Computing device performance analysis method and computing device

Abstract

The invention provides a computing device performance analysis method and a computing device. In the embodiment, the method comprises the following steps: obtaining a to-be-analyzed performance feature of the computing device and an operating environment of the computing device; based on the to-be-analyzed performance features and the operation environment, searching a feature collection tool through a first AI tool, the first AI tool being used for searching the feature collection tool suitable for the operation environment; based on a search result returned by the first AI tool, running the searched feature collection tool, and collecting feature data of the to-be-analyzed performance features by using the feature collection tool; and determining a performance analysis result of the computing device based on the feature data. According to different performance features and the operation environment of the computing device, the feature collection tool can be flexibly selected, and the feature collection convenience is improved.

Description

Technical Field

[0001] The present application relates to the field of server technology, and in particular to a computing device performance analysis method and a computing device. Background Art

[0002] Performance optimization is an integral part of high-performance computing (HPC) systems. It helps improve task completion time, saves power, space and other resources consumed by system operation, improves system reliability and security, and maximizes the use of existing hardware resources.

[0003] For HPC systems, feature analysis is a necessary step for performance optimization; therefore, in HPC systems, collecting feature analysis data and analyzing HPC system bottlenecks are crucial for performance optimization.

[0004] The current performance analysis process of HPC systems is relatively complicated, so a more convenient performance analysis method is urgently needed. Summary of the invention

[0005] The embodiments of the present application provide a computing device performance analysis method and a computing device. According to the performance characteristics and the operating environment of the computing device, an AI tool can be used to flexibly search for feature collection tools that are adapted to the operating environment and performance characteristics, thereby improving the convenience and accuracy of selecting feature collection tools, and subsequently performing performance analysis based on the feature data collected by the feature collection tools; the AI ​​tool can be used to flexibly select various feature collection tools to collect feature data for various performance characteristics, thereby improving the convenience of performance analysis.

[0006] In a first aspect, an embodiment of the present application provides a method for analyzing performance of a computing device, the method comprising:

[0007] Acquire the performance characteristics to be analyzed of the computing device and the operating environment of the computing device; based on the performance characteristics to be analyzed and the operating environment, search for a feature collection tool through a first AI tool; wherein the AI ​​tool is used to search for a feature collection tool suitable for the operating environment; based on the search results returned by the first AI tool, run the searched feature collection tool, and use the feature collection tool to collect feature data of the performance characteristics to be analyzed; based on the feature data, determine the performance analysis result of the computing device.

[0008] In this solution, according to the performance characteristics and the operating environment of the computing device, AI tools can be used to flexibly search for feature collection tools that are adapted to the operating environment and performance characteristics, thereby improving the convenience and accuracy of selecting feature collection tools, and subsequently performing performance analysis based on the feature data collected by the feature collection tools; AI tools can be used to flexibly select various feature collection tools to collect feature data for various performance characteristics, thereby improving the convenience of performance analysis.

[0009] In a possible implementation, obtaining the performance characteristics to be analyzed of the computing device includes: obtaining the performance characteristics to be analyzed of the computing device set by the user based on a human-computer interaction interface.

[0010] In this solution, the user only needs to input the performance characteristics related to the computing device performance, and does not need to care about the situation of the characteristic collection tool, which reduces the user's participation in performance analysis and improves the user experience. It should be noted that the user can input various performance characteristics related to the computing device performance according to actual needs.

[0011] In a possible implementation, searching for a feature collection tool through a first AI tool based on the performance feature to be analyzed and the operating environment includes: sending a search request to the first AI tool; the search request includes the performance feature and the operating environment, and the search request is used to request a search for a feature collection tool for the performance feature in the operating environment;

[0012] Based on the search results returned by the first AI tool, running the searched feature collection tool includes: if the search results of the first AI tool in response to the search request are accurate, running the feature collection tool indicated by the search results on the computing device.

[0013] In a possible implementation, the method further includes: when an error occurs in the search results, sending the search request to a second AI tool.

[0014] In one possible implementation, the first AI tool has a higher number of successes than the second AI tool, and / or the first AI tool has a lower search time than the second AI tool. The number of successes is used to indicate the number of accurate search results for different search requests, and the search time is used to indicate the time consumed in responding to different search requests. The performance characteristics and / or operating environments in different search requests are different.

[0015] In this solution, we first consider AI tools that are accurate and / or fast in searching, thereby improving the efficiency of searching for feature collection tools.

[0016] In a possible implementation, the search results include a download link of the feature collection tool, a compilation method of the feature collection tool, an installation method of the feature collection tool, and an operation method of the feature collection tool;

[0017] Based on the search results returned by the first AI tool, run the searched feature collection tools, including:

[0018] Download the feature collection tool based on the download link; compile the feature collection tool into an executable file based on the compilation method; install the executable file in the operating environment based on the installation method; and run the executable file in the operating environment based on the running method.

[0019] In a possible implementation, before using a feature collection tool to collect feature data of the performance feature to be analyzed, the method further includes: obtaining an operation method of the business software; and running the business software based on the operation method.

[0020] In a possible implementation, the operating environment of the computing device includes the architecture of the central processing unit and the version of the operating system.

[0021] In one possible implementation, the computing device performance analysis method is executed by a computing device;

[0022] The first AI tool search feature acquisition tool is executed by a device other than a computing device.

[0023] In the second aspect, the embodiment of the present application provides a computing device performance analysis device, which includes several modules, each of which is used to execute each step in the computing device performance analysis method provided in the first aspect of the embodiment of the present application, and the division of modules is not limited here. For the specific functions performed by each module of the computing device performance analysis device and the beneficial effects achieved, please refer to the functions of each step of the computing device performance analysis method provided in the first aspect of the embodiment of the present application, which will not be repeated here.

[0024] For example, the computing device performance analysis apparatus includes:

[0025] An acquisition module, used to acquire the performance characteristics to be analyzed of the computing device and the operating environment of the computing device;

[0026] A search module, configured to search for a feature collection tool through a first AI tool based on the performance feature to be analyzed and the operating environment; wherein the AI ​​tool is configured to search for a feature collection tool suitable for the operating environment;

[0027] A collection module, configured to run the searched feature collection tool based on the search results returned by the first AI tool, and collect feature data of the performance feature to be analyzed using the feature collection tool;

[0028] The analysis module is used to determine the performance analysis result of the computing device based on the characteristic data.

[0029] In a possible implementation, the acquisition module is used to acquire the performance characteristics to be analyzed of the computing device set by the user based on the human-computer interaction interface.

[0030] In a possible implementation, the search module is used to send a search request to the first AI tool; the search request includes a performance feature and an operating environment, and the search request is used to request description information of a feature collection tool for the performance feature in the operating environment;

[0031] The collection module is used to run a feature collection tool indicated by the search result on the computing device when the search result of the first AI tool in response to the search request is accurate.

[0032] In a possible implementation, the search module is configured to send a search request to a second AI tool when an error occurs in the search results.

