CN107506932A - Power grid risk scenes in parallel computational methods and system - Google Patents
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Abstract
The present invention relates to a kind of power grid risk scenes in parallel computational methods and system, structure parallel computation pond, the parallel computation pond includes multiple calculate nodes, and the calculate node includes multiple computing engines；Multiple power grid risk contextual datas are obtained, for power grid risk contextual data corresponding to each computing engines distribution；Each power grid risk contextual data computing engines corresponding to are transmitted, parallel computation is carried out to each power grid risk contextual data by each computing engines；Obtain parallel computation result.In this programme, described power grid risk contextual data, contain the data of large-scale branch breaking, pass through the distribution to the contextual data, each power network contextual data is transferred to corresponding computing engines and carries out parallel computation, can efficiently complete the power grid risk scene calculating task that computationally intensive, iterations is more, requirement of real-time is high.
Description
Technical field
The present invention relates to electric power network technique field, more particularly to a kind of power grid risk scenes in parallel computational methods and system.
Background technology
With the growth of social and economic level and the increase of population, power network scale is increasing, safety of the society to power network
The requirement of property and reliability also more and more higher.In order to ensure power network normal operation, researcher proposes power networks risk and commented
Estimate index and computational methods.It will be carried out because the forecast failure scene to be considered is large number of, and to each fault scenes
Evaluation work, the amounts of calculation such as tidal current analysis, load reduction are very large.
In order to improve the computational efficiency of power grid risk assessment, typically completed by buying high-performance server or work station,
But because expensive and maintenance cost is high, it is difficult to possess；Also have a trial optimized algorithm, but due to technology involves a wide range of knowledge,
Deep, the raising that optimized algorithm is brought to computational efficiency is very little, therefore the computational efficiency of power grid risk assessment is low.
The content of the invention
Based on this, it is necessary to for traditional power grid risk assessment computational efficiency it is low the problem of, there is provided a kind of power network
Risk scenes in parallel computational methods and system.
A kind of power grid risk scenes in parallel computational methods, comprise the following steps：
Parallel computation pond is built, parallel computation pond includes multiple calculate nodes, and calculate node includes multiple computing engines；
Multiple power grid risk contextual datas are obtained, for power grid risk contextual data corresponding to the distribution of each computing engines；
Each power grid risk contextual data computing engines corresponding to are transmitted, by each computing engines to each power grid risk scene
Data carry out parallel computation；
Obtain parallel computation result.
A kind of power grid risk scenes in parallel computing system, including with lower module：
Parallel computation pond builds module, and for building parallel computation pond, parallel computation pond includes multiple calculate nodes, calculates
Node includes multiple computing engines；
Contextual data distribute module, for obtaining multiple power grid risk contextual datas, for corresponding to the distribution of each computing engines
Power grid risk contextual data, transmit each power grid risk contextual data computing engines corresponding to；
Parallel computation module, for carrying out parallel computation to each power grid risk contextual data by each computing engines；
As a result collection module, for obtaining parallel computation result.
According to the power grid risk scenes in parallel computational methods and system of the invention described above, built simultaneously using multiple calculate nodes
Row computing pool, each computing engines multiple power network contextual datas being assigned in each calculate node, drawn by each calculating
Hold up and parallel computation is carried out to power grid risk contextual data, obtain the result of calculation of each computing engines.In this scheme, by right
The distribution of contextual data, each power network contextual data is transferred to corresponding computing engines and carries out parallel computation, can be efficiently
The power grid risk scene calculating task that computationally intensive, iterations is more, requirement of real-time is high is completed, so as to improve power grid risk field
The computational efficiency of scape.
A kind of readable storage medium storing program for executing, is stored thereon with executable program, and the program is realized above-mentioned when being executed by processor
The step of power grid risk scenes in parallel computational methods.
A kind of computing device, including memory, processor and storage on a memory and can run on a processor can
Configuration processor, the step of realizing above-mentioned power grid risk scenes in parallel computational methods during computing device program.
