CN108563510A - The architecture method for sensing and optimizing calculated towards E grades - Google Patents
The architecture method for sensing and optimizing calculated towards E grades Download PDFInfo
- Publication number
- CN108563510A CN108563510A CN201810418522.1A CN201810418522A CN108563510A CN 108563510 A CN108563510 A CN 108563510A CN 201810418522 A CN201810418522 A CN 201810418522A CN 108563510 A CN108563510 A CN 108563510A
- Authority
- CN
- China
- Prior art keywords
- sensing
- optimizing
- code section
- grades
- program code
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of architecture method for sensing and optimizing calculated towards E grades, include the steps that task flow sensing and optimizing；The step of program code sensing and optimizing；The step of the step of programmed algorithm sensing and optimizing and vectorial sensing and optimizing.This architecture method for sensing and optimizing calculated towards E grades provided by the invention, using a variety of sensing and optimizing algorithms, when magnanimity computing resource that can be when being calculated towards E grade, rationally adjustment and distribution hardware resource, raising computational efficiency.
Description
Technical field
Present invention relates particularly to a kind of architecture method for sensing and optimizing calculated towards E grades.
Background technology
With the arrival of the development of economic technology, the improvement of people's living standards and information age, E grades calculate (million
Trillion times) epoch of supercomputer arrived.E grades calculate have superpower computing capability, can give computational science and
Scientific research brings revolutionary variation.
With the reach of science, the scale of various numerical models is also increasing, and model is also more nearly reality
Situation.But more accurate model means more complicated model solution.Currently, the reasearch funds of scientific research institutions are opposite
Anxiety can not undertake a large amount of required funds of server of purchase.So even if scientific research institutions successfully establish more accurately
Numerical model, its own also can not independently solve the numerical model.
If scientific research institutions can be by the code migrating of more accurate numerical model to the super calculation platform calculated towards E grades
On, then scientific research institutions are then not necessarily to purchase the powerful server of a large amount of performances, it is only necessary to calculation code is transplanted to super calculation platform,
It is long-range to check calculating progress and effect, the expense of the super calculation of calculating of payment.And the expense and a large amount of performances of super calculation of calculating are powerful
Server compare, undoubtedly cost wants cheap many.
But how the calculation code of numerical model is effectively transplanted to the super calculation platform calculated towards E grades, to most
The computing capabilitys for playing the super calculation platform of E grades of calculating of degree greatly, there has been no relevant researchs at present, thus also to a certain extent
Constrain the development in the field.
Invention content
The purpose of the present invention is to provide one kind to calculate towards E grades, can be perceived to journey according to task architecture
The architecture method for sensing and optimizing calculated towards E grades that sequence optimizes.
This architecture method for sensing and optimizing calculated towards E grades provided by the invention, includes the following steps：
The step of task flow sensing and optimizing, is perceived and is optimized for all tasks to required execution, and adjustment is simultaneously
The processing sequence of each task is obtained, to accelerate the treatment effeciency of task flow；
The step of program code sensing and optimizing, carries out for the program code for needing to execute to be distributed to most suitable system
It carries out Distributed Calculation and carries out result and summarize, to improve the treatment effeciency of program code；
The step of programmed algorithm sensing and optimizing, for being analyzed programmed algorithm and being distributed corresponding computing resource, from
And improve the treatment effeciency of programmed algorithm；
The step of vectorial sensing and optimizing, selects maximum compression to store lattice for calculating the vectorization in calculating process
Formula, to improve the treatment effeciency of vectorization calculating.
The task flow sensing and optimizing, specifically comprises the following steps：
(1) task of institute's execution in need is obtained, and confirms that there is no relation of interdependence between all tasks；
(2) subtask of all tasks to being obtained in step (1) is identified, and unifies in a period of time to each
Computing resource is distributed in a subtask；
(3) subtask of calculation amount minimum in each subtask, and priority processing are found using most short optimization of job algorithm
The subtask of calculation amount minimum.
