Accurate computing power scheduling method and device for intelligent computing center

Abstract

The invention provides a computing power accurate scheduling method and device of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, receiving a target request used for processing a target computing task; s2, determining a first target node according to the target calculation task and preset mapping information, wherein the preset mapping information is used for representing a mapping relationship between the calculation task and the node; and S3, scheduling the computing power resource of the first target node to process the target computing task. Nodes do not need to be distributed randomly, the phenomenon that the computing power resources of the randomly distributed nodes are poor is reduced, the computing power resources of the first target node are good, and the computing power of an intelligent computing center is enhanced, so that the processing efficiency of the target computing task is greatly improved, and the computing efficiency of the target computing task is improved. And the accuracy of computing power scheduling of the intelligent computing center is greatly improved, so that a large amount of waste of computing power resources is avoided.

Description

Technical Field

[0001] The present invention relates to the technical fields of intelligent computing centers, intelligent computing centers, and computing power infrastructure technologies, and in particular, to a method and device for accurately scheduling the computing power of an intelligent computing center. Background Art

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "intelligent computing centers" have emerged as the times require.

[0003] An "intelligent computing center" refers to a facility that provides the required computing power, data, and algorithms mainly for artificial intelligence applications (such as scenarios of artificial intelligence deep learning model development, model training, and model inference) by using large-scale heterogeneous computing power resources, including general computing power and intelligent computing power. The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from the underlying computing power to the top-level application enabling.

[0004] The "intelligent computing center" includes but is not limited to the "intelligent computing center".

[0005] An "intelligent computing center", that is, an artificial intelligence computing center, is a type of computing power infrastructure that provides computing power services, data services, and algorithm services required for artificial intelligence applications based on artificial intelligence theory and using an artificial intelligence computing architecture.

[0006] "Computing power" is the core of "intelligent computing centers" and "intelligent computing centers", and is the ability of computer devices or computing / data centers to process information. It is the ability of computer hardware and software to cooperate to jointly execute a certain computing requirement. It is the computing ability to process information data and output the target result. It is a new type of productive force integrating information computing power, network carrying capacity, and data storage capacity, and mainly provides services to society through computing power infrastructure.

[0007] Currently, when receiving a request for processing a computing task, usually a certain node is randomly assigned to the above computing task, and the computing power resources of the node are scheduled to process the above computing task. It can be seen that when the computing power resources of the randomly assigned node are poor, it is easy to cause a very low processing efficiency of the above computing task. Summary of the Invention

[0008] The present invention provides a method and device for accurately scheduling the computing power of an intelligent computing center, which are used to solve the problem of very low processing efficiency of computing tasks.

[0009] In order to solve the above technical problems, the present invention is implemented as follows:

[0010] In a first aspect, the present invention provides a method for accurately scheduling the computing power of an intelligent computing center, including:

[0011] Step S1: Receive a target request for processing a target computing task;

[0012] Step S2: Determine a first target node according to the target computing task and preset mapping information, where the preset mapping information is used to represent the mapping relationship between computing tasks and nodes;

[0013] Step S3: Schedule the computing power resources of the first target node to process the target computing task.

[0014] Optionally, step S2 includes:

[0015] Step S21: Obtain a target label carried by the target request, where the target label is a label of the target computing task;

[0016] Step S22: Determine the first target node according to the target label and the preset mapping information, where the target label corresponds to the first target node in the preset mapping information.

[0017] Optionally, step S22 includes:

[0018] Step S221: Input the target label into a preset model to query a node label corresponding to the target label, where the preset model stores the preset mapping information;

[0019] Step S222: Determine the first target node corresponding to the node label.

[0020] Optionally, before step S1, the method further includes:

[0021] Step S4: Display a preset interface, and an information for inputting a node label of the first target node is provided in a first display box of the preset interface;

[0022] Step S5: In the case where the information of the node label of the first target node is input in the first display box, input the information of the computing power resources of the first target node in a second display box of the preset interface, and associate the node label of the first target node with the computing power resources of the first target node.

