A testing method, device, storage medium, and equipment for a mimetic computing system

Abstract

This specification discloses a testing method, device, storage medium, and equipment for a mimic computing system. The method includes determining a mimic computing system to be tested, using the system to be tested to execute various test tasks, determining the network variability of the system to be tested based on the network structures formed by the computing nodes in the system when running various test tasks, determining the functional completeness of the system to be tested based on the test tasks successfully run by the system to be tested, and determining the computing performance of the system to be tested based on the running performance when running various test tasks. This method is applicable to various mimic computing systems, provides an objective testing method and standard for the comprehensive performance of mimic computing systems, enables users to have a clearer and more intuitive understanding of the performance and capabilities of different mimic computing systems without actual use, and facilitates users to select mimic computing systems.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a testing method, apparatus, storage medium, and device for a mimicking computing system. Background Art

[0002] The mimetic computing system is an innovative computing architecture. Compared with traditional high-performance computing clusters, the mimetic computing system has a pre-installed software and hardware resource library and a preset task orchestration strategy. It can adapt to computing tasks in various different fields through a single computing system, and can achieve more efficient computing through the computing system's own software and hardware structure adjustment capabilities and software and hardware collaborative deployment capabilities.

[0003] Due to the characteristics of the mimetic computing system that can adjust its own structure, the existing high-performance computing cluster performance benchmark tests are difficult to truly reflect the full functional level of a mimetic computing system, and thus users find it difficult to determine which mimetic computing system product to choose to meet their own research or business needs.

[0004] Therefore, the present invention provides a testing method, apparatus, storage medium, and device for a mimetic computing system. Summary of the Invention

[0005] This specification provides a testing method and device for a mimicking computing system to partially solve the above-mentioned problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides a testing method for a mimic computing system, including:

[0008] Determine the mimetic computing system to be tested;

[0009] Utilizing the mimetic computing system to be tested to perform various test tasks;

[0010] Determining the network variability of the mimetic computing system to be tested based on the network structures formed by the computing nodes in the system when the mimetic computing system to be tested runs each test task, determining the functional completeness of the mimetic computing system to be tested based on the test tasks successfully run by the mimetic computing system to be tested, and determining the computing performance of the mimetic computing system to be tested based on the operating performance of the mimetic computing system to be tested when running each test task;

[0011] A test result of the mimetic computing system to be tested is determined according to the network variability, the functional completeness, and the computing performance.

[0012] Optionally, running each test task using the mimetic computing system to be tested specifically includes:

[0013] For any test task, the task description of the test task is input into the mimetic computing system to be tested, so that the mimetic computing system to be tested determines the target software resources, target hardware resources and target network structure required to run the test task in the software resource library and hardware resource library of the mimetic computing system to be tested, and deploys the target software resources on each computing node composed of the target hardware resources, and combines each computing node according to the target network structure to form the system to be tested;

[0014] The test data corresponding to the test task is input into the system to be tested to execute the test task.

[0015] Optionally, the test tasks specifically include: test tasks of different task types and test tasks of different task computation amounts;

[0016] Determining the network variability of the mimetic computing system to be tested based on the network structures formed by computing nodes in the system when the mimetic computing system to be tested runs each test task specifically includes:

[0017] Determine the scale of each system under test formed by computing nodes within the system when the mimetic computing system under test runs each test task with different task computational loads, and determine the topology of each network structure formed by computing nodes within the system when the mimetic computing system under test runs each test task with different task types;

[0018] The network variability of the mimetic computing system to be tested is determined according to the difference between the scales and the difference between the topological structures.

[0019] Optionally, the test tasks specifically include: at least two of an image recognition test task, an encryption and decryption test task, and a web server test task;

[0020] Determining the functional completeness of the mimetic computing system to be tested based on each test task successfully run by the mimetic computing system to be tested, specifically including:

[0021] The functional completeness of the mimetic computing system to be tested is determined according to the number of types of test tasks successfully executed by the mimetic computing system to be tested.

[0022] Optionally, determining the computing performance of the mimetic computing system to be tested based on the running performance of the mimetic computing system to be tested when running each test task specifically includes:

[0023] The computing performance of the simulated computing system to be tested is determined based on at least one of the storage performance when the simulated computing system to be tested runs each test task, the system energy efficiency when the simulated computing system to be tested runs each test task, the acceleration ratio of the simulated computing system to be tested when running each test task, and the network performance when the simulated computing system to be tested runs each test task.

[0024] Optionally, determining the network variability of the mimetic computing system to be tested based on each network structure formed by computing nodes in the system when the mimetic computing system to be tested runs each test task, and determining the computing performance of the mimetic computing system to be tested based on the running performance of the mimetic computing system to be tested when running each test task, specifically includes:

[0025] Determining the network variability of the mimetic computing system for each type of test task based on each network structure formed by computing nodes in the system when the mimetic computing system to be tested runs the same type of test task, and determining the operating performance of the mimetic computing system to be tested when running the same type of test task;

[0026] The method further comprises:

[0027] Determine test results of the mimetic computing system to be tested for various types of test tasks according to the network variability and the computing performance.

[0028] Optionally, determining a test result of the mimetic computing system to be tested based on the network variability, the functional completeness, and the computing performance specifically includes:

[0029] A test result of the mimetic computing system to be tested is determined according to the network variability, the functional completeness, and the computing performance, as well as preset test weights corresponding to the network variability, the functional completeness, and the computing performance, respectively.

[0030] This specification provides a testing device for a mimic computing system, including:

[0031] Determine the module and the mimetic computing system to be tested;

[0032] An execution module, utilizing the mimetic computing system to be tested to execute each test task;

[0033] a computing module that determines the network variability of the mimetic computing system under test based on each network structure formed by computing nodes within the system when the mimetic computing system under test runs each test task, determines the functional completeness of the mimetic computing system under test based on each test task successfully run by the mimetic computing system under test, and determines the computing performance of the mimetic computing system under test based on the operating performance of the mimetic computing system under test when running each test task;

[0034] An output module determines a test result of the mimetic computing system to be tested according to the network variability, the functional completeness, and the computing performance.

[0035] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned testing method of the mimetic computing system.

[0036] This specification provides a device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the test method of the mimetic computing system is implemented.

[0037] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0038] It can be seen from the above method that this method is applicable to various types of mimetic computing systems. It proposes an objective testing method and standard for the comprehensive performance of mimetic computing systems. It can enable users to have a clearer and more intuitive understanding of the performance and capabilities of different mimetic computing systems without actual use, making it easier for users to choose mimetic computing systems to meet their own research or business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The exemplary embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation of this specification. In the drawings:

[0040] Figure 1 A flowchart of a testing method for a mimetic computing system in this specification;

[0041] Figure 2 A schematic diagram of the process of executing a test task by the mimicking computing system in this specification;

[0042] Figure 3 This is a schematic diagram of the display method of the test results in this manual;

[0043] Figure 4 A schematic diagram of a test device for a mimic computing system provided in this specification;

[0044] Figure 5 The corresponding Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0045] To make the purpose, technical solutions, and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0046] The technical solutions provided by the embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0047] Figure 1 The flowchart of a method for testing a mimetic computing system in this specification is as follows:

[0048] S100: Determine a mimic computing system to be tested.

[0049] The core concept of a mimetic computing system is to achieve high-efficiency computing through dynamic structural variation and hardware-software collaboration. Dynamic structural variation refers to the system's ability to dynamically select or generate the optimal computing structure based on the application requirements of the computing task being performed, adapting to different tasks and environmental changes. This dynamic structural variation enables the system to adjust its hardware and software resources to achieve optimal computing performance according to different computing tasks. Hardware-software collaboration refers to the fact that in a mimetic computing system, software and hardware are no longer static, fixed combinations, but can adapt and change to form different computing structures. This synergy allows the system to achieve higher energy efficiency through different hardware and software variants while maintaining functional equivalence. These characteristics of mimetic computing systems give them broad application prospects in high-performance computing, cloud computing, big data processing, and other fields. Therefore, the present application provides a testing method for a mimetic computing system. The execution entity of this specification can be a test server used to test the mimetic computing system or other electronic devices capable of connecting to the mimetic computing system under test, but this specification does not limit this. For ease of explanation, the following description of a testing method for a mimetic computing system provided in this specification uses the test server as the execution entity.

[0050] First, a mimetic computing system to be tested is determined, and the test server is used to connect to a control server of the mimetic computing system to be tested.

[0051] S102: Utilize the mimetic computing system to be tested to execute various test tasks.

[0052] The test server contains a built-in test program and dataset corresponding to each test task. Each test task is described and encoded according to the task orchestration strategy of the mimetic computing system to determine task information. This task information may include the task type, data type, and load variations. Thus, pre-storing the test program and dataset corresponding to each test task in the test server facilitates the deployment and implementation of subsequent test tasks.

[0053] Furthermore, the simulated computing system to be tested is used to perform various test tasks. For any test task, the task information, test program and data set of the test task can be sent to the control server of the simulated computing system to be tested. The control server can complete the deployment of the test program on the simulated computing system to be tested based on the task information of the test task and its own task scheduling strategy, and use the data set to run the deployed test program to complete the test task.

[0054] S104: Determine the network variability of the mimetic computing system to be tested based on the network structures formed by the computing nodes in the system when the mimetic computing system to be tested runs each test task, determine the functional completeness of the mimetic computing system to be tested based on the test tasks successfully run by the mimetic computing system to be tested, and determine the computing performance of the mimetic computing system to be tested based on the operating performance of the mimetic computing system to be tested when running each test task.

[0055] After the control server of the simulated computing system to be tested obtains the task information of any test task, it can determine the execution of the test task based on the task information and its own task scheduling strategy, how to use the software and hardware resources in the software resource library and the hardware resource library to form each computing node, and determine the network structure with which the computing nodes in the system should be connected to perform communication and transmission between the computing nodes. If the network variability of the simulated computing system to be tested is strong, the simulated computing system to be tested can determine the appropriate network structure for each test task based on the difference in task information. In the method provided in this specification, the test tasks used to test the simulated computing system to be tested are all different test tasks. Therefore, the greater the difference in the network structure of the simulated computing system to be tested for each test task, the stronger the network variability of the simulated computing system to be tested can be determined.

[0056] On the other hand, due to its dynamic variable structure and the characteristics of software and hardware collaboration, the mimetic computing system can adapt to the task requirements of a variety of different scenarios. However, in order to execute a test task, the mimetic computing system to be tested still needs to be pre-installed with the software resources and hardware resources required to execute the test task, and its own task scheduling strategy needs to cover the test task, otherwise the deployment of the test program cannot be completed and the test task cannot be completed. Therefore, the more test tasks the mimetic computing system to be tested successfully runs, the more functional completeness the mimetic computing system to be tested will have.

[0057] On the other hand, due to its own characteristics, the mimetic computing system is suitable for high-performance computing. Therefore, the computing performance of the mimetic computing system under test can be determined based on the running performance of the mimetic computing system under test when performing various test tasks.

[0058] Specifically, the above-mentioned network variability, functional completeness and computing performance can be used as test items of the test results, and each test item can be represented by a numerical value. For example, for any test item, the numerical value of the test item can be determined based on the ratio of the actual measured value of the test item of the simulated computing system to be tested and the preset standard value corresponding to the test item.

[0059] For example, the representative value of the simulated storage bandwidth of the simulated computing system to be tested may be the ratio of the actually measured bandwidth of the simulated computing system to be tested to a preset standard bandwidth.

[0060] In one or more embodiments of the present specification, the computing performance of the simulated computing system to be tested can be determined based on at least one of the storage performance when the simulated computing system to be tested runs each test task, the system energy efficiency when the simulated computing system to be tested runs each test task, the acceleration ratio of the simulated computing system to be tested when running each test task, and the network performance when the simulated computing system to be tested runs each test task.

[0061] Among them, the storage performance of the simulated computing system to be tested may include at least one of the simulated storage bandwidth, the simulated storage response time, and the simulated storage capacity. Similar to the above, the simulated storage bandwidth, the simulated storage response time, the simulated storage capacity, etc. are the test sub-items contained in a test item. For a test sub-item, the test sub-item can be represented by a numerical value, and the numerical value of the test item can be determined based on the ratio of the actual measured value of the simulated computing system to be tested for the test item and the preset standard value corresponding to the test item; the system energy efficiency of the simulated computing system to be tested can be determined by the weighted sum of the energy efficiency indicators for each test task, wherein the energy efficiency indicator can be the energy efficiency of the simulated computing system to be tested. The ratio of the energy consumption of a test task to the standard energy consumption of the test task; the acceleration ratio of the simulated computing system to be tested can be determined based on the ratio of the standard time of the simulated computing system to be tested for each test task to the actual time; the network performance can be determined based on at least one of the ratio of the bandwidth of the simulated computing system to be tested for each test task to the standard bandwidth of the test task, the ratio of the delay of the simulated computing system to be tested for each test task to the standard delay of the test task, the ratio of the delay fluctuation of the simulated computing system to be tested for each test task to the standard delay fluctuation of the test task, and the ratio of the packet loss of the simulated computing system to be tested for each test task to the standard packet loss of the test task. It should be noted that for the same test task, the standard time of the test task is the same and the standard energy consumption of the test task is the same for different simulated computing systems to be tested. When the storage performance of the simulated computing system to be tested includes simulated storage bandwidth, simulated storage response time and simulated storage capacity, the storage performance of the simulated computing system to be tested can be determined by weighted summing the simulated storage bandwidth, simulated storage response time and simulated storage capacity according to preset weights.

[0062] It should also be noted that the standard values ​​corresponding to the above-mentioned test items or sub-test items can be set by the user according to needs, for example, they can be determined based on the actual measured values ​​of various common high-performance computing clusters.

[0063] S106: Determine a test result of the mimetic computing system to be tested according to the network variability, the functional completeness, and the computing performance.

[0064] After determining the network variability, functional completeness and computing performance of the simulated computing system to be tested, the test results of the simulated computing system to be tested can be determined based on the preset weights and the performance of the simulated computing system to be tested in the above three aspects of network variability, functional completeness and computing performance.

[0065] Specifically, the ranking of the level of each test item of the mimetic computing system to be tested among the test items of the known mimetic computing systems can be determined based on the numerical value of each test item, or the numerical value of each test item of the mimetic computing system to be tested can be directly used as the test result of the mimetic computing system to be tested; or the ranking of the comprehensive level of the mimetic computing system to be tested among the known mimetic computing systems can be determined based on the numerical value of each test item, or the weighted average of the test items of the mimetic computing system to be tested can be directly used as the test result of the mimetic computing system to be tested.

[0066] In one or more embodiments of the present specification, the test result of the simulated computing system to be tested is determined based on the network variability, the functional completeness and the computing performance, and the preset test weights corresponding to the network variability, the functional completeness and the computing performance, respectively.

[0067] The test weight can be set according to the actual needs of the user, thereby obtaining a quantitative test result of the mimetic computing system to be tested, objectively indicating the comprehensive capabilities of the mimetic computing system to be tested.

[0068] like Figure 1 The testing method for a mimetic computing system shown can be applied to various types of mimetic computing systems. It proposes objective testing methods and standards for the comprehensive performance of mimetic computing systems, which can enable users to have a clearer and more intuitive understanding of the performance and capabilities of different mimetic computing systems without actual use, making it easier for users to select mimetic computing systems to meet their own research or business needs.

[0069] In addition, in Figure 1 In step S102 shown, for any test task, the task description of the test task is input into the simulated computing system to be tested, so that the simulated computing system to be tested can determine the target software resources, target hardware resources and target network structure required to run the test task in the software resource library and hardware resource library of the simulated computing system to be tested, and deploy the target software resources to the target hardware resources to constitute each computing node, combine the computing nodes according to the target network structure to form the system to be tested, and input the test data corresponding to the test task into the system to be tested to execute the test task.

[0070] The process is as follows Figure 2 As shown, step S200 is first executed: the test system inputs the test task into the mimetic computing system to be tested;

[0071] After the mimetic computing system receives the test task, step S202 is executed: the mimetic computing system to be tested determines the target software resources, target hardware resources, and target network structure required to run the test task in the software resource library and hardware resource library of the mimetic computing system to be tested;

[0072] After all the determinations are completed, step S204 is executed: the mimicking computing system to be tested deploys the target software resources on the target hardware resources to constitute the computing nodes, and combines the computing nodes according to the target network structure to form the system to be tested;

[0073] Then, step S206 is executed: the test system inputs the test data corresponding to the test task into the system to be tested;

[0074] Finally, step S208 is executed: the mimic computing system to be tested executes the test task.

[0075] In one or more embodiments of the present specification, step S206 may be advanced to be performed simultaneously with step S200 , while step S208 needs to be performed after step S204 .

[0076] Specifically, the software resource library may include a computing core library, an application image library, a network structure library, etc.; the hardware resource library may include a computing power resource library composed of devices that provide computing power, a storage resource library composed of devices that provide storage capacity, and a communication resource library composed of devices that provide communication routing functions. The computing core library may include packaged, reusable computing functions for applications in different scenarios, the application image library may include the deployment environment information required for applications in different scenarios, and the network structure library may include computing node communication structures for different scenarios.

[0077] In addition, the test tasks specifically include: test tasks of different task types, test tasks of different task computation amounts; Figure 1 In step S104 shown, the scale of each system to be tested composed of computing nodes in the system is determined when the simulated computing system to be tested runs each test task with different task computational loads, and the topology of each network structure composed of computing nodes in the system is determined when the simulated computing system to be tested runs each test task with different task types. Based on the differences between the scales and the differences between the topologies, the network variability of the simulated computing system to be tested is determined.

[0078] It is easy to know that different test tasks with different task computation amounts and task types have different requirements for the computing amount and communication capabilities of the computing system. For example, model training tasks have higher requirements for computing power and often require one-to-one connections between computing nodes, while encryption and decryption tasks also have higher requirements for computing power, but may require one-to-many connections between computing nodes. Therefore, for different types of test tasks, the mimetic computing system should reasonably adjust its own network structure according to the test tasks to improve computing efficiency, so as to give full play to its advantages over traditional high-performance computing clusters.

[0079] Among them, the scale of the network structure can be determined according to at least one of the total computing power or the number of nodes of the system to be tested for a test task; the node scale adjustment capability of the system to be tested of the simulated computing system to be tested for a test task can be determined according to the difference between the scales of different systems to be tested composed of the simulated computing system to be tested for test tasks with different computing amounts. The more the difference between the scales adapts to the difference in computing amounts between the test tasks, the stronger the node scale adjustment capability of the simulated computing system to be tested; the kernel methods can be used to determine the differences between topological structures.

[0080] In one or more embodiments of the present specification, the scale of each system to be tested composed of computing nodes within the system is determined when the simulated computing system to be tested runs the same test task and receives test data of different data amounts, and the topology of each network structure composed of computing nodes within the system is determined when the simulated computing system to be tested runs test tasks of different task types, and the network variability of the simulated computing system to be tested is determined based on the differences between the scales and the differences between the topologies.

[0081] The process of the tested mimetic computing system running a test task can be a process of continuously receiving data sent by a test server and processing the data. The frequency of data sending by the test server can be dynamically changed. Accordingly, in order to improve computing efficiency, the scale of the tested system composed of the mimetic computing system should also be changed accordingly. Therefore, the node scale adjustment capability of the tested system for a test task of the mimetic computing system to be tested can be determined according to the difference between the scales of different tested systems composed of the mimetic computing system to be tested at different data sending frequencies for the same test task. The more the difference between the scales adapts to the difference in different data sending frequencies, the stronger the network variability and node scale adjustment capability of the tested mimetic computing system to be tested.

[0082] On the other hand, the test tasks specifically include: at least two of the image recognition test tasks, encryption and decryption test tasks and web server test tasks; Figure 1 In step S104 shown, the functional completeness of the mimetic computing system to be tested is determined according to the number of types of test tasks successfully executed by the mimetic computing system to be tested.

[0083] Depending on the differences in the application scenarios faced, the types of test tasks may include but are not limited to image recognition test tasks, encryption and decryption test tasks, and web server test tasks. The software resource library and hardware resource library of the mimetic computing system to be tested need to prepare corresponding software and hardware resources to enable the mimetic computing system to be tested to adapt to different application scenarios.

[0084] In one or more embodiments of the present specification, the acceleration ratio of each test task run by the simulated computing system to be tested may include the acceleration ratio of each type of test task of the simulated computing system to be tested, and the above-mentioned type may be one of an image recognition test task, an encryption and decryption test task, and a web server test task. The acceleration ratio of a type of test task can be determined based on the average of the acceleration ratios of each test task of the same type run by the simulated computing system to be tested.

[0085] In addition, in Figure 1 In step S104 shown, the network variability of the mimetic computing system to be tested for each type of test task is determined based on the network structures of the computing nodes in the system when the mimetic computing system to be tested runs the same type of test task, and the network variability of the mimetic computing system to be tested for each type of test task is determined based on the running performance of the mimetic computing system to be tested when running the same type of test task. Figure 1 After step S104, the test results of the mimetic computing system to be tested for each type of test task are determined according to the network variability and the computing performance.

[0086] The method provided in this specification can also determine the single-dimensional test results of the simulated computing system to be tested for each application scenario, providing more fine-grained reference materials for user selection. Among them, one application scenario can correspond to one type of test task.

[0087] For each application scenario, Figure 3 The test result graph shown in the figure represents the test results of the mimetic computing system under test. The network variability reference value and computing performance reference value can be determined based on the statistical values ​​of the tested mimetic computing systems in the application scenario. Specifically, statistical values ​​such as the mean or median can be selected based on user needs. The solid circles in the figure represent the statistical values ​​of each tested mimetic computing system, and the hollow circles represent the statistical values ​​of the mimetic computing system under test, which can more intuitively represent the test results of the mimetic computing system under test.

[0088] The above is the XX method provided in one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding test device for the mimic computing system, such as Figure 4 shown.

[0089] Figure 4 A schematic diagram of a test device for a mimetic computing system provided in this specification specifically includes:

[0090] Determination module 400, determining the mimic computing system to be tested;

[0091] An execution module 402 executes each test task using the mimetic computing system to be tested;

[0092] The computing module 404 determines the network variability of the mimetic computing system under test based on the network structures formed by the computing nodes in the system when the mimetic computing system under test runs each test task, determines the functional completeness of the mimetic computing system under test based on the test tasks successfully run by the mimetic computing system under test, and determines the computing performance of the mimetic computing system under test based on the operating performance of the mimetic computing system under test when running each test task;

[0093] The output module 406 determines a test result of the mimetic computing system to be tested according to the network variability, the functional completeness, and the computing performance.

[0094] Optionally, the execution module 402 is specifically used to: for any test task, input the task description of the test task into the simulated computing system to be tested, so that the simulated computing system to be tested can determine the target software resources, target hardware resources and target network structure required to run the test task in the software resource library and hardware resource library of the simulated computing system to be tested, and deploy the target software resources to each computing node composed of the target hardware resources, combine the computing nodes according to the target network structure to form the system to be tested, and input the test data corresponding to the test task into the system to be tested to execute the test task.

[0095] Optionally, the test tasks specifically include: test tasks of different task types and test tasks of different task computation amounts;

[0096] The computing module 404 is specifically used to: determine the scale of each system to be tested composed of computing nodes in the system when the simulated computing system to be tested runs each test task with different task computational loads, and determine the topology of each network structure composed of computing nodes in the system when the simulated computing system to be tested runs each test task with different task types, and determine the network variability of the simulated computing system to be tested based on the differences between the scales and the differences between the topologies.

[0097] Optionally, the test tasks specifically include: at least two of an image recognition test task, an encryption and decryption test task, and a web server test task;

[0098] The calculation module 404 is specifically configured to determine the functional completeness of the mimic computing system to be tested according to the number of types of test tasks successfully executed by the mimic computing system to be tested.

[0099] Optionally, the computing module 404 is specifically used to determine the computing performance of the simulated computing system to be tested based on at least one of the storage performance when the simulated computing system to be tested runs each test task, the system energy efficiency when the simulated computing system to be tested runs each test task, the acceleration ratio of the simulated computing system to be tested when running each test task, and the network performance when the simulated computing system to be tested runs each test task.

[0100] Optionally, the computing module 404 is specifically configured to: determine the network variability of the mimetic computing system to be tested for each type of test task based on each network structure formed by computing nodes in the system when the mimetic computing system to be tested runs the same type of test task, and determine the network variability of the mimetic computing system to be tested for each type of test task based on the running performance of the mimetic computing system to be tested when running the same type of test task;

[0101] The output module 406 is further configured to determine the test results of the mimetic computing system to be tested for various types of test tasks according to the network variability and the computing performance.

[0102] Optionally, the output module 406 is specifically used to determine the test result of the simulated computing system to be tested based on the network variability, the functional completeness and the computing performance, and the preset test weights corresponding to the network variability, the functional completeness and the computing performance.

[0103] This specification also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 A testing method for a mimetic computing system is provided.

[0104] This manual also provides Figure 5 The schematic structure diagram of the electronic device shown in FIG. Figure 5 As mentioned above, at the hardware level, the test equipment of the emulated computing system includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0105] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using physical hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly performed using software called a "logic compiler." This is similar to the software compilers used during program development. Before compilation, the original code must be written in a specific programming language, called a Hardware Description Language (HDL). There are many types of HDL, including ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that simply by programming a method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0106] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the memory control logic. Those skilled in the art will also appreciate that, in addition to implementing the controller purely in computer-readable program code, the controller can also be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, an embedded microcontroller, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the means for implementing the various functions included therein can also be considered as structures within the hardware component. Alternatively, the means for implementing the various functions can be considered both a software module implementing the method and a structure within the hardware component.

[0107] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0108] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0113] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0114] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0115] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0116] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0117] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0119] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0120] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of this application.

Claims

1. A method for testing a mimic computing system, characterized in that: The method comprises: Determine the mimic computing system to be tested; Utilizing the mimetic computing system to be tested to perform various test tasks; Determine the network variability of the mimetic computing system to be tested according to each network structure formed by computing nodes in the system when the mimetic computing system to be tested runs each test task, the stronger the network variability is, the greater the difference in the network structure of the mimetic computing system to be tested when running each test task for the mimetic computing system to be tested, and determine the functional completeness of the mimetic computing system to be tested according to each test task successfully run by the mimetic computing system to be tested, and determine the computing performance of the mimetic computing system to be tested according to the running performance of the mimetic computing system to be tested when running each test task; A test result of the mimetic computing system to be tested is determined according to the network variability, the functional completeness and the computing performance.

2. The method according to claim 1, characterized in that The test tasks are run by using the mimic computing system to be tested, specifically including: For any test task, the task description of the test task is input into the simulated computing system to be tested, so that the simulated computing system to be tested can determine the target software resources, target hardware resources and target network structure required to run the test task in the software resource library and hardware resource library of the simulated computing system to be tested, and deploy the target software resources on the target hardware resources to form each computing node, and combine each computing node according to the target network structure to form the system to be tested; The test data corresponding to the test task is input into the system to be tested to execute the test task.

3. The method according to claim 1, characterized in that The test tasks specifically include: test tasks of different task types and test tasks of different task computation amounts; Determining the network variability of the mimetic computing system to be tested according to each network structure formed by computing nodes in the system when the mimetic computing system to be tested runs each test task, specifically includes: Determine the scale of each system to be tested formed by computing nodes in the system when the simulated computing system to be tested runs each test task with different task computational loads, and determine the topology of each network structure formed by computing nodes in the system when the simulated computing system to be tested runs each test task with different task types; The network variability of the mimetic computing system to be tested is determined according to the difference between the scales and the difference between the topological structures.

4. The method according to claim 1, characterized in that The test tasks specifically include: at least two of an image recognition test task, an encryption and decryption test task, and a web server test task; Determining the functional completeness of the mimetic computing system to be tested according to each test task successfully run by the mimetic computing system to be tested, specifically includes: The functional completeness of the mimetic computing system to be tested is determined according to the number of types of test tasks successfully run by the mimetic computing system to be tested.

5. The method according to claim 1, characterized in that Determining the computing performance of the mimetic computing system to be tested according to the running performance of the mimetic computing system to be tested when running each test task specifically includes: The computing performance of the simulated computing system to be tested is determined based on at least one of the storage performance when the simulated computing system to be tested runs each test task, the system energy efficiency when the simulated computing system to be tested runs each test task, the acceleration ratio of the simulated computing system to be tested when running each test task, and the network performance when the simulated computing system to be tested runs each test task.

6. The method according to claim 3, characterized in that Determining the network variability of the mimetic computing system to be tested according to each network structure formed by computing nodes in the system when the mimetic computing system to be tested runs each test task, and determining the computing performance of the mimetic computing system to be tested according to the running performance of the mimetic computing system to be tested when running each test task, specifically including: Determine the network variability of the mimetic computing system to be tested for each type of test task according to each network structure formed by computing nodes in the system when the mimetic computing system to be tested runs the same type of test task, and determine the running performance of the mimetic computing system to be tested when running the same type of test task; The method further comprises: According to the network variability and the computing performance, a test result of the simulated computing system to be tested for each type of test task is determined.

7. The method according to claim 1, characterized in that Determining a test result of the mimetic computing system to be tested according to the network variability, the functional completeness, and the computing performance, specifically includes: A test result of the mimetic computing system to be tested is determined according to the network variability, the functional completeness and the computing performance, and preset test weights corresponding to the network variability, the functional completeness and the computing performance, respectively.

8. A testing device for a mimic computing system, characterized in that: include: Determine the module and the mimic computing system to be tested; An execution module, using the mimic computing system to be tested to execute various test tasks; The computing module determines the network variability of the mimetic computing system to be tested according to the network structures formed by the computing nodes in the system when the mimetic computing system to be tested runs each test task. The stronger the network variability, the greater the difference in the network structure of the mimetic computing system to be tested when running each test task for the mimetic computing system to be tested. The functional completeness of the mimetic computing system to be tested is determined according to the test tasks successfully run by the mimetic computing system to be tested. The computing performance of the mimetic computing system to be tested is determined according to the running performance of the mimetic computing system to be tested when running each test task. The output module determines the test result of the simulated computing system to be tested according to the network variability, the functional completeness and the computing performance.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

10. A device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

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