The program code sensing and optimizing, specifically comprises the following steps：
1) test code is tested, the best match found between each typical independent code section and system hardware is closed
System；
2) program code is identified, finds the independent code section in program code；
3) the independent code section that step 2) obtains is matched with typical independent code section, to obtain each independent generation
Correspondence between code section and typical independent code section；
4) according in the independent code section in step 3) and the correspondence between typical independent code section and step 1)
Optimum matching relation between each typical case's independent code section and system hardware, independent code section is distributed hard to corresponding system
Part, to improve the treatment effeciency of program code.
The programmed algorithm sensing and optimizing, specifically comprises the following steps：
A. before programmed algorithm operation, the mean allocation of computing resource is carried out to different programmed algorithms；
B. when programmed algorithm is run, if occurring calculating the case where waiting for, the resource requirement of programmed algorithm carries out again
Analysis；
C. the analysis result obtained according to step B balances (DRF) algorithm using dynamic resource and is carried out again to computing resource
Distribution.
Being redistributed to computing resource described in step C is specially redistributed using following principle：
R1. user cannot obtain resources more more than other users；
R2. user cannot obtain more resources by lying about its resource requirement；
R3. all utilizable resources are distributed, do not have to replace existing resource allocation；
R4. user will not prefer the resource allocation of other users.
This architecture method for sensing and optimizing calculated towards E grades provided by the invention, is calculated using a variety of sensing and optimizings
Method, can towards E grades calculate when magnanimity computing resource when, rationally adjustment and distribution hardware resource, improve computational efficiency.
Description of the drawings
Fig. 1 is the method schematic diagram of the method for the present invention.
Specific implementation mode
It is the method flow schematic diagram of the method for the present invention as shown in Figure 1：This body calculated towards E grades provided by the invention
Architecture method for sensing and optimizing, includes the following steps：
The step of task flow sensing and optimizing, is perceived and is optimized for all tasks to required execution, and adjustment is simultaneously
The processing sequence of each task is obtained, to accelerate the treatment effeciency of task flow；Specifically comprise the following steps：
(1) task of institute's execution in need is obtained, and confirms that there is no relation of interdependence between all tasks；
(2) subtask of all tasks to being obtained in step (1) is identified, and unifies in a period of time to each
Computing resource is distributed in a subtask；
(3) subtask of calculation amount minimum in each subtask, and priority processing are found using most short optimization of job algorithm
The subtask of calculation amount minimum；
The step of program code sensing and optimizing, carries out for the program code for needing to execute to be distributed to most suitable system
It carries out Distributed Calculation and carries out result and summarize, to improve the treatment effeciency of program code；Specifically comprise the following steps：
1) test code is tested, the best match found between each typical independent code section and system hardware is closed
System；
2) program code is identified, finds the independent code section in program code；
3) the independent code section that step 2) obtains is matched with typical independent code section, to obtain each independent generation
Correspondence between code section and typical independent code section；
4) according in the independent code section in step 3) and the correspondence between typical independent code section and step 1)
Optimum matching relation between each typical case's independent code section and system hardware, independent code section is distributed hard to corresponding system
Part, to improve the treatment effeciency of program code；
The step of programmed algorithm sensing and optimizing, for being analyzed programmed algorithm and being distributed corresponding computing resource, from
And improve the treatment effeciency of programmed algorithm；Specifically comprise the following steps：
A. before programmed algorithm operation, the mean allocation of computing resource is carried out to different programmed algorithms；
B. when programmed algorithm is run, if occurring calculating the case where waiting for, the resource requirement of programmed algorithm carries out again
Analysis；
C. the analysis result obtained according to step B balances (DRF) algorithm using dynamic resource and is carried out again to computing resource
Distribution；
The step of vectorial sensing and optimizing, selects maximum compression to store lattice for calculating the vectorization in calculating process
Formula, to improve the treatment effeciency of vectorization calculating；Specially redistributed using following principle：
R1. user cannot obtain resources more more than other users；
R2. user cannot obtain more resources by lying about its resource requirement；
R3. all utilizable resources are distributed, do not have to replace existing resource allocation；
R4. user will not prefer the resource allocation of other users.