[0023] Optionally, after step S3, the method further includes:

[0024] Step S6: Display target information, where the target information includes at least one of the following information: the processing progress of the target computing task and the utilization rate of the computing power resources of the first target node;

[0025] Step S7: Perform a target operation according to the target information.

[0026] Optionally, the target information includes: the processing progress of the target computing task and the utilization rate of the computing power resources of the first target node, and step S7 includes:

[0027] Step S71: When the duration for which the processing progress is the target progress is greater than a preset duration and the utilization rate of the computing power resources of the first target node is greater than a preset value, schedule the computing power resources of the second target node, where the utilization rate of the computing power resources of the second target node is less than or equal to the preset value;

[0028] Step S72: Use the computing power resources of the second target node to process the target computing task.

[0029] In a second aspect, the present invention provides a computing power precise scheduling device for an intelligent computing center, including:

[0030] A receiving module, configured to receive a target request for processing a target computing task;

[0031] A determining module, configured to determine a first target node according to the target computing task and preset mapping information, where the preset mapping information is used to represent the mapping relationship between computing tasks and nodes;

[0032] A scheduling module, configured to schedule the computing power resources of the first target node to process the target computing task.

[0033] In a third aspect, the present invention provides an electronic device, including: a processor, a memory, and a program stored on the memory and executable on the processor, where when the program is executed by the processor, the steps of the computing power precise scheduling method for the intelligent computing center as described in the first aspect above are implemented.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the computing power precise scheduling method for the intelligent computing center as described in the first aspect above are implemented.

[0035] In a fifth aspect, the present invention provides a computer program product, including computer instructions, and when the computer instructions are executed by a processor, the steps of the computing power precise scheduling method for the intelligent computing center as described in the first aspect above are implemented.

[0036] In the present invention, a target request for processing a target computing task is received; a first target node is determined according to the target computing task and preset mapping information, where the preset mapping information is used to represent the mapping relationship between computing tasks and nodes; and the computing power resources of the first target node are scheduled to process the target computing task.

[0037] In this way, when a target request for processing a target computing task is received, the first target node can be determined according to the target computing task and the preset mapping information, and the computing power resources of the first target node can be scheduled to process the target computing task. That is, in the present invention, the fixed first target node can be determined according to the target computing task and the preset mapping information, without randomly allocating nodes, thus reducing the occurrence of the phenomenon that the computing power resources of randomly allocated nodes are relatively poor. Since the computing power resources of the first target node are very good and the computing power of the intelligent computing center is added, the processing efficiency of the target computing task is greatly improved, and the accuracy of the computing power scheduling of the intelligent computing center is greatly improved, thereby avoiding a large amount of waste of computing power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0039] Figure 1 is a flowchart of a method for precise computing power scheduling of an intelligent computing center provided by the present invention;

[0040] Figure 2 is a structural schematic diagram of a device for precise computing power scheduling of an intelligent computing center provided by the present invention;

[0041] Figure 3 is a structural schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described content is part of the present invention, not all of it. Based on the content in the present invention, all other content obtained by those of ordinary skill in the art without creative efforts belongs to the scope of protection of the present invention. The "computing power" described in the present invention refers to: the ability of a computer device or a computing / data center to process information, the ability of computer hardware and software to cooperate to execute a certain computing requirement, the computing ability to achieve the output of a target result by processing information data, a new type of productive force integrating information computing power, network carrying capacity, and data storage capacity, and mainly providing services to society through computing power infrastructure.

[0043] The "Computational Power (CP)" described in the present invention refers to: the ability of a data center server to process data and output results, which is a comprehensive indicator for measuring the computing power of a data center and includes general computing power, supercomputing power, and intelligent computing power. The commonly used measurement unit is the number of floating-point operations per second (FLOPS, 1 EFLOPS = 10^18 FLOPS), and the larger the value, the stronger the comprehensive computing power. It is estimated that 1 EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream laptops. The calculation formula is: CP = CP 通用 + CP 智能 + CP 超级 。

[0044] The "Network Power (NP)" described in the present invention refers to: the performance of the data transmission ability of computing power facilities, which is a comprehensive ability including network architecture, network bandwidth, transmission delay, intelligent management and scheduling, etc., and involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling ability.