[0033] In one possible implementation, the first AI tool has a higher number of successes than the second AI tool, and / or the first AI tool has a lower search time than the second AI tool. The number of successes is used to indicate the number of accurate search results for different search requests, and the search time is used to indicate the time consumed in responding to different search requests. The performance characteristics and / or operating environments in different search requests are different.

[0034] In a possible implementation, the search results include a download link of the feature collection tool, a compilation method of the feature collection tool, an installation method of the feature collection tool, and an operation method of the feature collection tool;

[0035] The collection module is used to download the feature collection tool based on the download link; compile the feature collection tool into an executable file based on the compilation method; install the executable file in the running environment based on the installation method; and run the executable file in the running environment based on the running method.

[0036] In a possible implementation, before using a feature collection tool to collect feature data of the performance feature to be analyzed, the collection module is also used to obtain an operating method of the business software; and based on the operating method, the business software is operated.

[0037] In a possible implementation, the operating environment of the computing device includes the architecture of the central processing unit and the version of the operating system.

[0038] In one possible implementation, the computing device performance analysis method is executed by a computing device;

[0039] The first AI tool search feature acquisition tool is executed by a device other than a computing device.

[0040] In a third aspect, an embodiment of the present application provides a computing device performance analysis apparatus, comprising: at least one memory for storing programs; and at least one processor for executing programs stored in the memory, wherein when the program stored in the memory is executed, the processor is used to execute the method provided in the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computing device performance analysis apparatus, characterized in that the apparatus runs computer program instructions to execute the method provided in the first aspect. Exemplarily, the apparatus may be a chip or a processor.

[0042] In one example, the device may include a processor, which may be coupled to a memory, read instructions in the memory and execute the method provided in the first aspect according to the instructions. The memory may be integrated in a chip or a processor, or may be independent of the chip or the processor.

[0043] In a fifth aspect, an embodiment of the present application provides a computer storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the method provided in the first aspect.

[0044] In a sixth aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method provided in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a system architecture diagram of a feature collection system provided in an embodiment of the present application;

[0046] Figure 2 This is a system architecture diagram of another feature collection system provided in an embodiment of the present application;

[0047] Figure 3 It is a flowchart of a computing device performance analysis method provided in an embodiment of the present application;

[0048] Figure 4 is a schematic diagram of a first AI tool search provided in an embodiment of the present application;

[0049] Figure 5 is a schematic diagram of sequential searching of n AI tools provided in an embodiment of the present application;

[0050] Figure 6a This is a schematic diagram of an accurate search result of a first AI tool provided in an embodiment of the present application;

[0051] Figure 6b It is a schematic diagram of accurate search results of another first AI tool provided in an embodiment of the present application;

[0052] Figure 7 is a schematic diagram of starting a second AI tool search provided by an embodiment of the present application;

[0053] Figure 8a is a schematic diagram of starting a second AI tool search provided by an embodiment of the present application;

[0054] Figure 8b is another schematic diagram of starting a second AI tool search provided by an embodiment of the present application;

[0055] Fig. 9 is a schematic diagram of parallel search of n AI tools provided in an embodiment of the present application;

[0056] Fig.10a is a schematic diagram of determining a performance analysis result of a computing device 102 provided in an embodiment of the present application;

[0057] Fig.10b is a schematic diagram of determining a performance adjustment suggestion for a computing device 102 provided in an embodiment of the present application;

[0058] Fig.10c is a schematic diagram of determining performance analysis results and performance adjustment suggestions for a computing device 102 provided in an embodiment of the present application;

[0059] Fig.10d It is a schematic diagram of generating a document provided in an embodiment of the present application;

[0060] Fig.11 is a schematic diagram of a first AI tool provided in an embodiment of the present application running on a management device 103;

[0061] Fig.12 is a schematic diagram of software modules in the feature acquisition system provided in an embodiment of the present application;

[0062] Fig.13 is a schematic diagram of an AI search module and an AI analysis module provided in an embodiment of the present application being located in a management device 103;

[0063] Fig.14 is a schematic diagram of another computing device performance analysis method provided in an embodiment of the present application;

[0064] Fig.15 It is a structural diagram of a computing device performance analysis apparatus provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0066] In the description of the embodiments of the present application, words such as "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a concrete way.

[0067] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the term "multiple" means two or more. For example, multiple systems refers to two or more systems, and multiple terminals refers to two or more terminals.

[0068] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprises", "has" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0069] Below, some terms in this embodiment are explained. It should be noted that these explanations are for the convenience of understanding by those skilled in the art and do not constitute a limitation on the scope of protection claimed in this application.

[0070] High-performance computing (HPC) is a computer cluster designed specifically for high-performance computing scenarios such as scientific computing and engineering simulation. It uses a large number of high-performance processors, high-speed networks, high-capacity storage and other technologies to form a highly parallel computing platform. It solves complex problems that traditional single machines cannot solve by providing massive data processing capabilities, ultra-high computing performance, and ultra-long-term operation capabilities.

[0071] Artificial Intelligence (AI): It is an important driving force for a new round of scientific and technological revolution and industrial transformation. It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

[0072] x86: refers to a series of central processing unit instruction set architectures based on Intel 8086 and backward compatible.

[0073] ARM architecture: formerly known as Advanced RISC Machine (Advanced RISC Machine) and earlier known as AcornRISC Machine, is a 32-bit reduced instruction set (RISC) processor architecture.

[0074] Regular Expression: Also known as regular expression (Regular Expression, often abbreviated as regex, regexp or RE in the code), it is a text pattern and also a concept in computer science, which includes common characters (for example, letters from a to z) and special characters (called "metacharacters"). Regular expressions use a single string to describe and match a series of strings that match a certain syntactic rule, and are usually used to retrieve and replace text that meets a certain pattern (rule).

[0075] Next, a feature collection system that may be applied to the computing device performance analysis method provided in the embodiment of the present application is introduced. Figure 1 The following is an example diagram of the architecture of a feature collection system provided by an embodiment of the present application. The present application embodiment provides a computing device performance analysis method that can be applied to Figure 1 As shown in the system architecture diagram. Figure 1 As shown, the feature collection system includes a terminal 101 and a computing device 102. The terminal 101 communicates with the computing device 102 through a network. The network may be a wired network and / or a wireless network. It is understood that the network may use any known network communication protocol to achieve different communications, and the network communication protocol may be various wired and / or wireless communication protocols.

[0076] Among them, the terminal 101 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. Exemplary embodiments of the terminal 101 involved in this solution include, but are not limited to, electronic devices equipped with iOS, Android, Windows, Harmony OS, or other operating systems. The embodiment of this application does not specifically limit the type of electronic device.

[0077] The computing device 102 may be used to provide services such as HPC services and cloud services. It may be a server or a hyperterminal that can establish a communication connection with other devices and provide computing functions and / or storage functions for other devices. In addition, the computing device 102 may also be a server cluster formed by multiple servers. The server involved in this solution may be a hardware server or may be implanted in a virtualized environment. For example, the server involved in this solution may be a virtual machine executed on a hardware server including one or more other virtual machines.