Claims (5)
1. a kind of architecture method for sensing and optimizing calculated towards E grades, includes the following steps：
The step of task flow sensing and optimizing, is perceived for all tasks to required execution and is optimized, adjusted and obtained
The processing sequence of each task, to accelerate the treatment effeciency of task flow；
The step of program code sensing and optimizing, carries out for the program code for needing to execute to be distributed to most suitable system
Distributed Calculation simultaneously carries out result and summarizes, to improve the treatment effeciency of program code；
The step of programmed algorithm sensing and optimizing, for being analyzed programmed algorithm and being distributed corresponding computing resource, to carry
The treatment effeciency of high programmed algorithm；
The step of vectorial sensing and optimizing, selects maximum compression storage format for calculating the vectorization in calculating process, from
And improve the treatment effeciency of vectorization calculating.
2. the architecture method for sensing and optimizing according to claim 1 calculated towards E grades, it is characterised in that described appoints
Business stream sensing and optimizing, specifically comprises the following steps：
(1) task of institute's execution in need is obtained, and confirms that there is no relation of interdependence between all tasks；
(2) subtask of all tasks to being obtained in step (1) is identified, and unifies in a period of time to each height
Task distributes computing resource；
(3) subtask of calculation amount minimum in each subtask is found using most short optimization of job algorithm, and the priority processing meter
The subtask of calculation amount minimum.
3. the architecture method for sensing and optimizing according to claim 1 or 2 calculated towards E grades, it is characterised in that described
Program code sensing and optimizing, specifically comprise the following steps：
1) test code is tested, finds the optimum matching relation between each typical independent code section and system hardware；
2) program code is identified, finds the independent code section in program code；
3) the independent code section that step 2) obtains is matched with typical independent code section, to obtain each independent code section
With the correspondence between typical independent code section；
4) according to each in the independent code section in step 3) and the correspondence between typical independent code section and step 1)
Optimum matching relation between typical independent code section and system hardware distributes independent code section to corresponding system hardware,
To improve the treatment effeciency of program code.
4. the architecture method for sensing and optimizing according to claim 1 or 2 calculated towards E grades, it is characterised in that described
Programmed algorithm sensing and optimizing, specifically comprise the following steps：
A. before programmed algorithm operation, the mean allocation of computing resource is carried out to different programmed algorithms；
B. when programmed algorithm is run, if occurring calculating the case where waiting for, the resource requirement of programmed algorithm is reanalysed；
C. the analysis result obtained according to step B divides computing resource using dynamic resource balance (DRF) algorithm again
Match.
5. the architecture method for sensing and optimizing according to claim 1 or 2 calculated towards E grades, it is characterised in that step C
Described redistributes computing resource, is specially redistributed using following principle：
R1. user cannot obtain resources more more than other users；
R2. user cannot obtain more resources by lying about its resource requirement；
R3. all utilizable resources are distributed, do not have to replace existing resource allocation；
R4. user will not prefer the resource allocation of other users.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810418522.1A CN108563510B (en) | 2018-05-04 | 2018-05-04 | E-level calculation-oriented architecture perception optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810418522.1A CN108563510B (en) | 2018-05-04 | 2018-05-04 | E-level calculation-oriented architecture perception optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108563510A true CN108563510A (en) | 2018-09-21 |
| CN108563510B CN108563510B (en) | 2021-07-13 |
Family
ID=63537648
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810418522.1A Active CN108563510B (en) | 2018-05-04 | 2018-05-04 | E-level calculation-oriented architecture perception optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108563510B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101453398A (en) * | 2007-12-06 | 2009-06-10 | 怀特威盛软件公司 | Novel distributed grid super computer system and method |