According to the power grid risk scenes in parallel computational methods of the invention described above, the present invention also provides a kind of readable storage medium storing program for executing
And computing device, for realizing above-mentioned power grid risk scenes in parallel computational methods by program.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the power grid risk scenes in parallel computational methods in one embodiment of the invention；
Fig. 2 is the schematic flow sheet of the structure parallel computation pond step in another embodiment of the present invention；
Fig. 3 is the schematic flow sheet of the allocation scenarios data step in another embodiment of the present invention；
Fig. 4 is the structural representation of the power grid risk scenes in parallel computing system in another embodiment of the present invention；
Fig. 5 is the schematic flow sheet of the power grid risk scenes in parallel computational methods in another embodiment of the present invention；
Fig. 6 is the schematic flow sheet of the power grid risk scenes in parallel computational methods in another embodiment of the present invention；
Fig. 7 is the parallel computation Organization Chart of the power grid risk scenes in parallel computational methods in another embodiment of the present invention.
Embodiment
For the objects, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with drawings and Examples, to this
Invention is described in further detail.It should be appreciated that embodiment described herein is only to explain the present invention,
Do not limit protection scope of the present invention.
It is shown in Figure 1, it is the schematic flow sheet of the power grid risk scenes in parallel computational methods of one embodiment of the invention.
Power grid risk scenes in parallel computational methods in the embodiment comprise the following steps：
Step S110：Parallel computation pond is built, parallel computation pond includes multiple calculate nodes, and calculate node includes multiple meters
Calculate engine；
In this step, calculate node can be computer equipment, including polycaryon processor and storage device, pass through connection
The processor and storage device of multiple calculate nodes, can construct parallel computation pond.
Step S120：Multiple power grid risk contextual datas are obtained, for power grid risk scene corresponding to the distribution of each computing engines
Data；
In this step, by distributing power grid risk contextual data to each computing engines, each calculating can be adjusted and drawn
The calculating pressure held up.
Step S130：Each power grid risk contextual data computing engines corresponding to are transmitted, by each computing engines to each electricity
Net risk contextual data carries out parallel computation；
In this step, distribute after the completion of power grid risk contextual data, according to allocation result, each contextual data is transmitted
Into each computing engines of each calculate node, parallel computation is carried out.
Step S140：Obtain parallel computation result.
In this step, after the completion of due to parallel computation, each result of calculation is still stored in each computing engines, because
This needs to return to result of calculation by each computing engines.
In the present embodiment, parallel computation pond is built using multiple calculate nodes, multiple power network contextual datas is assigned to
Each computing engines in each calculate node, parallel computation is carried out to power grid risk contextual data by each computing engines,
Obtain the result of calculation of each computing engines.In this scheme, by the distribution to contextual data, by each power network contextual data
Computing engines corresponding to being transferred to carry out parallel computation, can efficiently complete that computationally intensive, iterations is more, requirement of real-time
High power grid risk scene calculating task.
Optionally, power grid risk contextual data can include the data of the branch breaking of large scale electric network.By to each
The arrangement of data is cut-off on road, forms multiple power grid risk contextual datas；
It is shown in Figure 2, it is the schematic flow sheet that parallel computation pond is built in one of embodiment, structure in the embodiment
The step of building parallel computation pond comprises the following steps：
Step S111：Multiple calculate nodes are obtained, each calculate node is added in cluster, task pipe is created to cluster
Reason；
Step S112：The computing engines quantity of each calculate node is configured, addition cluster to initial cluster configuration file, is obtained
Target cluster configuration file, parallel computation pond is created according to target cluster configuration file.
In the present embodiment, obtain the calculate node for participating in parallel computation, addition calculate node to same collection
Group, after creating task management and configuration computing engines quantity, the cluster is added in initial cluster configuration file, the mesh of acquisition
Cluster configuration file is marked, can be used in and create parallel computation pond.Due in practical application, for different calculating tasks, it is necessary to
Cluster corresponding to targetedly creating.Due to the feature of group system multinode, cluster configuration is intricate, especially large-scale
In system, manual configuration clustered node efficiency is low, by using cluster configuration file, can improve establishment and the pipe in parallel pond
The efficiency of reason.
Optionally, aforesaid operations can be completed in the one of calculate node in parallel pond, pass through host IP address or master
Machine name, the calculate node available for participation parallel computation is found, and the calculate node found is added to a cluster, by right
The cluster creates task unified management.
In one of the embodiments, the step of computing engines quantity for configuring each calculate node, comprises the following steps：
By the CPU core quantity that the computing engines quantity configuration in current calculate node is current calculate node.
In the present embodiment, Data-intensive computing is calculated as due to power grid risk contextual data, in calculating process,
Substantially there is no I/O operation, IO obstructions seldom occur；On the other hand, the single cores of CPU are queuing processing when handling multiple processes
, due to the corresponding process of a computing engines, therefore the computing engines quantity of each calculate node is arranged to and the meter
The CPU core quantity for calculating engine is consistent, it is possible to reduce due to performance loss caused by system call, makes full use of each calculate node
Computing resource.In one of the embodiments, each calculate node is in same LAN, passes through LAN between calculate node
Carry out the transmission of data.
In the present embodiment, because the data volume that power grid risk contextual data is designed into practical operation is very big, and
Contextual data, which needs to be transferred in each computing engines, carries out parallel computation, therefore the transmission rate of data is had higher requirements.
Each calculate node in parallel computation pond is networked using the transmission medium specially laid, and has higher transmission rate,
And postpone low, improve the transmission of parallel computation pond Scene data, and then improve the efficiency of parallel computation.
In one of the embodiments, after building parallel computation pond, any one chosen in parallel computation pond calculates section
Point is used as client, and each power grid risk contextual data is transmitted to client；Wherein, client is that the distribution of each computing engines is corresponding
Power grid risk contextual data；
Each data are transmitted corresponding to the step of computing engines to comprise the following steps：
Pass through each data of client transmissions to corresponding computing engines.
In the present embodiment, the calculating of power grid risk contextual data is designed into substantial amounts of computing, enters by using client
The distribution of row calculating task, calculating task can be preferably divided, improve the efficiency of parallel computation.
Shown in Figure 3, it is power grid risk scene number corresponding to each computing engines distribution in one of embodiment to be
According to schematic flow sheet, the step of being power grid risk contextual data corresponding to each computing engines distribution in the embodiment include with
Lower step：
Step S121：Obtain that computing engines in parallel computation pond are total and the engine sequence number of each computing engines；
Step S122：Power grid risk contextual data is sequentially numbered, obtains scene numbering；
Step S123：Current scene numbering is added 1 to computing engines sum complementation, obtained corresponding with current scene numbering
Targeting engine sequence number, current scene is numbered into affiliated power grid risk contextual data and distributed to the calculating belonging to targeting engine sequence number
Engine.
In the present embodiment, by using the distribution method of contextual data, each contextual data can distribute to obtain one
The individual computing engines for being responsible for calculating it, while the contextual data amount that each computing engines distribute to obtain is essentially identical, because
This makes parallel computation pond reach the effect of load balance, is advantageous to improve CPU utilization rate, minimizes task free time, carry
High parallel efficiency calculation.
In one of which embodiment, the platform of parallel computation is MATLAB, utilizes the single program multiple data stream of platform
Method to power grid risk contextual data carry out parallel computation.
In the present embodiment, the data volume of power grid risk contextual data is larger, but the computational methods of each contextual data are
It is similar, the simply difference of data.SPMD (Single Program Multiple Data) refer to single program multiple data stream and
Row computational methods, Parallel Computation is run by using MATLAB, same section of program is operated on multiple computing engines, journey
Corresponding code is encoded with to different data in sequence, each computing engines is using same program to different power grid risk fields
Scape data are calculated, while the program bag contains necessary logic, and each computing engines can only carry out the part language in program
Sentence, it is not necessary to perform whole program, the return value of result of calculation is stored with the object of composite types, therefore is improved parallel
The efficiency of calculating.