[0045] The "Storage Power (SP)" described in the present invention refers to: the comprehensive ability of a data center in four aspects: data storage capacity, performance, security and reliability, and green and low-carbon, which is a comprehensive indicator for measuring the data storage ability of a data center and includes external storage devices such as storage arrays and server internal storage devices. The commonly used measurement unit for storage capacity is exabyte (EB, 1 EB = 2^60 bytes), the commonly used measurement unit for performance is the number of read and write operations per second per unit capacity (IOPS / TB, Input / Output Operations Per Second / TB), and the disaster recovery ratio is an important manifestation of security and reliability.

[0046] The "computing power infrastructure" described in the present invention refers to: a new type of information infrastructure that integrates information computing power, network carrying power, and data storage power, and can realize the centralized computing, storage, transmission, and application of information.

[0047] The "new type of information infrastructure" described in the present invention mainly includes network infrastructures such as 5G networks, fiber broadband networks, backbone networks, international communication networks, and satellite Internet, computing power infrastructures such as data centers, general computing power centers, intelligent computing centers, and supercomputing centers, and new technology infrastructures such as artificial intelligence, blockchain, and quantum computing.

[0048] The "computing power" described in the present invention includes: general computing power, intelligent computing power, and supercomputing power.

[0049] The "general computing power" described in the present invention refers to the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0050] The "intelligent computing power" described in the present invention refers to a computing platform that is deployed on a large scale for various artificial intelligence innovation applications based on dedicated chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit), such as natural language processing, machine vision, and so on.

[0051] The "super computing power" described in the present invention mainly refers to the computing power provided by high-performance computing clusters such as supercomputers. It utilizes the centralized computing resources of multiple computer systems working in parallel and processes extremely complex or data-intensive problems through a dedicated operating system. It is mainly used for computing in cutting-edge scientific fields, such as planetary simulation, drug molecule design, gene analysis, etc.

[0052] The "intelligent computing center" described in the present invention refers to a facility that mainly provides the required computing power, data, and algorithms for artificial intelligence applications (such as scenarios like artificial intelligence deep learning model development, model training, and model inference) by using large-scale heterogeneous computing power resources, including general computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enabling.

[0053] The "intelligent computing center" described in the present invention includes, but is not limited to, the "intelligent computing center".

[0054] The "intelligent computing center" described in the present invention, that is, the artificial intelligence computing center, is a type of computing power infrastructure that provides computing power services, data services, and algorithm services required for artificial intelligence applications based on artificial intelligence theory and using an artificial intelligence computing architecture.

[0055] The "computing power center" described in the present invention refers to a facility mainly composed of infrastructure such as wind, fire, water, and electricity and IT software and hardware devices, and having computing power, carrying capacity, and storage capacity, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0056] The "supercomputing center" described in the present invention refers to: namely, a supercomputing data center, which is a data center based on supercomputers or large-scale computing clusters and can provide functions such as large-scale computing, storage, and network services. It is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling, and genome sequencing.

[0057] The "computing power resources" described in the present invention refer to: technologies and facilities with information computing, transmission, storage, and application capabilities required for the development of the digital society, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and support and guarantee resources such as wind, fire, water, and electricity.

[0058] The "models" described in the present invention include but are not limited to "large language models" and "multimodal large models".

[0059] The "large language model" described in the present invention refers to a large language model (LLM), which is a language model with a relatively large number of parameters and is designed to understand and generate human language. It is trained through a large amount of text data and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0060] The "multimodal large model" (Multimodal Large Models) described in the present invention refers to: a model trained by jointly combining multimodal information such as text, images, videos, and audio, including but not limited to multimodal large language models.