| US20130080482A1 (en) * | 2003-04-09 | 2013-03-28 | Gary Charles Berkowitz | Virtual Supercomputer |
| CN103885839A (en) * | 2014-04-06 | 2014-06-25 | 孙凌宇 | Cloud computing task scheduling method based on multilevel division method and empowerment directed hypergraphs |
| US8874879B2 (en) * | 2010-11-11 | 2014-10-28 | Fujitsu Limited | Vector processing circuit, command issuance control method, and processor system |
| CN104969207A (en) * | 2012-10-22 | 2015-10-07 | 英特尔公司 | High performance interconnect coherence protocol |
| CN107147517A (en) * | 2017-03-24 | 2017-09-08 | 上海交通大学 | A kind of adaptive polo placement resource allocation methods for virtual network function |
| CN107977270A (en) * | 2017-11-22 | 2018-05-01 | 用友金融信息技术股份有限公司 | Peers distribution method, peers distribution system and computer installation |
-
2018
- 2018-05-04 CN CN201810418522.1A patent/CN108563510B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130080482A1 (en) * | 2003-04-09 | 2013-03-28 | Gary Charles Berkowitz | Virtual Supercomputer |
| CN101453398A (en) * | 2007-12-06 | 2009-06-10 | 怀特威盛软件公司 | Novel distributed grid super computer system and method |
| US8874879B2 (en) * | 2010-11-11 | 2014-10-28 | Fujitsu Limited | Vector processing circuit, command issuance control method, and processor system |
| CN104969207A (en) * | 2012-10-22 | 2015-10-07 | 英特尔公司 | High performance interconnect coherence protocol |
| CN103885839A (en) * | 2014-04-06 | 2014-06-25 | 孙凌宇 | Cloud computing task scheduling method based on multilevel division method and empowerment directed hypergraphs |
| CN107147517A (en) * | 2017-03-24 | 2017-09-08 | 上海交通大学 | A kind of adaptive polo placement resource allocation methods for virtual network function |
| CN107977270A (en) * | 2017-11-22 | 2018-05-01 | 用友金融信息技术股份有限公司 | Peers distribution method, peers distribution system and computer installation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108563510B (en) | 2021-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ben-Nun et al. | A modular benchmarking infrastructure for high-performance and reproducible deep learning | |
| US10032114B2 (en) | Predicting application performance on hardware accelerators | |
| CN106484777B (en) | Multimedia data processing method and device | |
| Meswani et al. | Modeling and predicting performance of high performance computing applications on hardware accelerators | |
| CN109523022B (en) | Terminal data processing method and device and terminal | |
| CN106991006B (en) | Support the cloud workflow task clustering method relied on and the time balances | |
| CN114902273A (en) | System and method for optimizing resource allocation using GPU | |
| KR101374120B1 (en) | System and method for coinstructing virtual cluster using the exectution history of analysis job | |
| US20170177544A1 (en) | Method and system for solving a problem involving a hypergraph partitioning | |
| EP3614312A1 (en) | Calculation technique determining system, calculation technique determining device, processing device, calculation technique determining method, processing method, calculation technique determining program, and processing program | |
| Baughman et al. | Profiling and predicting application performance on the cloud | |
| CN104793993A (en) | Cloud computing task scheduling method of artificial bee colony particle swarm algorithm based on Levy flight | |
| CN113962704A (en) | Transfer verification method, device and equipment based on face recognition and storage medium | |
| CN112328869A (en) | User loan willingness prediction method and device and computer system | |
| CN108563510A (en) | The architecture method for sensing and optimizing calculated towards E grades | |
| Ríos et al. | Dynamically adapting floating-point precision to accelerate deep neural network training | |
| CN109769031A (en) | A kind of dynamic self-adapting load-balancing method and system | |
| JP6154491B2 (en) | Computer and graph data generation method | |
| CN113657724B (en) | Resource allocation method and device based on multi-source heterogeneous data and electronic equipment | |
| CN108170404A (en) | A kind of Web service combination verification method based on parameterized model | |
| Li et al. | Adaptive multi-objective swarm crossover optimization for imbalanced data classification | |
| US20210350280A1 (en) | Model modification and deployment | |
| CN108932459A (en) | Face recognition model training method and device and recognition algorithms | |
| CN115527626A (en) | Molecular processing method, molecular processing apparatus, electronic device, storage medium, and program product | |
| Liu et al. | Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |