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WO2014101532A1 - Procédé et dispositif pour analyser les performances d'exécution d'un programme - Google Patents

Procédé et dispositif pour analyser les performances d'exécution d'un programme Download PDF

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Publication number
WO2014101532A1
WO2014101532A1 PCT/CN2013/085302 CN2013085302W WO2014101532A1 WO 2014101532 A1 WO2014101532 A1 WO 2014101532A1 CN 2013085302 W CN2013085302 W CN 2013085302W WO 2014101532 A1 WO2014101532 A1 WO 2014101532A1
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WO
WIPO (PCT)
Prior art keywords
programs
program
performance
running
performance interference
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.)
Ceased
Application number
PCT/CN2013/085302
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English (en)
Chinese (zh)
Inventor
赵家程
崔慧敏
冯晓兵
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Filing date
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Publication of WO2014101532A1 publication Critical patent/WO2014101532A1/fr
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Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3447—Performance evaluation by modeling

Definitions

  • Embodiments of the present invention provide a program running performance analysis method and apparatus, which are capable of analyzing performance interference of multiple concurrently running programs, thereby improving resource scheduling efficiency and utilization of hardware resources during program running.
  • the embodiment of the present invention uses the following technical solutions:
  • a method for analyzing program performance including: acquiring feature vectors of each program in an operating state in a preset program set; Obtaining respective performance interference parameters of at least two programs that are commonly run in the preset program set, where the performance interference parameters are interactions of feature vectors of at least two programs that are commonly run in the preset program set Determining a rate of decline of respective performances of at least two co-operating programs; curve fitting a feature vector of the at least two co-operating programs with performance interference parameters of the at least two co-operating programs to generate a performance interference function model.
  • the method further includes: acquiring feature vectors of at least two programs in an operating state; and operating according to the at least two programs The lower feature vector and the performance interference function model calculate respective performance interference parameters of the at least two programs in an operating state.
  • the competitive characteristics of the shared resources including: shared cache, shared prefetcher, shared memory, shared bandwidth, and shared input and output devices.
  • the second aspect provides a program running performance analyzing apparatus, including: a feature vector acquiring unit, configured to acquire a feature vector of each program in an operating state in a preset program set; and a parameter acquiring unit, configured to acquire the a performance interruption parameter of each of the at least two programs running in the preset program, the performance interference parameter being the program of the at least two co-operating programs in the preset program set acquired by the feature vector acquisition unit a rate of decrease of respective performances of the at least two co-operating programs when the feature vector interacts; a curve fitting unit, configured to acquire a feature vector of the at least two co-operating programs acquired by the feature vector acquiring unit The performance interference parameter of the at least two co-operating programs acquired by the parameter obtaining unit performs curve fitting to generate a performance interference function model.
  • the device further includes: the feature vector acquiring unit, configured to acquire feature vectors of at least two programs in an operating state; and a parameter calculating unit, configured to: And acquiring, according to the feature vector obtained by the feature vector acquiring unit, the feature vector of the at least two programs in an operating state and the curve fitting unit
  • the performance interference function model calculates respective performance interference parameters of the at least two programs in an operating state.
  • the curve fitting unit includes: a parameter storage subunit, configured to acquire the pre-obtained by the parameter obtaining unit a performance interference parameter of each of the at least two co-operating programs is added to the spatial coordinate system; a function setting sub-unit is configured to store all performances of the sub-unit into the spatial coordinate system according to the parameter a data amount of the interference parameter and a feature vector setting function form of the program in the preset program acquired by the feature vector acquiring unit; a curve fitting subunit, configured to perform according to the space coordinate system The function form set by the function setting subunit is configured to acquire the feature vector of the at least two cooperating programs acquired by the feature vector acquiring unit and the at least two acquired by the parameter acquiring unit The performance of the running program interferes with the parameters to perform curve fitting, obtains a fitting curve, and generates a performance interference function model.
  • the program running performance analysis device acquires a feature vector of each program in a running state in a preset program set.
  • the feature vector described in this embodiment is a competitive feature of the shared resource in the running state of the program, where the shared resource includes: a shared cache, a shared prefetcher, a shared memory, a shared bandwidth, and Shared input and output devices.
  • step 101 is mainly for quantifying the behavior characteristics of the input program, and the behavior characteristics of the input program are represented here by feature vectors.
  • the feature vector of the program refers to the demand characteristics of the shared resource when the program is running.
  • the shared resource includes: shared cache, shared prefetcher, shared memory, shared bandwidth broadband, and shared input and output devices.
  • the program's competitive features for the shared cache can use the program cache hit rate, the number of cache misses per unit time, or the number of cache misses per million instructions.
  • the competitive features of the shared prefetcher can be characterized by the number of prefetches per unit time or the number of prefetches per million instructions; the bandwidth characteristics of the shared bandwidth are characterized by bandwidth traffic;
  • the contention of the memory is characterized by the amount of memory consumed;
  • the competing characteristics of the shared input and output device are characterized by the number of times the input and output read and write requests are made, and the number of bytes per read and write request.
  • the program running performance analysis device acquires respective performance interference parameters of at least two programs that are commonly run in a preset program set, where the performance interference parameter is a feature vector of at least two programs that are commonly run in a preset program set.
  • the rate of decline in performance of each of the at least two co-operating programs Specifically, if m programs are randomly selected from the program (the selected programs can be repeatedly selected) as a workload, the performance degradation ratio of each program when the m programs are run together is detected, and the obtained m groups are obtained.
  • the data is added to the spatial coordinate system.
  • the performance interference parameter of the program can be expressed as a function ⁇ 1 ''...' - '): 7 ⁇ ' ⁇ 1 '...' ⁇ " 1 - ') , the meaning of the function formula indicates that at least two are running together
  • the performance of the program interferes with the function vector of the program itself and the function vector of other programs running together.
  • ⁇ ⁇ ' ⁇ ' ⁇ ⁇ »- 1 means ⁇ 'and program '...' 1 performance changes during joint operation, and the order of '...' - 1 does not affect the value of the performance interference parameter (ie, the arbitrarily changing the order of the feature vectors other than c'' does not affect the performance change of c'').
  • the program running performance analysis device performs curve fitting on a feature vector of at least two commonly running programs and a performance interference parameter of at least two commonly running programs to generate a performance interference function model. Further, as shown in FIG. 2, step 203 specifically includes:
  • the program running performance analysis device performs curve fitting on the feature vector of the at least two co-operating programs and the performance interference parameter of the at least two co-operating programs according to the set function form in the space coordinate system to obtain a fitting curve.
  • Generate a performance interference function model is a value of a feature vector of at least two programs that are commonly operated, and the function value is a performance degradation rate Pif of the program of interest in at least two programs running together (t i 1 And you can use different fitting tools, such as Ma tl ab (matrix lab), Or ig in (scientific drawing, data analysis software) and other graphic data analysis tools.
  • the method further includes: after obtaining the performance interference function model, performing performance interference analysis on any at least two programs that are commonly run according to the performance interference function model.
  • the program running performance analysis device acquires a feature vector of at least two programs in an operating state. Perform a program profiling of all the procedures mentioned in step 104 to collect the feature vectors of all the programs parsed by the process.
  • the curve fitting unit 3 3 is configured to curve the feature vector of the at least two commonly-run programs acquired by the feature vector acquiring unit 31 and the performance interference parameter of at least two commonly-run programs acquired by the parameter acquiring unit 32. Combine, generate a performance interference function model.
  • the device further includes: a parameter calculation unit 34, wherein: the feature vector obtaining unit 31 is further configured to acquire feature vectors of at least two programs in an operating state.
  • the parameter calculation unit 34 is configured to calculate at least two programs in the running state according to the feature vector of the at least two programs acquired by the feature vector acquiring unit 31 in the running state and the performance interference function model fitted by the curve fitting unit 33 The respective performance interference parameters.
  • the feature vector mentioned in the embodiment of the present invention is a competitive feature of the shared resource in the running state of the program, where the shared resource includes: a shared cache, a shared prefetcher, a shared memory, a shared bandwidth, and Shared input and output devices.
  • the curve fitting unit 3 3 further includes: a parameter storage sub-unit 331, a function setting sub-unit 332, and a curve-fitting sub-unit 333, wherein: the parameter storage sub-unit 331 is configured to use at least two of the preset programs acquired by the parameter obtaining unit 32 to operate together.
  • the respective performance interference parameters of the program are added to the spatial coordinate system.
  • the function setting sub-unit 332 is configured to set, according to the data quantity of all performance interference parameters added to the space coordinate system by the parameter storage sub-unit 331 and the feature vector of the program in the preset program set acquired by the feature vector acquiring unit 31.