[0061] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for accurately scheduling the computing power of an intelligent computing center provided by the present invention. As Figure 1 shown, it includes the following steps:

[0062] Step S1: Receive a target request, where the target request is used to process a target computing task;

[0063] Among them, the input method of the target request is not limited herein. Optionally, the input method of the target request may include at least one of the following methods: voice input, touch input, press input, etc. In this way, through the above input methods, the target request can be sent, and the target request can carry a request message for processing the target computing task, and the request message can correspond to the input method of the target request. For example: when the input method of the target request is voice input, the request message can be voice information; when the input method of the target request is touch input, the request message can be a touch signal; when the input method of the target request is press input, the request message can be a press signal.

[0064] It should be noted that, optionally, when the input method of the target request includes multiple methods, the types of request messages can also include multiple types. For example, when the input method of the target request includes voice input and touch input, the request message can include voice information and touch signals. In this way, the voice information and touch signals can be mutually verified, supplemented, and corrected, thereby improving the accuracy of the input request message.

[0065] The specific type of the target computing task is not limited herein. Optionally, the target computing task can be a computing task for calculating the training parameters of a model. Alternatively, the target computing task can also be a computing task for calculating a navigation route.

[0066] Step S2: Determine a first target node according to the target computing task and the preset mapping information, where the preset mapping information is used to represent the mapping relationship between the computing task and the node.

[0067] Among them, the preset mapping information can be pre-stored information, that is, the present invention can be applied to an electronic device, and the preset mapping information can be pre-stored on the electronic device; or, optionally, the electronic device can receive the target request and the preset mapping information sent by a first electronic device, and the preset mapping information and the target computing task can be information pre-sent by a second electronic device to the first electronic device, that is, the first electronic device is used to forward the preset mapping information and the target computing task. In this way, by the second electronic device determining the preset mapping information and the target computing task, the consumption of the computing power resources of the electronic device in the present invention can be reduced, and the determination efficiency of the preset mapping information and the target computing task is improved.

[0068] Among them, the electronic device of the present invention can include multiple nodes, and each node can have corresponding computing power resources. In this way, the first target node can be determined from multiple nodes through the target computing task and the preset mapping information, that is, the determination efficiency of the first target node is improved.

[0069] Step S3: Schedule the computing power resources of the first target node to process the target computing task.

[0070] The specific type of the computing power resources of the first target node is not limited herein. Optionally, the computing power resources of the first target node can include at least one of the following: the computing power resources of a Graphics Processing Unit (GPU), the computing power resources of a Central Processing Unit (CPU), etc.

[0071] In the present invention, through steps S1 to S3, when a target request for processing a target computing task is received, a first target node can be determined according to the target computing task and preset mapping information, and the computing power resources of the first target node are scheduled to process the target computing task. That is, in the present invention, a fixed first target node can be determined according to the target computing task and preset mapping information, without the need for random node allocation, thereby reducing the occurrence of the phenomenon that the computing power resources of randomly allocated nodes are poor. Since the computing power resources of the first target node are very good and the computing power of the intelligent computing center is added, the processing efficiency of the target computing task is greatly improved, and the accuracy of the computing power scheduling of the intelligent computing center is greatly improved, thereby avoiding a large amount of waste of computing power resources.

[0072] Optionally, step S2 includes:

[0073] Step S21: Obtain the target label carried by the target request, where the target label is the label of the target computing task;

[0074] Step S22: Determine the first target node according to the target label and the preset mapping information, and the target label corresponds to the first target node in the preset mapping information.

[0075] Here, the specific content of the target label is not limited herein. Optionally, the content of the target label may be an identifier (ID); alternatively, the content of the target label may be the demand information of the computing power resources of the target computing task. For example, the demand information may include the computing power quantity of the GPU required by the target computing task and the computing power quantity of the CPU required by the target computing task.

[0076] Here, the target label corresponds to the first target node in the preset mapping information. Optionally, the information of the target label and the first target node may be associated and stored in the preset mapping information, and the above information of the first target node may be the ID or index information of the first target node, etc.

[0077] It should be noted that the ID or index information of the first target node, etc. may also be referred to as the node label in the following text, and the node label may also be referred to as a specific label.

[0078] In the present invention, the target label corresponds to the first target node in the preset mapping information. In this way, the storage resources for storing the mapping relationship between the target label and the first target node in the preset mapping information can be made smaller, saving storage resources, and at the same time, making the efficiency of determining the first target node based on the target label of the target computing task higher.

[0079] Optionally, step S22 includes:

[0080] Step S221: Input the target label into a preset model to query the node label corresponding to the target label. The preset mapping information is stored in the preset model.

[0081] Step S222: Determine the first target node corresponding to the node label.

[0082] Among them, the preset model can be a model pre-trained for querying node labels. The training process of the preset model can be referred to the following description: Use the sample labels of computing tasks as the input information of the model to be trained, so that the model to be trained predicts node labels based on the sample labels. When the error value between the output node label and the sample label is less than a preset value, the model to be trained after being trained with the sample labels can be determined as the preset model.

[0083] It should be noted that the specific type of the preset model is not limited here. Optionally, the preset model can be a neural network model.

[0084] In the present invention, the preset mapping information is stored in the preset model. In this way, by querying the node label corresponding to the target label through the preset model, the query accuracy and efficiency of the node label can be improved.

[0085] Optionally, before step S1, the method further includes:

[0086] Step S4: Display a preset interface. The first display box of the preset interface is used to input information about the node label of the first target node.

[0087] Step S5: When the information about the node label of the first target node is input in the first display box, input the information about the computing power resources of the first target node in the second display box of the preset interface, and associate the node label of the first target node with the computing power resources of the first target node.

[0088] Among them, the specific ways of inputting information in the first display box and the second display box are not limited here. Optionally, the corresponding information can be input in the first display box and the second display box through voice input, touch input or press input.

[0089] For example: Multiple controls can be floatingly displayed in the first display box, and the multiple controls respectively represent information about different node labels. By the user's input for at least some of the multiple controls, the information about the node label corresponding to the selected control by the user can be determined as the information about the node label to be input in the first display box. Similarly, the way of inputting the information about the computing power resources of the first target node in the second display box can refer to the relevant description of the way of inputting the information about the node label in the first display box, and will not be elaborated here specifically.

[0090] It should be noted that the display mode of the preset interface is not limited herein. Optionally, the preset interface can be displayed in a floating manner.

[0091] In the present invention, by displaying the preset interface, and the information of the node label of the first target node is used for input in the first display box of the preset interface, and the information of the computing power resource of the first target node is used for input in the second display box of the preset interface. In this way, the above-mentioned preset interface provides a shortcut for setting the information of the node label of the first target node and the information of the computing power resource of the first target node, and improves the efficiency of associating the information of the node label of the first target node and the computing power resource of the first target node.

[0092] Optionally, after step S3, the method further includes:

[0093] Step S6: Display the target information, where the target information includes at least one of the following information: the processing progress of the target computing task and the utilization rate of the computing power resource of the first target node;

[0094] Step S7: Perform a target operation according to the target information.

[0095] Among them, the target information may further include other information. For example: the target information may further include the fault information of the electronic device, and the fault information may include information such as the fault location and the fault type.

[0096] In the present invention, by displaying the target information, the electronic device can monitor the processing progress of the target computing task and the utilization rate of the computing power resource of the first target node, enhancing the monitoring effect of the target information. At the same time, when performing the target operation according to the above target information, the accuracy and flexibility of the execution result of the target operation can be improved, and the occurrence of misoperation phenomena can be reduced.

[0097] Optionally, the target information includes: the processing progress of the target computing task and the utilization rate of the computing power resource of the first target node, and step S7 includes:

[0098] Step S71: When the continuous duration of the processing progress being the target progress is greater than the preset duration, and the utilization rate of the computing power resource of the first target node is greater than the preset value, schedule the computing power resource of the second target node, where the utilization rate of the computing power resource of the second target node is less than or equal to the preset value;

[0099] Step S72: Process the target computing task by using the computing power resource of the second target node.

[0100] Wherein, the specific values of the preset duration and the preset value are not limited herein. Optionally, the preset duration and the preset value can be empirical values, that is, the above-mentioned empirical values can be values obtained by summarizing and calculating multiple sample data; alternatively, the preset duration and the preset value can also be values calculated by artificial intelligence (AI).

[0101] It should be noted that the second target node and the first target node can both be different nodes in the electronic device of the present invention, and the second target node can be referred to as an alternative node of the first target node.

[0102] It should be noted that, optionally, the second target node can also be stored in the preset mapping information, and there can be two nodes corresponding to the target calculation task in the preset mapping information. The above two nodes include the first target node and the second target node, and the priority of the first target node is higher than that of the second target node. That is, the computing power resources of the first target node are preferentially scheduled to process the target calculation task. Only when it is determined that the first target node may have a fault, the computing power resources of the second target node are scheduled to process the target calculation task.

[0103] Among them, the computing power resources of the second target node can refer to the relevant description of the computing power resources of the first target node above, and will not be elaborated herein.

[0104] In the present invention, when the duration of the processing progress being the target progress is greater than the preset duration, and the utilization rate of the computing power resources of the first target node is greater than the preset value, the probability of a fault occurring in the first target node is relatively high at this time. Therefore, the computing power resources of the first target node can be scheduled to process the target calculation task, so as to ensure that the target calculation task is processed in a timely manner and enhance the processing effect of the target calculation task.

[0105] See Figure 2 , Figure 2 which is a schematic structural diagram of a computing power precise scheduling device of an intelligent computing center provided by the present invention. As Figure 2 shown, the computing power precise scheduling device 200 of the intelligent computing center includes:

[0106] A receiving module 201, configured to receive a target request, where the target request is used to process a target calculation task;

[0107] A determining module 202, configured to determine a first target node according to the target calculation task and preset mapping information, where the preset mapping information is used to represent the mapping relationship between the calculation task and the node;

[0108] A scheduling module 203, configured to schedule the computing power resources of the first target node to process the target calculation task.

[0109] Optionally, the determination module 202 includes:

[0110] An acquisition sub-module, configured to acquire a target label carried in the target request, where the target label is a label of the target computing task;

[0111] A determination sub-module, configured to determine the first target node according to the target label and the preset mapping information, where the target label corresponds to the first target node in the preset mapping information.

[0112] Optionally, the determination sub-module includes:

[0113] A query unit, configured to input the target label into a preset model to query a node label corresponding to the target label, where the preset mapping information is stored in the preset model;

[0114] A determination unit, configured to determine a first target node corresponding to the node label.

[0115] Optionally, the computing power precise scheduling device 200 of the intelligent computing center further includes:

[0116] A first display module, configured to display a preset interface, and an information for inputting a node label of the first target node is used in a first display box of the preset interface;

[0117] An association module, configured to, when information of the node label of the first target node is input in the first display box, input information of computing power resources of the first target node in a second display box of the preset interface, and associate the node label of the first target node with the computing power resources of the first target node.

[0118] Optionally, the computing power precise scheduling device 200 of the intelligent computing center further includes:

[0119] A second display module, configured to display target information, where the target information includes at least one of the following information: a processing progress of the target computing task and a utilization rate of computing power resources of the first target node;

[0120] An execution module, configured to execute a target operation according to the target information.

[0121] Optionally, the target information includes: a processing progress of the target computing task and a utilization rate of computing power resources of the first target node, and the execution module includes:

[0122] A scheduling sub-module, configured to schedule the computing power resources of a second target node when the duration of the processing progress being the target progress is greater than a preset duration and the utilization rate of the computing power resources of the first target node is greater than a preset value, and the utilization rate of the computing power resources of the second target node is less than or equal to the preset value;

[0123] A processing sub-module, configured to process the target computing task by using the computing power resources of the second target node.

[0124] The computing power precise scheduling device 200 of the intelligent computing center provided by the present invention can execute each step in the above-mentioned computing power precise scheduling method of the intelligent computing center, and thus has the same beneficial technical effects as the above-mentioned computing power precise scheduling method of the intelligent computing center, which will not be elaborated herein.

[0125] Please refer to Figure 3 , the present invention also provides an electronic device 30, including a processor 31, a memory 32, and a computer program stored on the memory 32 and executable on the processor 31. When the computer program is executed by the processor 31, it realizes each process shown in the above-mentioned computing power precise scheduling method of the intelligent computing center and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0126] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes each process of the above-mentioned computing power precise scheduling method of the intelligent computing center and can achieve the same technical effects. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0127] The present application also provides a computer program product, including computer instructions, which when executed by a processor, realize each process of the above-mentioned Figure 1 shown computing power precise scheduling method of the intelligent computing center and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0128] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the presence of additional identical elements in the process, method, article or device including such element.

[0129] From the description of the above embodiments, those skilled in the art can clearly understand that the method provided by the above invention can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the various methods provided by the present invention.

[0130] The present invention has been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention, and all of them fall within the protection scope of the present invention.

Claims

1. A method for precise computing power scheduling of an intelligent computing center, characterized in that, Including: Step S1: Receive a target request for processing a target computing task; Step S2: Determine a first target node according to the target computing task and preset mapping information, where the preset mapping information is used to represent the mapping relationship between computing tasks and nodes; Step S3: Schedule the computing power resources of the first target node to process the target computing task.

2. The method according to claim 1, wherein The step S2 includes: Step S21: Obtain the target label carried by the target request, where the target label is the label of the target computing task; Step S22: Determine the first target node according to the target label and the preset mapping information, and the target label corresponds to the first target node in the preset mapping information.

3. The method according to claim 2, wherein The step S22 includes: Step S221: Input the target label into a preset model to query the node label corresponding to the target label, where the preset mapping information is stored in the preset model; Step S222: Determine the first target node corresponding to the node label.

4. The method according to claim 3, characterized in that, Before step S1, the method further includes: Step S4: Display a preset interface, and an information for inputting the node label of the first target node is in the first display box of the preset interface; Step S5: When the information of the node label of the first target node is input in the first display box, input the information of the computing power resources of the first target node in the second display box of the preset interface, and associate the node label of the first target node with the computing power resources of the first target node.

5. The method according to any one of claims 1 to 4, characterized in that, After step S3, the method further includes: Step S6: Display target information, where the target information includes at least one of the following information: the processing progress of the target computing task and the utilization rate of the computing power resources of the first target node; Step S7: Perform a target operation according to the target information.

6. The method according to claim 5, characterized in that, The target information includes: the processing progress of the target computing task and the utilization rate of the computing power resources of the first target node, and the step S7 includes: Step S71: When the continuous duration of the target progress of the processing progress is greater than a preset duration and the utilization rate of the computing power resources of the first target node is greater than a preset value, schedule the computing power resources of a second target node, where the utilization rate of the computing power resources of the second target node is less than or equal to the preset value; Step S72: Use the computing power resources of the second target node to process the target computing task.

7. An arithmetic power precise scheduling device for an intelligent computing center, characterized in that, Including: A receiving module for receiving a target request for processing a target computing task; A determining module for determining a first target node according to the target computing task and preset mapping information, where the preset mapping information is used to represent the mapping relationship between computing tasks and nodes; A scheduling module for scheduling the computing power resources of the first target node to process the target computing task.

8. An electronic device, characterized in that, Including: A processor, a memory, and a program stored on the memory and executable on the processor. When the program is executed by the processor, the steps of the method for precise scheduling of computing power of the intelligent computing center according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the computing power precise scheduling method of the intelligent computing center according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that, It includes computer instructions, and when the computer instructions are executed by a processor, the steps of the computing power precise scheduling method of the intelligent computing center according to any one of claims 1 to 6 are implemented.

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