Login node of HPC and management method, system and device thereof, and storage medium

Abstract

The invention relates to an HPC login node and a management method, system and device thereof, and a storage medium. The login node comprises an instruction receiving module used for receiving a user instruction to access the resource node; the user behavior detection module is used for analyzing the operation behavior of the user; and the operation suggestion module is used for feeding back suggested operation to the user based on the detection result of the user behavior detection module and the running state of the HPC. According to the HPC login node, through integrating the user behavior detection and operation suggestion module, the user operation behavior can be analyzed in real time, personalized optimization suggestions can be provided, and the user experience and the working efficiency can be effectively improved. The system not only optimizes the utilization rate of HPC resources, but also enhances the adaptability and security of the cluster, and meanwhile, the suggestion strategy is continuously improved through user feedback, so that the continuous optimization of resource management is realized, and the overall efficiency of the HPC and the use experience of the user are improved.

Description

Technical Field

[0001] The present application relates to the technical field of high performance computing clusters, and specifically, to a login node of an HPC and a management method, system, device and storage medium thereof. Background Art

[0002] With the rapid development of the biomedical field, the complexity of scientific research computing and data processing is also increasing. The large-scale data processing and complex computing requirements involved in biomedical analysis make high-performance computing clusters (HPC) an indispensable and important tool in this field. HPC can significantly improve the data analysis speed and processing efficiency in biomedical research by providing large-scale parallel computing capabilities and efficient data processing capabilities, thereby accelerating the progress of scientific research. However, despite the core role of HPC in biomedical analysis, existing HPC systems generally lack analysis of user behavior. This lack of functionality has seriously affected the further improvement of HPC in terms of resource utilization, workflow optimization, and system management efficiency.

[0003] Therefore, current HPC management methods still need to be improved. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to a certain extent. To this end, one purpose of the present application is to provide a HPC login node management method that effectively improves user experience.

[0005] Specifically, this application proposes the following technical solutions:

[0006] In the first aspect, the present application proposes a login node for HPC. According to an embodiment of the present application, the login node includes: an instruction acceptance module for accepting user instructions to access resource nodes; a user behavior detection module for analyzing the user's operation behavior; and an operation suggestion module for providing the user with feedback on recommended operations based on the detection results of the user behavior detection module and the operating status of the HPC.

[0007] In some examples of this application, the aforementioned HPC login node can analyze user operation behavior in real time and provide personalized optimization suggestions by integrating user behavior detection and operation suggestion modules, significantly improving user experience and work efficiency. This system not only optimizes the utilization of HPC resources, but also enhances the adaptability and security of the cluster. At the same time, it continuously improves the suggestion strategy through user feedback, realizes the continuous optimization of resource management, and improves the overall performance of HPC and user experience.

[0008] In a second aspect, the present application proposes a method for managing login nodes of an HPC. According to an embodiment of the present application, the method includes: collecting historical behavior data of users on the HPC side; analyzing the historical behavior data, and providing the user with feedback on a user behavior adjustment strategy based on the analysis results.

[0009] In some examples of this application, the above method can provide users with personalized behavior adjustment strategies by collecting and analyzing historical behavior data of users, thereby accurately optimizing user operations and improving HPC resource utilization and computing efficiency. At the same time, the method supports dynamic optimization and continuous improvement, significantly improving user experience.

[0010] In the third aspect, the present application proposes a login node management system for HPC. According to an embodiment of the present application, the system includes: a behavior data collection module for collecting historical behavior data of users on the HPC side; a behavior analysis and strategy feedback module for analyzing the historical behavior data and providing the user with feedback on the user behavior adjustment strategy based on the analysis results.

[0011] In some examples of this application, the aforementioned system can effectively collect historical behavior data of users on the HPC side through the cooperation of the behavior data collection module and the behavior analysis and strategy feedback module, and provide personalized behavior adjustment strategies to users based on in-depth analysis. This system can not only help users optimize operations and improve scientific research computing efficiency, but also improve the utilization rate of HPC resources. At the same time, it can realize dynamic adjustment through continuous data analysis and feedback, significantly improving the user experience.

[0012] In a fourth aspect of the present application, the present application proposes a computer program product. According to an embodiment of the present application, the computer program product includes: computer instructions; when part or all of the computer instructions are run on a computer, the HPC login node management method described in the second aspect of the present application is executed.