[0078] The computing device 102 can be a single-node server, such as a rack server; it can also be a computing node in a multi-node server. The multi-node server can be a blade server, a high-density server or a whole cabinet server (an independent product formed by integrating the original architecture in which the rack and the machine, such as the server node, are separated).

[0079] For example, Figure 1 As shown, the computing device 102 may include a power supply 121 and a mainboard 110 , wherein the power supply 121 is electrically connected to the mainboard 110 to supply power to devices connected to the mainboard 110 .

[0080] In some possible embodiments, the devices connected to the mainboard 110 may include a processor 111 , a memory 112 , and a network card 122 .

[0081] The processor 111 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 111 may also be a controller with computing capabilities.

[0082] Among them, the memory 112 can be inserted into a memory slot to connect to the motherboard 110. The memory 112 can be a random access memory (RAM). By way of example but not limitation, many forms of RAM are available. Exemplarily, the memory 112 includes at least two types of memories, for example, the memory can be either a random access memory (RAM) or a read-only memory (ROM). For example, the RAM can include a dynamic random access memory (DRAM) or a storage class memory (SCM) and other memories.

[0083] The network card 122 is used to communicate with other devices such as the terminal 101 or Figure 2 Communicates with the management device 102 shown in FIG.

[0084] It should be noted that Figure 1 The computing device 102 is merely an example and does not constitute a specific limitation. In practical applications, it may include Figure 1 More or fewer devices. For example, the feature collection system only includes the computing device 102.

[0085] In other possible situations, such as Figure 2 As shown, the feature collection system may further include a management device 103, which is used to manage the computing device 102, for example, providing one or more AI tools to the computing device 102, etc. The details of the AI ​​tools are described below and will not be repeated here. The structure of the management device 103 can be found in the description of the computing device 102 above and will not be repeated here.

[0086] Next, in combination with the feature acquisition system provided above, a computing device performance analysis method provided in an embodiment of the present application is introduced in detail.

[0087] Figure 3 is a flow chart of a computing device performance analysis method provided in an embodiment of the present application. This embodiment can be applied to a feature acquisition system. Figure 3 As shown, the computing device performance analysis method provided in the embodiment of the present application includes at least the following steps:

[0088] Step 301: The computing device 102 obtains the performance characteristics to be analyzed of the computing device 102 .

[0089] Performance characteristics are used to illustrate the characteristics related to the performance of the computing device 102 that need to be analyzed. For example, characteristics related to disk I / O (Input / Output), memory, or processor. Disk I / O refers to data reading and writing operations involving disks in a computer system. Disk I / O is an important way to exchange data between computers and storage devices. When a computer needs to read data from a disk, it initiates a read request, and the disk will find and transfer the data to the computer's memory; when the computer needs to write data to a disk, it initiates a write request to write the data from the memory to the disk. In an embodiment of the present application, a performance characteristic can describe several characteristics, such as one characteristic or multiple characteristics.

[0090] For example, for disk I / O, the characteristics described by the performance characteristics may include one or more of the following: input / output per second (IOPS), data throughput, type of IO request (such as read / write request), request size, request mode (continuous or random), and physical characteristics of the disk (such as rotation speed, cache size, etc.).

[0091] For example, for memory, the characteristics described by the performance characteristics may include one or more of the following: memory capacity, memory transfer speed (measured in megabytes per second (MB / s) or gigabytes per second (GB / s)), memory latency (the time required from the processor requesting data to obtaining a response), memory bandwidth (the maximum amount of data that the memory can transfer), memory channels (the number of connections between the memory module and the processor), memory frequency (representing the highest operating frequency that the memory can reach, measured in MHz), and memory timing (referring to the timing characteristics of the memory interface signal, including indicators such as Tras, Trp, and Trcd, all of which refer to the delay time of the memory interface transmission signal. The smaller the timing, the better the memory performance).

[0092] For example, for the CPU, the characteristics described by the performance characteristics may include one or more of the following: main frequency (the number of times the CPU executes instructions per second, measured in Hertz. The higher the main frequency, the faster the CPU executes instructions), number of CPU cores (the CPU core is the main computing unit of the processor, and a multi-core CPU can execute multiple tasks simultaneously to improve processing power), CPU cache size (the CPU cache is a high-speed memory located inside the CPU, used to temporarily store data and instructions), instruction set (the instruction set is a set of instructions supported by the CPU. Different instruction sets can provide different instructions and functions, affecting the functions and performance of the CPU), floating-point operation performance (referring to the CPU's ability to perform floating-point operations), and number of instructions per cycle (Instructions PerCycle, IPC, indicating the number of instructions that the CPU can complete in each clock cycle).

[0093] There may be multiple ways to obtain the performance characteristics of the computing device 102 to be analyzed.

[0094] In some possible implementations, computing device 102 may provide an interface such as an application programming interface (API), and a user may operate terminal 101 to access the interface and input performance characteristics to be analyzed for computing device 102 . Subsequently, terminal 101 sends the performance characteristics input by the user to computing device 102 .

[0095] In some other possible implementations, the computing device 102 may provide a human-computer interaction interface, and the user may operate the terminal 101 to display the human-computer interaction interface and input performance characteristics to be analyzed for the computing device 102 in the human-computer interaction interface. Subsequently, the terminal 101 sends the performance characteristics input by the user to the computing device 102.

[0096] In some other possible implementations, the management device 103 may provide a human-computer interaction interface or interface, and the user may operate the terminal 101 to access the interface or display the human-computer interaction interface, and input the performance characteristics to be analyzed for the computing device 102; for example, assuming that the management device 103 manages multiple computing devices 102, the user may input the model of the computing device 102 and the performance characteristics to be analyzed corresponding to the model, and subsequently, the management device 103 may send the performance characteristics to be analyzed to each computing device 102 under the model.

[0097] Step 302 : The computing device 102 obtains the operating environment of the computing device 102 .

[0098] In the embodiment of the present application, the operating environment of the computing device 102 is used to describe the hardware environment and software environment of the computing device 102. For example, the operating environment of the computing device 102 may include the processor architecture of the CPU and the operating system version; wherein the processor architecture may be x86 or ARM, and the operating system version is used to describe the type and version number of the operating system, such as the Windows version, and for example, the Linux version.

[0099] It should be noted that the execution order of step 301 and step 302 is not limited, and step 302 can be executed before, after or simultaneously with step 301.

[0100] Step 303: The computing device 102 searches for a feature collection tool through a first AI tool based on the performance characteristics and the operating environment; wherein the AI ​​tool is used to search for a feature collection tool suitable for the operating environment.

[0101] In some possible embodiments, Figure 4 As shown, the computing device 102 determines a search request based on the performance characteristics and the operating environment, the search request including the performance characteristics and the operating environment, the search request being used to request descriptive information of a feature collection tool for the performance characteristics under the operating environment; sends the search request to the first AI tool; the first AI tool searches for the descriptive information of the feature collection tool in response to the search request and returns search results.

