宏白音包力皋穆祥鹏崔巍
Owner CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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