Comprise the following steps in one of which embodiment, the step of parallel computation：
Corresponding power grid risk contextual data is transferred to by current computing engines corresponding with current computing engines
GPU；
Wherein, GPU judges the type of the power grid risk contextual data received, if the power grid risk contextual data received is thick
Close matrix data, then dense matrix data transfer is subjected to parallel computation into GPU video memorys using gpuArray () function；
If the power grid risk contextual data received is sparse matrix data, storehouse is calculated to dilute using MEX functions and CUDA
Dredge matrix data and carry out parallel computation；
If the power grid risk contextual data received is multiple data and is scalar, made by oneself by the generation of arrayfun functions
Adopted function, parallel computation is carried out to the power grid risk contextual data of reception using SQL.
In the present embodiment, computing engines transfer data to GPU after the contextual data of distribution is received
(Graphics Processing Unit, graphics processor) carries out parallel computation.GPU is the core of computer display apparatus
Part, each GPU contain multiple stream handles, and for image procossing, these stream handles are designed to work in a parallel fashion.CUDA
(Compute Unified Device Architecture) is GPU universal parallel computing architecture, applies to GPU
Floating-point operation.Using the far super CPU of GPU floating-point operation performance, high memory bandwidth and high performance-price ratio the characteristics of, by CUDA frameworks
Apply in the parallel computation task of large scale electric network risk scene, be advantageous to improve computational efficiency.
The calculating of matrix is the basic composition that power grid risk scene calculates, in a matrix, if the element number that numerical value is 0 is remote
Far more than the number of non-zero element, and when non-zero Elemental redistribution does not have rule, then the matrix is referred to as sparse matrix；In contrast,
If non-zero element number is in the great majority, the matrix is referred to as dense matrix.
In order to improve the parallel efficiency calculation of contextual data, by gpuArray () function by the scene of dense matrix type
Data duplication matrix and vector of the generation with gpuArray attributes, can be calculated automatically into GPU video memorys using GPU.
For the contextual data of sparse matrix type, if using above-mentioned and dense matrix type identical calculation, can consume
Substantial amounts of GPU video memorys space, causes calculation scale to be restricted, therefore calculates storehouse to sparse matrix using MEX functions and CUDA
Data carry out parallel computation, and MEX functions therein can call all kinds of CUDA to calculate the parallel computation that storehouse mix GPU.Tool
Body, the parallel of GPU can be carried out using two sparse matrix numerical computations storehouses of the CUDA CUSPARSE provided and CUSOLVER
Calculate.The power grid risk contextual data of reception is multiple data and is the situation of scalar, can be that input or output variable are more than
The situation of one.
In one of which embodiment, obtain parallel computation result the step of comprise the following steps：
Receive the result of calculation that each calculate node returns；Wherein, each calculate node is converged by gather functions respectively
Collect the result of calculation of each computing engines each included.
In the present embodiment, due to power grid risk contextual data be assigned to each computing engines carry out parallel computation, it is necessary to
Obtain the result of calculation that each computing engines return.Calculate node each first is by gather functions, by result of calculation from GPU
Video memory is recovered to physical memory, and then each calculate node returns again to result of calculation.By calling gather functions, improve and obtain
Take the speed of the result of calculation of each computing engines.
According to above-mentioned power grid risk scenes in parallel computational methods, the present invention also provides a kind of power grid risk scenes in parallel and calculated
System, just the embodiment of the power grid risk scenes in parallel computing system of the present invention is described in detail below.
It is shown in Figure 4, it is the structural representation of the power grid risk scenes in parallel computing system of one embodiment of the invention,
Power grid risk scenes in parallel computing system in the embodiment includes：
Parallel computation pond builds module 210, and for building parallel computation pond, parallel computation pond includes multiple calculate nodes,
Calculate node includes multiple computing engines；
Contextual data distribute module 220, it is corresponding for the distribution of each computing engines for obtaining multiple power grid risk contextual datas
Power grid risk contextual data, transmit each power grid risk contextual data to corresponding to computing engines；
Parallel computation module 230, for carrying out parallel computation to each power grid risk contextual data by each computing engines；
As a result collection module 240, for obtaining parallel computation result.