[0102] Among them, AI tools refer to various software and applications developed using artificial intelligence technology, which can simulate and learn human behavior and thinking, and use this information to automate tasks or improve existing business processes. In the embodiment of the present application, the first AI tool is a tool with a search function, which is used to search according to user requirements. For example, the first AI tool can be chatgpt, or Baidu Wenxin and other tools with search functions. The first AI tool can use open source tools or customized development, and can be designed in combination with actual needs. It should be noted that the first AI tool can search for feature collection tools in a way that: based on the performance characteristics, operating environment and description information of the feature collection tool in the search request, request the server to search for feature collection tools, and receive the search results returned by the server; for example, if the first AI tool is Baidu Wenxin, the first AI tool can request the server of Baidu Wenxin to search for feature collection tools based on the performance characteristics, operating environment and description information of the feature collection tool in the search request. The server of Baidu Wenxin searches for feature collection tools for performance characteristics under the operating environment to obtain search results, and feeds the search results back to the first AI tool. In some possible implementations, the first AI tool may include a request template, the request template includes a first blank field, a second blank field, and a third blank field, the first blank field is used to fill in the performance characteristics, the second blank field is used to fill in the operating environment, and the third blank field is used to fill in the description information of the feature collection tool; subsequently, the first AI tool fills the performance characteristics, operating environment, and description information of the feature collection tool into the first blank field, the second blank field, and the third blank field based on the search request, obtains the request under the request template, sends the request under the request template to the server, and the server is used to search for the feature collection tool to obtain the search results, and feeds the search results back to the first AI tool. For example, if the first AI tool is Baidu Wenxin, the first AI tool can fill the description information of the performance characteristics, operating environment, and feature collection tool in the search request into the request module to obtain the request under the request template, and send the request to the server of Baidu Wenxin, and the server of Baidu Wenxin searches based on the request and feeds the search results back to the first AI tool. It can be seen from this that in the embodiment of the present application, the search process of the AI ​​tool does not require human intervention, and can automatically search for feature collection tools for performance characteristics in the operating environment of the computing device 102, thereby improving the convenience of selecting feature collection tools.

[0103] In the embodiment of the present application, the description information of the feature collection tool may be the download link, compilation method, installation method and operation method of the feature collection tool, and the search request may be used to request to search for the download link, compilation method, installation method and operation method of the feature collection tool. Figure 4As shown, the search results may include the download link, compilation method, installation method, and operation method of the feature collection tool. It should be noted that the description information of the feature collection tool is user-defined; it can be flexibly designed in combination with actual needs, and the embodiments of the present application do not specifically limit this. It should be noted that the first AI tool may include a request template, and the format of the content filled in the third blank field can be flexibly designed in combination with the actual request. For example, it can be in the form of key-value. For example, the third blank field can be "Download link:; Compilation method:; Installation method:; Operation method:".

[0104] In some possible cases, the first AI tool is any one of n (greater than or equal to 2) AI tools; in some optional examples, the computing device 102 includes a sequence of n AI tools; assuming that the sequence of the n AI tools is: AI tool 1, AI tool 2, ..., AI tool n, then in some possible scenarios, the computing device 102 searches for the feature collection tool in the sequence of the n AI tools, which is specifically implemented as follows:

[0105] like Figure 5 As shown, the computing device 102 can send a search request to AI tool 1 that is in the first position in the order of n AI tools. If the search result returned by AI tool 1 in response to the search request is accurate, the search is terminated. If the search result returned by AI tool 1 in response to the search request is erroneous, the computing device 102 can send a search request to AI tool 2 that is in the second position. If the search result returned by AI tool 2 in response to the search request is accurate, the search is terminated. If the search result returned by AI tool 2 in response to the search request is erroneous, the computing device 102 can send a search request to AI tool 3 that is in the second position, and the cycle is repeated.

[0106] like Figure 6a As shown, the accuracy of the search results can be understood as the successful parsing of the search results, the download link of the feature collection tool in the search results can successfully download the feature collection tool, the compilation method of the feature collection tool in the search results can successfully compile the feature collection tool into an executable file, the installation method of the feature collection tool in the search results can successfully install the executable file compiled by the feature collection tool, and the running method of the feature collection tool in the search results can successfully run the executable file compiled by the feature collection tool. The successful parsing of the search results can be understood as the ability to extract the key content in the search results: the download link, compilation method, installation method, and running method of the feature collection tool.

[0107] The error in the search results may be that the parsing of the search results fails, the download link of the feature collection tool in the search results cannot download the feature collection tool, the compilation method of the feature collection tool in the search results can compile the feature collection tool into an executable file, the installation method of the feature collection tool in the search results cannot install the executable file compiled by the feature collection tool, and / or the running method of the feature collection tool in the search results cannot run the executable file compiled by the feature collection tool. The failure to parse the search results can be understood as the inability to extract the key content in the search results: the download link, compilation method, installation method, and running method of the feature collection tool.

[0108] It should be noted that the method for parsing search results can be regular expression parsing; in the embodiment of the present application, the regular expression uses a string to match the string in the search results, and the string is used to represent the download link, compilation method, installation method, and operation method of the feature collection tool.

[0109] exist Figure 5 In the scenario shown, the method for the computing device 102 to determine the first AI tool is: based on the order of the n AI tools and the number of AI tools among the n AI tools that have responded to the search request when searching for feature collection tools in the order of the n AI tools, determine the first AI tool from the n AI tools; illustratively, the computing device 102 determines the number of AI tools among the n AI tools that have responded to the search request as the target sequence position, and uses the AI ​​tool with the next sequence position of the target sequence position in the order of the n AI tools as the first AI tool. For example, when the target sequence position is 0, the first AI tool is the AI ​​tool that ranks first among the n AI tools.

[0110] Among them, the order of the n AI tools can be a pre-set order, or it can be determined based on the number of successes and / or search time. The number of successes is used to indicate the number of accurate search results for different search requests, that is, the accurate search results of the AI ​​tools are counted as successful searches; the search time is used to indicate the time consumed to respond to different search requests, such as the average of the total time consumed to respond to different search requests, and the performance characteristics and / or operating environments in different search requests are different; exemplarily, the time consumed to respond to a search request can be the time from receiving the search request to obtaining the search results. Exemplarily, the order of the n AI tools can be obtained by sorting the n AI tools from large to small according to the number of successes; Exemplarily, the order of the n AI tools can be obtained by sorting the n AI tools from small to large according to the search time; Exemplarily, for each AI tool, the evaluation value of the AI ​​tool can be determined based on the number of successes and the search time, such as weighting; The order of the n AI tools can be obtained by sorting the n AI tools from large to small according to the evaluation value.

[0111] Step 304: The computing device 102 runs the searched feature collection tool based on the search results returned by the first AI tool, and uses the feature collection tool to collect feature data of the performance feature to be analyzed.