[0013] In a fifth aspect of the present application, the present application proposes a computing device. According to an embodiment of the present application, the computing device includes: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program to implement the HPC login node management as described in the second aspect of the present application.

[0014] In a sixth aspect of the present application, the present application proposes a computer-readable storage medium. According to an embodiment of the present application, the computer-readable storage medium stores a computer instruction or program, and when the computer instruction or program is executed on a computer, the HPC login node management as described in the second aspect of the present application is executed.

[0015] The aforementioned computer program product, computing device, and computer-readable storage medium automatically perform HPC login node management through computer instructions, achieving efficient automation and improving screening efficiency and accuracy. Secondly, the characteristics based on instructions make the above method highly consistent, stable, and reliable in different experimental scenarios.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of the HPC login node structure provided for the specific implementation of this application;

[0019] Figure 2 A flowchart of a method for managing a login node of an HPC provided in a specific embodiment of the present application;

[0020] Figure 3 A schematic diagram of a login node management system for HPC provided for a specific implementation of the present application;

[0021] Figure 4 A schematic diagram of the bioinformatics behavior matrix provided for the specific implementation of this application;

[0022] Figure 5 A schematic diagram of an electronic device provided for a specific implementation of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In the present application, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the description of the present application, unless otherwise specified, "multiple" refers to two or more than two.

[0025] In view of the current problem that HPC only provides computing resources but does not analyze user behavior, resulting in waste of resources, performance degradation, and poor experience, this application proposes an HPC login node, an HPC login node management method, an HPC login node management system, a computer program product, a computing device, and a computer-readable storage medium. They are described below:

[0026] HPC login node

[0027] On the one hand, the present application proposes a HPC login node, referring to Figure 1 ,include:

[0028] Instruction receiving module 01, used to receive user instructions to access resource nodes;

[0029] In some examples of this application, this module is responsible for receiving various instructions issued by users to the HPC login node, mainly including requests to access resource nodes. These instructions usually involve operations such as task submission, data processing, and job scheduling, and serve as an interface for users to interact with the HPC system. Specifically, this module is used to parse the instructions submitted by the user and convert them into a form that the system can recognize; further, the parsed request is passed to the resource node to trigger the corresponding computing task or data processing operation.

[0030] In addition, this module is also responsible for verifying user identity to ensure that only authorized users can access resource nodes.

[0031] User behavior detection module 02, used to analyze the user's operation behavior;

[0032] In some examples of this application, this module is responsible for real-time detection and recording of user operation behaviors in the HPC system. These behavioral data cover how users use computing resources, how workflows are executed, how often tasks are submitted, and so on. Specifically, the user's operation data is continuously collected, including instruction type, amount of resources used, jobs run, and time and frequency of operations; and through analysis of the data, the module can identify the user's operation mode, behavior habits, and interaction characteristics with the system.

[0033] In addition, this module can detect abnormal operating behaviors, such as resource abuse or potential security threats, so as to issue warnings or take measures in a timely manner.

[0034] The operation suggestion module 03 is used to provide the user with operation suggestions based on the detection result of the user behavior detection module and the operation status of the HPC.

[0035] In some examples of this application, this module generates personalized operation suggestions based on the data provided by the user behavior detection module and the current operating status of the HPC system, aiming to help users use cluster resources more efficiently and reasonably and improve the user experience. Specifically, this module combines the user's operation behavior data and the system operation load status to analyze the user's usage habits and identify optimization opportunities in resource utilization and job scheduling; based on the analysis results, it provides feedback to the user on operation suggestions, which may involve resource allocation, job priority adjustment, or data processing optimization.

[0036] In addition, the module can continuously adjust and optimize operation suggestions based on changes in system status and user operation feedback, helping users adapt to the dynamically changing HPC environment and thus maximize resource utilization and job performance.

[0037] HPC login node management method

[0038] On the other hand, this application proposes a HPC login node management method, referring to Figure 2 , the method comprises the following steps:

[0039] S10, collecting historical behavior data of users on the HPC side;

[0040] In some examples of this application, the aforementioned historical behavior data includes: user information, bioinformatics analysis workflow, and analysis workflow operation information.

[0041] The user information includes but is not limited to the user's age, profession, analysis field, operation time, operation frequency, etc.;

[0042] The bioinformatics analysis workflow includes, but is not limited to, one or more of the specific analysis steps in whole genome sequencing (WGS) analysis, whole exome sequencing (WES) analysis, RNA-seq data analysis, ChIP-seq analysis, DNA methylation analysis, and mass spectrometry data analysis. The aforementioned specific analysis steps include at least one of data quality control, alignment to a reference genome or transcriptome, expression calculation, differential expression analysis, gene function enrichment analysis, alternative splicing analysis, assembly and gene prediction, functional annotation, and community comparison and functional difference analysis.

[0043] The analysis workflow operation information includes: the analysis time corresponding to the bioinformatics analysis workflow, such as specific task items, login time, task submission time, and task end time.

[0044] In order to effectively achieve data isolation, improve the security of the system, and prevent data leakage and unauthorized access. In some examples of this application, different users correspond to different storage spaces. For example, when user A is created, the system will automatically allocate an independent storage space for it, which ensures that the data of different users are isolated and independent of each other. The above independent storage space avoids data conflicts and competitive access problems, improves the access efficiency and response speed of the system, and provides users with a more efficient and secure user experience.

[0045] In the storage of historical behavior data of the same user, the historical behavior data of the user is clustered and stored according to the user behavior type. In some examples of the present application, the storage is performed in a key-value pair manner.

[0046] The aforementioned term "Key-Value Pair" is a data storage structure in which each data item consists of a unique key (Key) and a corresponding value (Value). The key is used as an identifier to quickly locate and retrieve the value associated with it. For example, in user information storage, the key is "User A-1" and the corresponding value is the user's "Data Quality Control". Storing in the form of key-value pairs can achieve rapid retrieval and statistics of historical behavior data, and the data is stored in the user's exclusive space. Each exclusive space has a storage area for each category. Therefore, from the beginning of data storage, it has been classified and stored according to the user's behavior type, and the storage space utilization rate is provided at the same time.

[0047] For example, all operation data of user A, such as submitted workflows, resource usage, etc., will be stored in the form of key-value pairs in his personal space. For different types of user behavior data, the system will cluster and store them according to the type of behavior. For example, all data related to bioinformatics analysis will be stored in one logical partition, and job scheduling data will be stored in another partition.

[0048] S20, analyzing the historical behavior data, and providing the user with feedback on a user behavior adjustment strategy based on the analysis result.

[0049] In some examples of the present application, the step of providing the user with a user behavior adjustment strategy based on the analysis result includes: comparing the analysis result with the cluster storage result to determine the user behavior adjustment strategy. The user behavior adjustment strategy includes: at least one of a resource allocation strategy, a job scheduling strategy, and a network security strategy.

[0050] After user A's behavior data is analyzed, the system will compare these analysis results with the clustering data previously stored in the system. By analyzing the differences between user A's historical behavior patterns and current behavior patterns, the system can determine the most appropriate user behavior adjustment strategy. For example, if the analysis finds that the RNA-seq data analysis process used by user A takes a long time, the system may recommend adjusting the resource allocation strategy and providing more CPU or memory resources to speed up the process. In addition, the system may recommend optimizing the job scheduling strategy, such as using other more efficient workflows to reduce execution time. For some operations involving data security, the system may also provide feedback on network security-related policy recommendations to ensure data security.

[0051] In some examples of the present application, the aforementioned method further includes: the user provides feedback on the user behavior adjustment strategy. By forming a cycle between system analysis and user feedback, continuous improvement is achieved, which helps to improve work efficiency and user experience.

[0052] In some examples of the present application, the analysis results include: at least one of: user behavior patterns and HPC resource usage habits. For example, by analyzing the historical behavior data of user A, the system can identify user A's behavior patterns and resource usage habits. For example, the system can detect that user A frequently uses certain specific workflows (such as KEGG pathway analysis) and usually submits jobs at night. At the same time, the system can also identify user A's habits of using resources in the HPC cluster, for example, user A often consumes a lot of CPU time, but memory usage is low. Based on these behavior patterns and usage habits, the system can provide user A with more personalized suggestions to optimize its resource allocation and job scheduling strategies, thereby improving work efficiency.

[0053] Different users may adopt different workflows or computing strategies when performing bioinformatics analysis, and their operational behaviors may also differ significantly. These differences in user behavior directly affect the allocation of HPC resources, job scheduling, and overall system performance. This application fully understands the user's workflow pattern and resource usage habits by detecting and analyzing the user's operational behavior in real time and accurately, and then effectively optimizes the system, maximizes the use of system resources, and improves the user experience.