  • the curve fitting sub-unit 333 is configured to acquire the feature vector and parameter acquiring unit 32 of the at least two commonly-run programs acquired by the feature vector acquiring unit 31 according to the function form set by the function setting sub-unit 332 in the space coordinate system.
  • the performance interference parameters of at least two commonly-run programs are obtained by curve fitting, and a fitting curve is obtained to generate a performance interference function model.
  • the parameter obtaining unit 32 is further configured to repeatedly obtain the performance interference parameters of the at least two programs that are commonly run in the preset program set, and add the re-acquired performance interference parameters to the space through the parameter storage sub-unit 331. Coordinate system, until the number of performance interference parameters in the space coordinate system reaches a predetermined threshold.
  • the program running performance analysis device provided by the embodiment of the present invention generates a performance interference function model by curve fitting the feature vector and the performance interference parameter of at least two commonly-run programs, and then multi-channels through the performance interference function model. The performance of each program running at the same time is analyzed to improve the efficiency of resource scheduling and the utilization of hardware resources during the running of the program.
  • the processor 51 can be: a central processing unit (CPU), an application specific integrated circuit (ASIC), a digital signal processor (DSP), an off-the-shelf programmable gate array (FPGA), or the like. Programmable logic device.
  • the communication interface 54 is used to connect the program running performance analysis device and the communication network, and the communication network includes: an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), or the like.
  • the memory 52 can be any available medium that can be accessed by a computer, including but not limited to: read only memory (ROM), random access memory (RAM), or disk storage, flash memory.
  • the parameter obtaining unit 522 is configured to acquire a performance interference parameter of each of the at least two programs that are commonly run in the preset program set, where the performance interference parameter is at least two common in the preset program set acquired by the feature vector acquiring unit 521.
  • the feature vector of the running program interacts with the rate of decline of the respective performance of at least two programs that are running together.
  • the curve fitting unit 523 is configured to perform curve fitting on the feature vector of the at least two commonly-run programs acquired by the feature vector acquiring unit 521 and the performance interference parameter of the at least two commonly-run programs acquired by the parameter acquiring unit 522 , Generate a performance interference function model.
  • the memory 52 further includes: a parameter calculation unit 524, where: The feature vector obtaining unit 521 is further configured to acquire feature vectors of at least two programs in an operating state.
  • the parameter calculation unit 524 is configured to calculate at least two programs in the running state according to the feature vector of the at least two programs acquired by the feature vector acquiring unit 521 in the running state and the performance interference function model fitted by the curve fitting unit 523.
  • the respective performance interference parameters are included in the embodiment of the present invention.
  • the feature vector mentioned in the embodiment of the present invention is a competitive feature of the shared resource in the running state of the program, where the shared resource includes: a shared cache, a shared prefetcher, a shared memory, a shared bandwidth, and Shared input and output devices.
  • the curve fitting unit 523 further includes: a parameter storage subunit, a function setting subunit, and a curve fitting subunit, wherein: the parameter storage subunit, the pre-acquisition obtained by the parameter obtaining unit 522
  • the performance interference parameters of at least two programs running together are added to the spatial coordinate system.
  • a function setting subunit, a data amount for all performance interference parameters added to the spatial coordinate system according to the parameter storage subunit, and a feature vector setting function of the program in the preset program acquired by the feature vector obtaining unit 521 form.
  • a curve fitting sub-unit configured to obtain, in the spatial coordinate system, the feature vector of the at least two commonly-running programs acquired by the feature vector acquiring unit 521 according to the function form set by the function setting sub-unit and the parameter acquiring unit 522
  • the performance interference parameters of at least two co-operating programs are curve-fitted to obtain a fitted curve, and a performance interference function model is generated.
  • the parameter obtaining unit 522 is further configured to repeatedly obtain performance interference parameters of at least two programs that are commonly run in the preset program set, and add the re-acquired performance interference parameters to the space coordinates through the parameter storage subunit. System, until the number of performance interference parameters in the spatial coordinate system reaches a predetermined threshold.
  • the program running performance analysis device provided by the embodiment of the present invention generates a performance interference function model by curve fitting the feature vector and the performance interference parameter of at least two commonly-run programs, and then multi-channels through the performance interference function model.
  • the performance of each program running at the same time is analyzed to improve the efficiency of resource scheduling during the running of the program. And utilization of hardware resources.