[0112] In the embodiment of the present application, if the search results of the first AI tool in response to the search request are accurate, the feature collection tool searched by the first AI tool can be successfully run based on the search results of the first AI tool in response to the search request. The feature collection tool can be understood as various software and applications with feature collection functions.

[0113] For example, Figure 6a As shown, the search request can be used to request the download link, compilation method, installation method and running method of the search feature collection tool; correspondingly, the search results may include the download link, installation method, compilation method and running method of the feature collection tool; the computing device 102 successfully parses the search results, successfully downloads the feature collection tool based on the download link of the feature collection tool, successfully compiles the feature collection tool into an executable file based on the compilation method, successfully installs the executable file compiled by the feature collection tool in the running environment of the computing device 102 based on the installation method, and successfully runs the executable file compiled by the feature collection tool in the running environment of the computing device 102 based on the running method of the feature collection tool, thereby determining that the search result of the first AI tool responding to the search request is accurate; subsequently, the running feature collection tool is directly used to collect feature data of the performance feature to be analyzed.

[0114] It is worth noting that the search request can be a plurality of requests arranged in sequence, and each of the plurality of requests is used to request a part of the download link, installation method, compilation method, and operation method of the feature collection tool, and the order of the contents requested by the plurality of requests arranged in sequence is the download link, installation method, compilation method, and operation method of the feature collection tool. In actual applications, the computing device 102 can send the first request to the first AI tool according to the plurality of requests arranged in sequence, and if the search result returned by the first AI tool is accurate, send the second request to the first AI tool, and repeat the cycle until the search result returned by the first AI tool in response to the last request is accurate. For example, if Figure 6bAs shown, the computing device 102 determines the first request based on the performance characteristics to be analyzed of the computing device 102 and the operating environment of the computing device 102. The first request can be used to request a download link, a compilation method, and an installation method for a search feature collection tool; correspondingly, the search result 11 of the first AI tool in response to the first request may include a download link, a compilation method, and an installation method for the feature collection tool. If the computing device 102 successfully parses the search result 11, successfully downloads the feature collection tool based on the download link, and successfully compiles the feature collection tool into an executable file based on the compilation method, and successfully installs the compiled executable file of the feature collection tool in the operating environment of the computing device 102 based on the installation method, it can be determined that the search content 11 of the first AI tool in response to the first request is accurate, and it can be determined that the feature collection tool is successfully installed. Subsequently, the computing device 102 may determine a second request based on the operating environment of the computing device 102 and the feature collection tool indicated by the feedback result 11. The second request may be used to request the operating method of the feature collection tool in the operating environment of the computing device 102; correspondingly, the search result 12 of the first AI tool in response to the second request may include the operating method of the feature collection tool. The computing device 102 successfully runs the executable file compiled by the feature collection tool in the operating environment based on the successful operation method, and then determines that the search result 12 of the first AI tool in response to the second request is accurate. Subsequently, the running feature collection tool is directly used to collect feature data of the performance feature to be analyzed.

[0115] In addition, Figure 5 In the scenario shown, Figure 7 As shown, when the search result (for the convenience of description and distinction, it can be referred to as search result 1) of the first AI tool in response to the search request is erroneous, the search request is sent to the second AI tool. For example, the number of successes of the first AI tool is higher than (greater than or equal to) the second AI tool, and / or the search time of the first AI tool is lower than (less than or equal to) the second AI tool, and the AI ​​tool with accurate and fast search is considered first, so as to improve the efficiency of searching for feature collection tools; subsequently, when the search result 2 of the second AI tool in response to the search request is accurate, the feature collection tool is determined based on the second search result. For details, please refer to the relevant description of the first AI tool, which will not be repeated; it should be noted that the first AI tool and the second AI tool are different, for example, the first AI tool is chatgpt, and the second AI tool is Baidu Wenxin. In the embodiment of the present application, when the search result of the first AI tool in response to the search request is erroneous, the search request continues to be sent to other AI tools, and the cycle is repeated, so as to integrate multiple search results and improve the probability of searching for feature collection tools that are adapted to the performance characteristics and the operating environment of the computing device 102.

[0116] It should be noted that in Figure 6bIn the scenario, the search request is the first request and the second request. In a possible situation, such as Figure 8a As shown, if the search result 11 of the first request by the first AI tool is wrong, the computing device 102 sends the first request to the second AI tool, and the second AI tool returns the search result 21 in response to the first request. If the search result 21 returned by the second AI tool in response to the first request is accurate, the second request is sent to the second AI tool; if the search result 22 returned by the second AI tool in response to the second request is wrong, the first request and the second request are sent to other AI tools in the aforementioned manner, and the cycle is repeated. In one possible situation, as Figure 8b As shown, if the search result 11 of the first AI tool for the first request is accurate, that is, the feature collection tool successfully installs the executable file compiled by the feature collection tool, the computing device 102 sends the second request to the first AI tool; if the search result 12 returned by the first AI tool in response to the second request is incorrect (that is, the feature collection tool cannot be run), the first request is sent to the second AI tool, and the second AI tool returns the search result 21 in response to the first request; if the search result 21 returned by the second AI tool in response to the first request is accurate, the second request is sent to the second AI tool; if the search result 22 returned by the second AI tool in response to the second request is incorrect, the first request and the second request are continued to be sent to other AI tools in the aforementioned manner, and the cycle is repeated.

[0117] It is worth noting that in actual applications, in an embodiment of the present application, the computing device 102 runs a feature collection tool based on the search request and the search results of at least some of the n (greater than or equal to 2) AI tools responding to the search request.

[0118] In some possible implementations, such as Figure 5 As shown, the computing device 102 may send search requests to the n AI tools sequentially in the order of the n AI tools.

[0119] It should be noted that the above method of determining the feature collection tool is only an example and does not constitute a specific limitation. In other possible implementation methods, such as Fig. 9 As shown, the computing device 102 may send a search request to each of the n AI tools, and then run the searched feature acquisition tool based on the search results of the n AI tools.

[0120] In a possible example, the computing device 102 determines the search results that need to be processed currently based on the order of the received search results and the number of search results that have been processed, and determines whether the search results that need to be processed currently are accurate. If they are accurate, the process ends, and the feature collection tool indicated by the search results is run based on the accurate search results. Otherwise, the process continues to determine the search results that need to be processed currently, and the cycle repeats. Exemplarily, the computing device 102 uses the number of search results that have been processed as the target order position, and uses the search results of the next order position of the target order position as the search results that need to be processed currently.

[0121] In another possible instance, the computing device 102 determines the feature collection tool with the highest number of occurrences based on all the n search results received. If it is determined based on the search results that the feature collection tool with the highest number of occurrences can be run, it will be used as the feature collection tool that is finally run. Otherwise, the feature collection tool with the second highest number of occurrences is determined whether it can be run according to the above method, and the cycle is repeated.