[0054] In some examples of this application, when a user uses the aforementioned login node, an independent container can also be allocated to the user based on the user's login information, in which the user has root privileges; the user directory of the aforementioned user is mounted to the aforementioned container; and the aforementioned container is managed based on the running status of the aforementioned container. By allocating an independent container instance to each user, resource isolation and security improvement are achieved. The user has root privileges in the container and can freely configure the environment and install software.

[0055] HPC login node management system

[0056] On the other hand, this application proposes a HPC login node management system, referring to Figure 3 The system includes: a behavior data collection module 100 and a behavior analysis and strategy feedback module 200.

[0057] Module 100 is used to collect historical behavior data of users on the HPC side;

[0058] In some examples of this application, the aforementioned historical behavior data includes: user information, bioinformatics analysis workflow, and analysis workflow operation information.

[0059] The user information includes but is not limited to the user's age, profession, analysis field, operation time, operation frequency, etc.;

[0060] The bioinformatics analysis workflow includes, but is not limited to, one or more of the specific analysis steps in whole genome sequencing (WGS) analysis, whole exome sequencing (WES) analysis, RNA-seq data analysis, ChIP-seq analysis, DNA methylation analysis, and mass spectrometry data analysis. The aforementioned specific analysis steps include at least one of data quality control, alignment to a reference genome or transcriptome, expression calculation, differential expression analysis, gene function enrichment analysis, alternative splicing analysis, assembly and gene prediction, functional annotation, and community comparison and functional difference analysis.

[0061] The analysis workflow operation information includes: the analysis time corresponding to the bioinformatics analysis workflow, such as specific task items, login time, task submission time, and task end time.

[0062] In some examples of the present application, the aforementioned 100 module further includes: corresponding different storage spaces to different users, and clustering and storing the user's historical behavior data according to the user behavior type.

[0063] In some examples of the present application, the storage is performed in the form of key-value pairs.

[0064] Module 200 is used to analyze the historical behavior data and provide the user with user behavior adjustment strategies based on the analysis results.

[0065] In some examples of the present application, the aforementioned 200 module includes: analyzing data from the data storage module, such as extracting user behavior patterns, abnormal behaviors, and potential problems.

[0066] In some examples of the present application, the aforementioned 200 module further includes: determining the user behavior adjustment strategy based on the results of the analysis module; wherein the user behavior adjustment strategy includes: at least one of a resource allocation strategy, a job scheduling strategy, and a network security strategy.

[0067] In some examples of the present application, the aforementioned 200 module further includes: the user provides feedback on the user behavior adjustment strategy. Based on the user feedback, the system can automatically adjust the optimization suggestions or further analyze the user behavior data to provide more accurate optimization suggestions. It is understandable that this module allows the user to view the optimization suggestions (recommend new analysis workflows to users, etc.).

[0068] In some examples of the present application, the analysis result includes: at least one of a user behavior pattern and a HPC resource usage habit.

[0069] To facilitate understanding, the following describes the HPC login node management system in detail through a specific example:

[0070] Assume that the login node of the HPC cluster contains a behavior detection system that can execute the above method. When the HPC cluster creates a new user named Li Si, the behavior detection system will create a storage space for the user Li Si, and all subsequent operations of the user will be saved in this independent space.

[0071] When Li Si logs into the HPC cluster, the behavior detection system will match the user information in the behavior data collection module based on the lisi username, and store the user's behavior records in the user's exclusive data storage and management submodule in real time.

[0072] If Li Si submits a whole genome cell analysis process, the behavior detection system will record that the user has used the whole genome cell analysis process, and the behavior detection system will also record the start time, end time, and other information of the whole genome cell analysis process. With the user behavior detection data, the data analysis submodule will analyze the data: such as analyzing which processes the user has used and the corresponding running time of each process. After the analysis by the data analysis submodule, the data analysis submodule will use the empirical comparison method for analysis: by counting the bioinformatics analysis workflows selected by the user, and the running status of each workflow, and by comparing the bioinformatics behavior matrix, a user usage recommendation report is obtained.

[0073] Among them, Figure 4 As shown in the figure, the bioinformatics behavior matrix is ​​derived by the administrator based on historical data, which stores the process replacement logic. For example, if process A runs for more than 10 hours, then it is recommended to use process D. For another example, if process A + process B run for 15 hours, then it is recommended to use process D + process F + process K, and the task can be analyzed within 2 hours.

[0074] In some examples of the present application, the aforementioned modules are connected, wherein the aforementioned connection includes physical connection and network connection.

[0075] The aforementioned system has the function of automatic user behavior detection and analysis. By collecting historical operation data of users and providing personalized optimization suggestions, it can significantly improve the overall operation efficiency and resource utilization of the system. The system supports independent storage space to ensure data isolation and security, and optimizes the operation process through intelligent resource allocation and experience comparison method, reduces the operation running time, and improves user work efficiency.

[0076] It should be understood that the system embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, they will not be described here. Specifically, Figure 3 The system shown can execute the above-mentioned embodiment of the HPC login node management method, and the operations and / or functions performed by each module in the system correspond to the method embodiment, which will not be described here for brevity.

[0077] The system of the embodiment of the present application is described above from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software form instructions in the processor, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or a combination of hardware and software modules in the decoding processor to perform. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and completes the steps in the above method embodiment in conjunction with its hardware.

[0078] Computer program product, computing device, and computer readable storage medium

[0079] In another aspect of the present application, the present application proposes a computer program product, a computing device, and a computer-readable storage medium. Based on the aforementioned computer program product, computing device, or computer-readable storage medium, the aforementioned HPC login node management method is executed.

[0080] Descriptions of computer program products, computing devices, or computer-readable storage media may refer to each other. This article takes electronic devices as an example for detailed description. The so-called electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Computing devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0081] like Figure 5As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 to a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0082] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0083] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the login node management method of HPC. For example, in some embodiments, the login node management method of HPC may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the aforementioned HPC login node management method in any other appropriate manner (for example, by means of firmware).

[0084] In the present application, the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the aforementioned program may be printed, because the aforementioned program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting or processing in other suitable ways as necessary, and then storing it in a computer memory. The various computer-readable storage media described in the present invention may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0085] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0086] A person of ordinary skill in the art may understand that all or part of the steps in the method of the above-mentioned embodiment may be completed by instructing related hardware through a program, and the aforementioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0087] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0088] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0089] It should be noted that the features and technical effects described in this article for different aspects can be used as reference for each other and will not be repeated here.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0091] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present application without departing from the principles and purpose of the present application.

Claims

1. A HPC login node, characterized in that: include: An instruction accepting module, used to accept user instructions to access resource nodes; A user behavior detection module, used to analyze the user's operation behavior; The operation suggestion module is used to provide the user with operation suggestions based on the detection result of the user behavior detection module and the operation status of the HPC.

2. A method for managing HPC login nodes, characterized in that: include; Collect historical behavior data of users on the HPC side; The historical behavior data is analyzed, and a user behavior adjustment strategy is fed back to the user based on the analysis result.

3. The method according to claim 2, characterized in that Different users have different storage spaces. Optionally, cluster and store the historical behavior data of the user according to the user behavior type; Optionally, the storage is performed in the form of key-value pairs.

4. The method according to claim 3, characterized in that Feeding back the user behavior adjustment strategy to the user according to the analysis result includes: comparing the analysis result with the cluster storage result to determine the user behavior adjustment strategy; Optionally, the user behavior adjustment strategy includes: at least one of a resource allocation strategy, a job scheduling strategy and a network security strategy.

5. The method according to claim 4, characterized in that Further including: The user provides feedback on the user behavior adjustment strategy.

6. The method according to any one of claims 2 to 5, characterized in that: The historical behavior data includes: user information, bioinformatics analysis workflow, and analysis workflow operation information; Optionally, the bioinformatics analysis workflow includes: at least one of data quality control, alignment to a reference genome or transcriptome, expression calculation, differential expression analysis, gene function enrichment analysis, alternative splicing analysis, assembly and gene prediction, functional annotation, and community comparison and functional difference analysis; Optionally, the analysis workflow running information includes: analysis time corresponding to the bioinformatics analysis workflow.

7. The method according to claim 6, characterized in that The analysis result includes: at least one of a user behavior pattern and a HPC resource usage habit.

8. A login node management system for HPC, characterized in that: include; Behavior data collection module, used to collect historical behavior data of users on the HPC side; The behavior analysis and strategy feedback module is used to analyze the historical behavior data and provide the user with feedback on the user behavior adjustment strategy based on the analysis results.

9. A computing device, characterized in that include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program to implement the HPC login node management method according to any one of claims 2 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions or programs. When the computer instructions or programs are executed on a computer, the HPC login node management method according to any one of claims 2 to 7 is executed.

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