In one of which embodiment, parallel computation pond structure module 210 obtains multiple calculate nodes, and each calculate is saved
Point is added in cluster, and task management is created to cluster；Configure the computing engines quantity of each calculate node, addition cluster is to initial
Cluster configuration file, target cluster configuration file is obtained, parallel computation pond is created according to target cluster configuration file.
In one of the embodiments, parallel computation pond builds module 210 by the computing engines number in current calculate node
Amount is configured to the CPU core quantity of current calculate node.
In one of the embodiments, parallel computation pond structure module 210 configures each calculate node in same local
Net, make the transmission for carrying out data between each calculate node by LAN.
In one of the embodiments, contextual data distribute module 220 chooses any one calculating in parallel computation pond
Node transmits each power grid risk contextual data to client as client；Wherein, client is the distribution pair of each computing engines
The power grid risk contextual data answered, pass through each data of client transmissions to corresponding computing engines.
In one of which embodiment, the computing engines that contextual data distribute module 220 is obtained in parallel computation pond are total
The engine sequence number of several and each computing engines；Power grid risk contextual data is sequentially numbered, obtains scene numbering；By current field
Scape numbering adds 1 to computing engines sum complementation, obtains targeting engine sequence number corresponding with current scene numbering, current scene is compiled
Power grid risk contextual data belonging to number is distributed to the computing engines belonging to targeting engine sequence number.
In one of which embodiment, parallel computation pond structure module 210 utilizes MATLAB platform construction parallel computations
Pond, parallel computation module 230 are carried out using the method for the single program multiple data stream of MATLAB platforms to power grid risk contextual data
Parallel computation.
In one of which embodiment, contextual data distribute module 220 is by current computing engines by corresponding power network
Risk contextual data is transferred to GPU corresponding with current computing engines；Wherein, GPU judges the power grid risk contextual data received
Type, when the power grid risk contextual data of reception is dense matrix data, using gpuArray () function by dense matrix
Data transfer carries out parallel computation into GPU video memorys；
When the power grid risk contextual data of reception is sparse matrix data, storehouse is calculated to dilute using MEX functions and CUDA
Dredge matrix data and carry out parallel computation；
It is multiple data in the power grid risk contextual data of reception and when being scalar, is made by oneself by the generation of arrayfun functions
Adopted function, parallel computation is carried out to the power grid risk contextual data of reception using SQL.
In one of which embodiment, as a result collection module 240 receives the result of calculation that each calculate node returns；Its
In, each calculate node collects the result of calculation of the computing engines each included by gather functions respectively.
The power grid risk scenes in parallel computational methods one of the power grid risk scenes in parallel computing system of the present invention and the present invention
One correspondence, the technical characteristic illustrated in the embodiment of above-mentioned power grid risk scenes in parallel computational methods and its advantage are applicable
In the embodiment of power grid risk scenes in parallel computing system.
According to above-mentioned power grid risk scenes in parallel computational methods, the embodiment of the present invention also provide a kind of readable storage medium storing program for executing and
A kind of computing device.Executable program is stored with readable storage medium storing program for executing, the program realizes above-mentioned power network when being executed by processor
The step of risk scenes in parallel computational methods；Computing device includes memory, processor and storage on a memory and can located
The executable program that runs on reason device, the step of above-mentioned power grid risk scenes in parallel computational methods is realized during computing device program
Suddenly.
In a specific embodiment, power grid risk scenes in parallel computational methods comprise the following steps：
Using perceptive construction on mathematics structure parallel computation pond, parallel computation pond includes multiple calculate nodes, calculate node
Including multiple computing engines；Wherein, the step of building parallel pond comprises the following steps：
Multiple calculate nodes are obtained, MATLAB is added to the system fire wall of calculate node, and are each node installation
MATLAB Distributed Parallel Computing servers, and start service, each calculate node is in same LAN, leads between calculate node
Cross the transmission that LAN carries out data；
Each calculate node is added in cluster using the interface Admin Center that MATLAB is provided, in interface
MATLAB Job Scheduler interfaces create task management to cluster；
By the CPU core quantity that the computing engines quantity configuration in current calculate node is current calculate node, cluster is added
To initial cluster configuration file, target cluster configuration file is obtained, while Monitor Jobs are established for the configuration, monitors cluster
Task status；Parallel computation pond is created according to target cluster configuration file.
Multiple power grid risk contextual datas are obtained, for power grid risk contextual data corresponding to the distribution of each computing engines；
Wherein, after building parallel computation pond, any one calculate node in parallel computation pond is chosen as client, will
Each power grid risk contextual data is transmitted to client；Pass through each data of client transmissions to corresponding computing engines.
Wherein, for each computing engines distribution corresponding to power grid risk contextual data the step of comprise the following steps：
The computing engines sum in parallel computation pond is obtained, and the engine of computing engines is returned using labindex () function
Sequence number；
Power grid risk contextual data is sequentially numbered, obtains scene numbering；
Current scene numbering is added 1 to computing engines sum complementation, obtained and the corresponding targeting engine of current scene numbering
Sequence number, current scene is numbered into affiliated power grid risk contextual data and distributed to the computing engines belonging to targeting engine sequence number.
The parallel computation tool box provided using MATLAB carries out parallel computation, specifically includes：
Each power grid risk contextual data computing engines corresponding to are transmitted, by each computing engines to each power grid risk scene
Data carry out parallel computation, and the single program multiple data stream method that parallel computation is provided using MATLAB platforms is realized；Wherein, parallel
The step of calculating, comprises the following steps：
Corresponding power grid risk contextual data is transferred to by current computing engines corresponding with current computing engines
GPU；If the power grid risk contextual data received is dense matrix data, using gpuArray () function by dense matrix number
Parallel computation is carried out according to being transferred in GPU video memorys；If the power grid risk contextual data received is sparse matrix data, use
MEX functions and CUDA calculate storehouse and carry out parallel computation to sparse matrix data；If the power grid risk contextual data received is multiple
Data and be scalar, then SQL, the power grid risk using SQL to reception are generated by arrayfun functions
Contextual data carries out parallel computation.
Obtain parallel computation result；Wherein, each calculate node collects the meter of each computing engines by gather functions
Result is calculated, is as a result returned in the variable of composite object types, then each calculate node is received in client and returns
The result of calculation returned, the result of calculation of each calculate node, value is indexed by the engine sequence number of computing engines.
It is shown in Figure 5, it is the flow signal of the power grid risk scenes in parallel computational methods of another embodiment of the present invention
Figure.Power grid risk scenes in parallel computational methods in the embodiment comprise the following steps：
Step S310：Parallel computation pond is built, is comprised the following steps：
The MATLAB softwares of identical version are installed to each calculate node of participation parallel computation, and MATLAB is added to
Fire wall；The MATLAB softwares of installation identical version can avoid the compatibility issue come due to version different band；Due to follow-up
Each calculate node in parallel computation pond carries out data interaction by LAN in step, in order to avoid some behaviour in interaction
Work is influenceed by fire wall, it is therefore desirable to makees relative set to protecting wall in advance.
Each calculate node installation MATLAB Distributed Parallel Computing servers are given with keeper's identity, and start service；
Each calculate node is configured in same LAN, each calculate node is then added to one using Admin Center
In individual cluster；Task management is created in MATLAB Job Scheduler；Computing engines quantity is configured for individual calculate node, it is excellent
Selection of land, the computing engines quantity of each calculate node are identical with the calculate node CPU core quantity；
Cluster is added in cluster configuration file, while Monitor Jobs are established for the configuration file, monitors cluster
Task status.
Parallel computation pond is created using cluster configuration file.