[0122] In a specific implementation, the computing device 102 needs to run business software. When the business software is running, a feature collection tool is used to collect feature data of the performance features to be analyzed.

[0123] Among them, the business software can be software that implements business for users, such as HPC software. HPC software is software that uses high-performance computing, such as software that implements weather forecasting, scientific research, engineering calculations, and simulations. In addition, the feature collection tool is used to collect the feature values ​​of the features described by the feature type. It should be noted that during the operation of the business software, the hardware resources of the computing device 102 will be used: CPU, memory, and disk. The performance characteristics are used to describe the characteristics related to the performance of the computing device 102. Therefore, the searched feature collection tool can collect the hardware resources of the computing device 102 used when the business software is running according to the performance characteristics. In addition, the business software needs to run with the feature collection tool, or run before the feature collection tool runs.

[0124] Considering that some business software is developed by the user, the operation method of the business software needs to be provided by the user. In this scenario, the computing device 102 needs to obtain the operation method of the business software from the user in advance and run the business software based on the operation method of the business software. There are many ways for the computing device 102 to obtain the operation method of the business software.

[0125] In some possible implementations, the computing device 102 may provide an interface, and the user may operate the terminal 101 to access the interface and input an operating method for the business software deployed on the computing device 102 . Subsequently, the terminal 101 sends the operating method for the business software input by the user to the computing device 102 .

[0126] In some other possible implementations, the computing device 102 may provide a human-computer interaction interface, and the user may operate the terminal 101 to display the human-computer interaction interface and input an operating method for the business software deployed on the computing device 102 in the human-computer interaction interface. Subsequently, the terminal 101 sends the operating method for the deployed business software input by the user to the computing device 102.

[0127] In some other possible implementations, the management device 103 may provide a human-computer interaction interface or interface, and the user may operate the terminal 101 to access the interface or display the human-computer interaction interface, and input the operating method of the business software deployed on the computing device 102; illustratively, assuming that the management device 103 manages multiple computing devices 102, the user may input the model of the computing device 102 and the operating method of the business software corresponding to the model, and subsequently, the management device 103 may send the operating method of the business software to each computing device 102 that deploys the business software under the model.

[0128] Step 305 : The computing device 102 determines a performance analysis result of the computing device 102 based on the characteristic data.

[0129] In the specific implementation, Fig.10a As shown, the computing device 102 can determine a feature analysis request (for requesting analysis of the feature data) based on the feature data, and send the feature analysis request to the feature analysis tool. The feature analysis tool determines the performance analysis result of the computing device 102 and feeds it back to the computing device 102.

[0130] Among them, the performance analysis results are used to describe the results of the performance analysis of the computing device 102, and may include charts and performance analysis conclusions. The performance analysis conclusions may include that the business software is read-write intensive software, the CPU usage of the business software (representing the CPU resources occupied by the business software), and the memory usage of the business software (the ratio of used memory to total memory).

[0131] Furthermore, the computing device 102 may also determine performance adjustment suggestions for the computing device 102 based on the characteristic data.

[0132] The performance adjustment suggestions may include: the specifications of the disk used, the specifications of the CPU used, the size of the memory used, modifying the compilation parameters, modifying the multi-process communication mode, and modifying the number of startup tasks.

[0133] In some possible implementations, such as Fig.10bAs shown, the computing device 102 can determine to generate a performance adjustment suggestion request (for requesting to determine a performance adjustment suggestion for the computing device 102 based on the characteristic data) based on the characteristic data, and send the performance adjustment suggestion request to the characteristic analysis tool. The characteristic analysis tool determines the performance analysis results and performance adjustment suggestions for the computing device 102, and feeds back to the computing device 102.

[0134] In other possible implementations, such as Fig.10c As shown, the computing device 102 can determine a feature analysis request (for requesting analysis of the feature data) based on the feature data, and send the feature analysis request to the feature analysis tool. The feature analysis tool determines the performance analysis results and performance adjustment suggestions of the computing device 102, and feeds back to the computing device 102.

[0135] In addition, the performance analysis results of the computing device 102 may also be carried in a document, which may include the performance analysis results of the computing device 102 (which may include charts, performance analysis conclusions), and further, may also include characteristic data, a computing device performance analysis method (describing steps 301 to 305) and / or performance adjustment suggestions for the computing device 102.

[0136] In the specific implementation, Fig.10c As shown, the computing device 102 can determine a feature analysis request (for requesting analysis of the feature data) based on the feature data, and send the feature analysis request to the feature analysis tool. The feature analysis tool determines the performance analysis result and performance adjustment suggestion of the computing device 102, and feeds back to the computing device 102; then, as shown in FIG. Fig.10d As shown, the computing device 102 can determine a document generation request based on the characteristic data, the performance analysis results of the computing device 102, the performance adjustment suggestions of the computing device 102, and the computing device performance analysis method (describing steps 301 to 305), and send the document generation request to a document generation tool. The document generation tool generates a document based on the characteristic data, the performance analysis results of the computing device 102, the performance adjustment suggestions of the computing device 102, and the computing device performance analysis method (describing steps 301 to 305), and feeds the generated document back to the computing device 102, and the computing device 102 displays the document.

[0137] In this solution, feature collection tools can be flexibly selected according to the performance characteristics selected by the user to meet diverse user needs.

[0138] It should be noted that, in the embodiment of the present application, the first AI tool may be located on the computing device 102, such as Fig.11As shown, it can also be located in the management device 103; in the scenario where the first AI tool is any one of the n AI tools, the n AI tools can be located in the computing device 102 and can also be located in the management device 103. It is worth noting that when the AI ​​tool is located in the management device 103, the resource consumption of the computing device 102 can be reduced, reducing the impact on the business.

[0139] It is worth noting that the description of the first AI tool in the above embodiment is only an example and does not constitute a specific limitation. In some possible implementations, when the computing device 102 searches for feature collection tools in the order of n AI tools, the first AI tool may be the AI ​​tool that firstly generates an accurate search result among the n AI tools; for example, Figure 5 In the scenario shown, the computing device 102 searches for feature collection tools in the order of AI tool 1, AI tool 2, ..., AI tool n. Assuming that the search results of AI tool 1, ..., AI tool (i-1) in response to the search request are erroneous, and the search results of AI tool i in response to the search request are accurate, then AI tool i is the first AI tool, and the computing device 102 runs the feature collection tool searched by the first AI tool based on the search results of the first AI tool in response to the search request. In other possible implementations, when the computing device 102 searches for feature collection tools in parallel with n tools, the first AI tool may be n AI tools. For example, in Fig. 9 In the scenario shown, the computing device 102 runs the searched feature collection tool based on the n search results of the n AI tools responding to the search request.

[0140] Based on the computing device performance analysis method provided above, a specific application of the computing device performance analysis method is described.

[0141] like Fig.12 As shown, in the embodiment of the present application, the feature data collection system may include a feature collection module, business software, an AI search module, an AI parsing module and a feature display module. The AI ​​search module may include multiple AI tools.

[0142] In actual applications, the user can install a feature collection module and business software in the computing device 102. If the feature analysis to be done involves multiple nodes, the feature collection module can obtain information of other nodes, for example, through / etc / hosts. The AI ​​search module is used to search for information, and the AI ​​parsing module is used to parse the search results returned by the AI ​​search module.

[0143] For the feature collection module: this module is used to read the performance features to be analyzed of the computing device 102 input by the user, such as IO, memory, etc., and also read the operation method of the business software input by the user. Then, according to the obtained performance features, read the relevant information of the computing device 102 (used to describe the operation environment of the computing device 102), and then send a search request to the AI ​​search module. The AI ​​search module searches for the download link, compilation method, installation method, operation method and other description information of the feature collection tool software of the performance features to be analyzed in the operation environment of the computing device 102, obtains the search results, and parses the search results through the AI ​​parsing module; according to the parsed search results returned by the AI ​​parsing module, run the searched feature collection tool on the computing device 102 to obtain the feature data of the performance features of the computing device 102 during the operation of the business software. Send the feature data to the AI ​​search module, the AI ​​search module determines the performance analysis results of the computing device 102 and the performance adjustment suggestions for the computing device 102, obtains the search results, and parses the search results through the AI ​​parsing module. According to the parsed search results returned by the AI ​​parsing module, the performance analysis results of the computing device 102 and the performance adjustment suggestions for the computing device 102 are obtained.

[0144] For the feature display module: the steps executed by the feature acquisition module, the feature data, the performance analysis results of the computing device 102 and the performance adjustment suggestions for the computing device 102 are sent to the AI ​​search module, the AI ​​search module determines the document, obtains the search results, and parses the search results through the AI ​​parsing module, and generates a document based on the parsed search results returned by the AI ​​parsing module and presents it to the user.

[0145] It should be noted that in the embodiment of the present application, the AI ​​search module and the AI ​​analysis module may be located in the computing device 102 or in the management device 103 (see Fig.13 shown).

[0146] like Fig.14 As shown, it is another specific embodiment of the computing device performance analysis method provided by the embodiment of the present application. Based on the previous embodiment, this embodiment combines the feature data collection system to provide a more specific description and a certain degree of optimization of the computing device performance optimization process. In this embodiment, taking the computing device 102 as a server as an example, the server runs the main program, runs the AI ​​search module, runs the AI ​​parsing module, and installs the business software as an example for description, where the main program can be the program of the feature collection module. Fig.14 As shown, the method in this embodiment includes:

[0147] 1. The main program reads the performance characteristics to be analyzed input by the user.

[0148] 2. The main program determines the performance characteristics and server information to be analyzed.

[0149] Exemplarily, the server information is used to describe the operating environment of the server, for example, it may be the version of the operating system and the processor architecture.

[0150] 3. The main program sends a first request to the AI ​​search request, and the first request is used to request the download link, compilation method and installation method of the feature collection tool.

[0151] For example, assuming that the processor architecture of the server is x86 and the operating system is Linux, the search request is used to describe the feature collection tool corresponding to the performance feature determined by the user under the x86 architecture and Linux, and return the relevant information of the feature collection tool.

[0152] 4. The AI ​​search module responds to the first request to search for the download link, compilation method and installation method of the feature collection tool to obtain the first response result and return it.

[0153] It should be noted that the first response result can also be called the search result.

[0154] 5. The AI ​​parsing module parses the first response result returned by the AI ​​search module.

[0155] In the specific implementation, the AI ​​parsing module parses the first response result returned by the AI ​​search module through a regular expression to extract the download link, compilation method and installation method of the feature collection tool.

[0156] 6. The main program installs the feature collection tool on the server based on the first response result after parsing.

[0157] The main program can download the feature collection tool based on the download link, compile the feature collection tool into an executable file based on the compilation method, and install the executable file based on the installation method.

[0158] In actual applications, the AI ​​search module can include multiple AI tools. The search request is an http request. The main program can implement the following two steps:

[0159] Step 1. Call the calling sequence files of multiple AI tools stored locally, such as chatgpt in the first sequence and Baidu Wenxin in the second sequence, send an http request to the AI ​​tool, parse the format of the response result returned by the AI ​​tool, such as regular expression parsing, and obtain the download link, compilation method, and installation method of the feature collection tool.

[0160] Step 2. After the main program downloads the feature collection tool, it installs the feature collection tool on the server; if the main program fails to install the feature collection tool, it will return to step 1 and send an http request to the next AI tool in the sequence.

[0161] Among them, the main program will have a default calling order for AI tools. In the process of updating the calling order of AI tools, they will be sorted based on the number of successes and time consumption. The higher the ranking, the higher the priority of the call. When there are multiple types of AI tools with the same number of successes and time consumption, these types of AI tools will be randomly sorted. It should be noted that the higher the number of successes and the shorter the time consumption, the higher the ranking of AI tools.

[0162] 7. The main program sends a second request to the AI ​​search module, and the second request is used to request the operation method of the feature collection tool.

[0163] 8. The AI ​​search module responds to the second search request, searches for the running method of the feature collection tool, obtains the second response result and returns it.

[0164] 9. The AI ​​parsing module parses the second response result returned by the AI ​​search module.

[0165] In a specific implementation, the AI ​​parsing module parses the second response result returned by the AI ​​search module through a regular expression to extract the operation method of the feature collection tool.

[0166] 10. The main program runs the business software on the server, and runs the feature collection tool on the server based on the parsed second response result.

[0167] In the specific implementation, after the main program installs the feature collection tool, it sends an http request to the AI ​​tool. The response result returned by the AI ​​tool is parsed in the format, such as regular expression parsing, to obtain the running method of the feature collection tool; the main program uses shell execution instructions on the server to run the business software and feature collection tool.

[0168] 11. The main program obtains feature data through feature acquisition tools.

[0169] 12. The main program sends a third request to the AI ​​search request, and the third request is used to request to determine the performance analysis results and performance adjustment suggestions of the server based on the feature data.

[0170] 13. The AI ​​search module responds to the third request and determines the performance analysis results and performance adjustment suggestions of the server based on the feature data, obtains the third response result and returns it.

[0171] 14. The AI ​​parsing module parses the third response result returned by the AI ​​search module.

[0172] In the specific implementation, the AI ​​parsing module parses the third response result returned by the AI ​​search module through a regular expression to extract the performance analysis results and performance adjustment suggestions of the server.

[0173] 15. The main program sends a fourth request to the AI ​​search request, and the fourth request is used to request to generate a document based on the performance analysis steps, feature data, performance analysis results of the server and performance adjustment suggestions.

[0174] The performance analysis steps may be steps 1 to 14 above.

[0175] 16. The AI ​​search module responds to the fourth request and generates document content and format according to the performance analysis steps, feature data, performance analysis results of the server and performance adjustment suggestions, obtains the fourth response result and returns it.

[0176] 17. The AI ​​parsing module parses the fourth response result returned by the AI ​​searching module.

[0177] In a specific implementation, the AI ​​parsing module parses the fourth response result returned by the AI ​​search module through a regular expression to extract the document content and format of the server.

[0178] The content of the document is: performance analysis steps, characteristic data, server performance analysis results, performance adjustment suggestions, title, content layout (performance analysis steps, characteristic data, server performance analysis results, performance adjustment suggestions), font, font size, color, alignment, etc. The format of the document is used to describe the format of the document. It refers to the characteristics of the information used to store the information.

[0179] Special encoding method.

[0180] 18. The main program generates a document based on the parsed fourth response result.

[0181] Based on the same concept as the embodiment of the method of the present application, the embodiment of the present application also provides a computing device performance analysis device. The computing device performance analysis device includes several modules, each module is used to execute each step in the computing device performance analysis method provided in the embodiment of the present application, and the division of the modules is not limited here. The technicians in the relevant field can clearly understand that in practical applications, the various steps in the computing device performance analysis method provided in the embodiment of the present application can be assigned to different modules as needed, that is, the internal structure of the device is divided into different modules to complete all or part of the functions described above. Each module in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more modules can be integrated in one unit, and the above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each module are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present application. The specific working process of the module in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0182] For example, the computing device performance analysis apparatus is used to execute the computing device performance analysis method provided in the embodiment of the present application. Fig.15 Schematic diagram of the structure of the computing device performance analysis device provided in the embodiment of the present application. Fig.15 As shown, the computing device performance analysis device provided in the embodiment of the present application includes:

[0183] An acquisition module 1501 is used to acquire the performance characteristics to be analyzed of the computing device and the operating environment of the computing device;

[0184] A request module 1502 is used to request the first AI tool to search for a feature collection tool based on the performance feature to be analyzed and the operating environment; wherein the first AI tool is used to search for a feature collection tool suitable for the operating environment;

[0185] A collection module 1503 is used to run the searched feature collection tool based on the search results returned by the first AI tool, and use the feature collection tool to collect feature data of the performance feature to be analyzed;

[0186] The analysis module 1504 is used to determine the performance analysis result of the computing device based on the characteristic data.

[0187] Based on the same concept as the embodiment of the method of the present application, the embodiment of the present application also provides a computing device. In a specific application, the computing device may include a memory, and the memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a hard disk 123. The volatile memory may be a memory 112. Specifically, the memory of the computing device, such as a hard disk and a memory 112, may store a computer program. When the computing device is running, the processor 111 may read the computer program stored in the memory, such as reading the computer program stored in the hard disk to the memory 112, and reading the computer program from the memory 112, to implement the steps in the above-mentioned computer performance testing method, such as Figure 3 Steps 301 to 305 in .

[0188] Exemplarily, the computer program may be divided into one or more modules / units, which may be a series of computer program instruction segments capable of completing specific functions, and the one or more modules / units are stored in a hard disk and executed by CPU 111 to complete the present application. For example, the computer program may be divided into an acquisition module 1501, a request module 1502, a collection module 1503, and an analysis module 1504, and the specific functions of each module are described above.

[0189] In addition to the above methods, apparatuses and computing devices, embodiments of the present application may also provide a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to perform the steps in the computing device performance analysis method of various embodiments of the present application described in the "Method" section of this specification. Wherein, the computer program product can be written in any combination of one or more programming languages ​​to perform computer program code for performing the operation of the embodiments of the present application, and the programming language includes an object-oriented programming language, such as Java, C++, etc., and also includes a conventional procedural programming language, such as "C" language or similar programming language. Wherein, the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer program code can be executed completely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device, partially on the remote computing device, or completely on the remote computing device or server.

[0190] In addition, the embodiment of the present application may also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions, when executed by the processor, cause the processor to execute the steps in the computing device performance analysis method according to various embodiments of the present disclosure described in the above "Method" section of this specification. The computer-readable storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0191] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0192] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0193] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0194] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0195] It should also be noted that in the apparatus, device and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0196] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

[0197] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

Claims

1. A computing device performance analysis method, characterized in that: The method comprises: Obtaining performance characteristics of a computing device to be analyzed and an operating environment of the computing device; Based on the performance characteristics to be analyzed and the operating environment, searching for a feature collection tool through a first AI tool; wherein the AI ​​tool is used to search for a feature collection tool suitable for the operating environment; Based on the search results returned by the first AI tool, running the searched feature collection tool, and using the feature collection tool to collect feature data of the performance feature to be analyzed; Based on the characteristic data, a performance analysis result of the computing device is determined.

2. The method according to claim 1, characterized in that The obtaining of the performance characteristics to be analyzed of the computing device includes: Based on the human-computer interaction interface, the performance characteristics to be analyzed of the computing device set by the user are obtained.

3. The method according to claim 1 or 2, characterized in that: The searching for a feature collection tool by a first AI tool based on the performance feature to be analyzed and the operating environment includes: Sending a search request to the first AI tool; the search request includes the performance feature and the operating environment, and the search request is used to request a search for a feature collection tool for the performance feature in the operating environment; The step of running the searched feature acquisition tool based on the search results returned by the first AI tool includes: In the case where the search result of the first AI tool in response to the search request is accurate, a feature collection tool indicated by the search result is run on the computing device.

4. The method according to claim 3, characterized in that The method further comprises: In the event that an error occurs in the search result, the search request is sent to a second AI tool.

5. The method according to claim 4, characterized in that The first AI tool has a higher number of successes than the second AI tool, and / or the first AI tool has a lower search duration than the second AI tool, the number of successes is used to indicate the accurate number of search results for different search requests, the search time is used to indicate the time consumed in responding to the different search requests, and the performance characteristics and / or operating environments in the different search requests are different.

6. The method according to any one of claims 3 to 5, characterized in that: The search results include a download link of the feature collection tool, a compilation method of the feature collection tool, an installation method of the feature collection tool, and an operation method of the feature collection tool; The step of running the searched feature acquisition tool based on the search results returned by the first AI tool includes: Download the feature collection tool based on the download link; Compiling the feature acquisition tool into an executable file based on the compilation method; Installing the executable file in the operating environment based on the installation method; Based on the running method, the executable file is run in the running environment.

7. The method according to any one of claims 1 to 6, characterized in that: Before using the feature collection tool to collect feature data of the performance feature to be analyzed, the method further includes: Obtaining the operation method of the business software; Based on the operation method, the business software is operated.

8. The method according to any one of claims 1 to 7, characterized in that: The operating environment of the computing device includes the architecture of the central processing unit and the version of the operating system.

9. The method according to any one of claims 1 to 8, characterized in that: The computing device performance analysis method is executed by the computing device; The first AI tool search feature acquisition tool is executed by other devices other than the computing device.

10. A computing device, characterized in that: comprising a processor and a memory; wherein, The memory is used to store programs; The processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the method described in any one of claims 1 to 9 is performed.

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