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Debugging And Monitoring (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention se rapporte à un procédé et à un dispositif adaptés pour analyser les performances d'exécution d'un programme. L'invention appartient au domaine technique de réseaux. Elle a pour objectif d'exécuter une analyse des interférences sur les performances respectives de programmes multicanal qui s'exécutent en même temps, dans le but d'améliorer une efficacité de programmation de ressources ainsi que le taux d'utilisation des ressources matérielles durant le processus d'exécution des programmes. Le procédé selon l'invention consiste : à acquérir un vecteur caractéristique de chaque programme qui se trouve dans un état d'exécution, dans un ensemble de programmes prédéfini; à acquérir des paramètres d'interférence sur les performances respectives d'au moins deux programmes qui s'exécutent en même temps dans l'ensemble de programmes défini, les paramètres d'interférence sur les performances correspondant à des taux de diminution de performances respectifs d'au moins deux programmes qui s'exécutent en même temps quand les vecteurs caractéristiques d'au moins deux programmes qui s'exécutent en même temps dans l'ensemble de programmes défini interagissent mutuellement; et à exécuter une adaptation de courbe sur les vecteurs caractéristiques d'au moins deux programmes qui s'exécutent en même temps ainsi que sur les paramètres des interférences sur les performances d'au moins deux programmes qui s'exécutent en même temps, de sorte à générer un modèle de fonction d'interférence sur les performances. La présente invention est mise en œuvre pour exécuter une analyse des interférences sur les performances de programmes.
PCT/CN2013/085302 2012-12-26 2013-10-16 Procédé et dispositif pour analyser les performances d'exécution d'un programme Ceased WO2014101532A1 (fr)

Applications Claiming Priority (2)

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CN201210576264.2A CN103902443B (zh) 2012-12-26 2012-12-26 一种程序运行性能分析方法及装置
CN201210576264.2 2012-12-26

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WO2021012511A1 (fr) * 2019-07-25 2021-01-28 平安科技（深圳）有限公司 Procédé et appareil d'ajustement de taux d'utilisation de cpu sur la base d'un contrôleur pid, terminal et support

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CN104424101B (zh) * 2013-09-10 2017-08-11 华为技术有限公司 程序性能干扰模型的确定方法及设备
CN106997367B (zh) * 2016-01-26 2020-05-08 华为技术有限公司 程序文件的分类方法、分类装置和分类系统
CN110178123B (zh) * 2017-07-12 2020-12-01 华为技术有限公司 性能指标评估方法及装置
CN113672489B (zh) * 2021-10-25 2022-01-25 国家超级计算天津中心 超级计算机的资源性能等级确定方法及设备

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CN103902443B (zh) 2017-04-26

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