Step S320：Multiple power grid risk contextual datas are obtained, for power grid risk scene corresponding to the distribution of each computing engines
Data, comprise the following steps：
Calculative power grid risk contextual data is obtained, and is numbered by ordered pair scene；
The numbering of scene obtains remainder+1, obtained result is the responsible execution scene to computing engines number complementation
The engine sequence number of computing engines；
Step S330：Each power grid risk contextual data computing engines corresponding to are transmitted, by each computing engines to each electricity
Net risk contextual data carries out parallel computation；Comprise the following steps：
Parallel computation is carried out using MATLAB parallel computation tool box, Parallel Computation is opened using spmd-end；
Wherein, spmd-end is used to mark the sentence for needing to carry out parallel computation using the method for SPMD single program multiple data streams.Wherein,
MATLAB parallel computation tool box can solve computational problem and data using polycaryon processor, GPU and computer cluster
The tool box of intensive problem, by using high-level structures such as parallel for circulations, special array type and parallelization numerical algorithms
Make, parallelization can be carried out to MATLAB application programs, without carrying out CUDA or MPI programmings.
The engine sequence number of current computing engines is returned using labindex () function；
According to the result of calculation of the above-mentioned numbering to scene, transmit each scene and enter into the computing engines of corresponding engine sequence number
Row parallel computation.
Wherein, the corresponding GPU of a computing engines, being selected by gpuDevice () function for each computing engines are corresponding
GPU calculated, including：
Dense matrix calculates：MATLAB provide can direct GPUization parallel computation built-in function to improve dense matrix
Computational efficiency.Contextual data is replicated by gpuArray () function and carries out parallel computation on GPU automatically into GPU video memorys；
Sparse matrix calculates：Because MATLAB built-in function does not support the direct GPU parallel computations of sparse matrix, therefore
Using two sparse matrix numerical computations storehouses of the CUSPARSE and CUSOLVER provided free by CUDA, provided with reference to MATLAB
MEX functions comprising GPU interfaces mix GPU parallel computation；
SQL：By the arrayfun functions of the MATLAB support SQLs provided, to inputting, exporting number
It is that scalar contextual data carries out parallel computation according to more than one and input data；
In this step, GPU is as coprocessor, and aiding CPU completion degree of parallelism is high, data-intensive, logic is simply transported
It can be regarded as industry.The ability of GPU parallel computations is even more powerful, and its inside has quick storage system, in addition, GPU hardware design energy
Thousands of parallel threads are enough managed, this thousands of thread is all created and managed by GPU, is carried out without developer any
Programming and management.
Step S340：Parallel computation result is obtained, is comprised the following steps：
Each computing engines collect result of calculation by gather functions and return to CPU, while reset falls GPU internal memory；
During due to carrying out parallel computation by SPMD methods, the return value of each computing engines is with composite type
Storage, it is therefore desirable to return the result in the object of composite types.Optionally, can be created in advance before parallel computation
Composite objects are built, and carry out initialization assignment.
Value is indexed by engine sequence number, obtains the result of calculation of each calculate node.
In the present embodiment, overall basic procedure is as shown in fig. 6, the Distributed Parallel Computing server structure for passing through MATLAB
Parallel computation environment as shown in Figure 7 is built, calls CUDA to calculate storehouse using MATLAB parallel computations tool box, is efficiently completed meter
The risk scene calculating task that calculation amount is big, iterations is more, requirement of real-time is high, can be by risk scene calculating task in Duo Tai
Parallel computation on computer, there is good fault-tolerance, both make use of the easy advantage of MATLAB itself parallel computations, combine again
GPU is adapted to the characteristics of complicated calculations, is favorably improved the efficiency of power grid risk scene calculating, realizes the parallel of large-scale calculations
Change.
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope that this specification is recorded all is considered to be.
Embodiment described above only expresses the several embodiments of the present invention, and its description is more specific and detailed, but simultaneously
Can not therefore it be construed as limiting the scope of the patent.It should be pointed out that come for one of ordinary skill in the art
Say, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (10)
1. a kind of power grid risk scenes in parallel computational methods, it is characterised in that comprise the following steps：
Parallel computation pond is built, the parallel computation pond includes multiple calculate nodes, and the calculate node includes multiple calculating and drawn
Hold up；
Multiple power grid risk contextual datas are obtained, for power grid risk contextual data corresponding to each computing engines distribution；
Each power grid risk contextual data computing engines corresponding to are transmitted, by each computing engines to each power network
Risk contextual data carries out parallel computation；
Obtain parallel computation result.
2. power grid risk scenes in parallel computational methods according to claim 1, it is characterised in that the structure parallel computation
The step of pond, comprises the following steps：
Multiple calculate nodes are obtained, each calculate node is added in cluster, task management is created to the cluster；
The computing engines quantity of each calculate node is configured, the cluster is added to initial cluster configuration file, obtains target
Cluster configuration file, parallel computation pond is created according to the target cluster configuration file.
3. power grid risk scenes in parallel computational methods according to claim 2, it is characterised in that the configuration is each to be calculated
The step of computing engines quantity of node, comprises the following steps：
By the CPU core quantity that the computing engines quantity configuration in current calculate node is current calculate node.
4. power grid risk scenes in parallel computational methods according to claim 2, it is characterised in that at each calculate node
The transmission of data is carried out by LAN between same LAN, calculate node.
5. power grid risk scenes in parallel computational methods according to claim 2, it is characterised in that further comprising the steps of：
Any one calculate node in the parallel computation pond is chosen as client, by each power grid risk contextual data
Transmit to the client；
Wherein, the client is power grid risk contextual data corresponding to each computing engines distribution；
Each data of transmission comprise the following steps corresponding to the step of computing engines：
Pass through each data of the client transmissions to corresponding computing engines.
6. power grid risk scenes in parallel computational methods according to claim 1, it is characterised in that described to draw for each calculating
The step of holding up power grid risk contextual data corresponding to distribution comprises the following steps：
Obtain that computing engines in the parallel computation pond are total and the engine sequence number of each computing engines；
The power grid risk contextual data is sequentially numbered, obtains scene numbering；
Current scene numbering is added 1 to the computing engines sum complementation, obtained and the corresponding targeting engine of current scene numbering
Sequence number, current scene is numbered affiliated power grid risk contextual data and distributed to the calculating belonging to the targeting engine sequence number and is drawn
Hold up.
7. power grid risk scenes in parallel computational methods according to claim 1, it is characterised in that the parallel computation is put down
Platform is MATLAB, and the power grid risk contextual data is counted parallel using the method for the single program multiple data stream of the platform
Calculate.
8. power grid risk scenes in parallel computational methods according to claim 7, it is characterised in that the step of the parallel computation
Suddenly comprise the following steps：
Corresponding power grid risk contextual data is transferred to GPU corresponding with current computing engines by current computing engines；
Wherein, the GPU judges the type of the power grid risk contextual data received, if the power grid risk contextual data received is thick
Close matrix data, then the dense matrix data transfer is counted parallel into GPU video memorys using gpuArray () function
Calculate；
If the power grid risk contextual data received is sparse matrix data, storehouse is calculated to described dilute using MEX functions and CUDA
Dredge matrix data and carry out parallel computation；
If the power grid risk contextual data received is multiple data and is scalar, self-defined letter is generated by arrayfun functions
Number, parallel computation is carried out to the power grid risk contextual data of reception using the SQL.
9. power grid risk scenes in parallel computational methods according to claim 8, it is characterised in that the acquisition parallel computation
As a result the step of, comprises the following steps：
Receive the result of calculation that each calculate node returns；Wherein, each calculate node is collected respectively by gather functions respectively
From including each computing engines result of calculation.
10. a kind of power grid risk scenes in parallel computing system, it is characterised in that including with lower module：
Parallel computation pond builds module, and for building parallel computation pond, the parallel computation pond includes multiple calculate nodes, described
Calculate node includes multiple computing engines；
Contextual data distribute module, for obtaining multiple power grid risk contextual datas, for corresponding to each computing engines distribution
Power grid risk contextual data, transmit each power grid risk contextual data computing engines corresponding to；
Parallel computation module, for carrying out parallel computation to each power grid risk contextual data by each computing engines；
As a result collection module, for obtaining parallel computation result.
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