CN112882817A - Workflow processing method based on super computer - Google Patents
Workflow processing method based on super computer Download PDFInfo
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- CN112882817A CN112882817A CN202110311627.9A CN202110311627A CN112882817A CN 112882817 A CN112882817 A CN 112882817A CN 202110311627 A CN202110311627 A CN 202110311627A CN 112882817 A CN112882817 A CN 112882817A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/54—Interprogram communication
- G06F9/546—Message passing systems or structures, e.g. queues
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
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- G06F2209/547—Messaging middleware
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
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Abstract
The invention relates to a workflow processing method based on a super computer, which comprises the following steps: the method comprises the steps that a cloud server constructs a workflow component based on an instruction received by a user terminal browser, wherein the workflow component comprises a plurality of modules arranged according to a preset execution sequence; the cloud server sends the workflow component to a super computer; the super computer sequentially converts data of each module in the workflow components into instructions executable by the super computer and executes the instructions according to a preset execution sequence corresponding to the workflow components, and maps an execution result obtained after the whole workflow components are executed to the cloud server; and the cloud server sends the execution result to a browser of the user terminal for displaying. The invention directly constructs the workflow component by interacting with the browser and runs the workflow component on the super computer, thereby improving the interaction efficiency and the calculation efficiency, facilitating the cooperation of the front end and the back end and reducing the interaction cost.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a workflow processing method based on a super computer.
Background
With the continuous development of the information age, more and more practical processes are electronized, so that the rapid development of the workflow technology is promoted, and in the face of the development technology and means of various workflow management systems, developing a workflow component which meets the standard specification, has strong flexibility and is universal is meaningful work. The workflow component can greatly accelerate the development speed of the business system in the flow direction in the business system, enhance the stability of the whole system and bring quick function promotion to the business system. The supercomputer is a computer which is formed by combining a plurality of computing nodes and can perform large-scale computation or data processing in parallel, is also called as a parallel computer, is the computer with the strongest function, fastest operation and largest storage capacity at present, is mainly used for the national high-tech field and advanced technical research, and is an important embodiment of the national technological development level and comprehensive national strength. The execution efficiency of the supercomputer is high, but the supercomputer corresponds to a command line interactive interface, and only professionals can interact with the command line interactive interface. Therefore, based on the prior art, if the workflow components are operated on the supercomputer, professional persons are inevitably required to spend a great deal of time and energy to realize the workflow components, and the workflow components are high in cost and low in efficiency.
Disclosure of Invention
The invention aims to provide a workflow processing method based on a super computer, which directly constructs a workflow component through interaction with a browser and runs the workflow component on the super computer, so that the interaction efficiency and the calculation efficiency are improved, the cooperation of a front end and a back end is facilitated, and the interaction cost is reduced.
A supercomputer-based workflow processing method, comprising:
the method comprises the steps that a cloud server constructs a workflow component based on an instruction received by a user terminal browser, wherein the workflow component comprises a plurality of modules arranged according to a preset execution sequence;
the cloud server sends the workflow component to a super computer;
the super computer sequentially converts data of each module in the workflow components into instructions executable by the super computer and executes the instructions according to a preset execution sequence corresponding to the workflow components, and maps an execution result obtained after the whole workflow components are executed to the cloud server;
and the cloud server sends the execution result to a browser of the user terminal for displaying.
Further, the cloud server constructs a workflow component based on an instruction received by the user terminal browser, including:
building a module set and a flow chart set on the cloud server, wherein the module set comprises one or more of parameter ID information, a maximum value, a minimum value, parameter options and default parameters, and the flow chart set comprises a node set and a connection set;
and constructing a UI interaction layer on the browser, receiving an instruction through the UI interaction layer, and acquiring data corresponding to the instruction in a module set and a flow chart set from the cloud server based on the instruction to construct a workflow component.
Further, the UI interaction layer includes a module pool and a modeling area, the receiving of the instruction by the UI interaction layer, and the obtaining of data corresponding to the instruction in the module set and the flowchart set from the cloud server based on the instruction to construct the workflow component include:
receiving a module dragging instruction, dragging a corresponding module in the module pool to a modeling area, and correspondingly updating service flow data;
receiving a module dragging instruction, automatically generating a connecting line from one module to another module, and recording information of a source point and a destination point of the connection;
and repeatedly executing the steps according to the preset execution sequence until the workflow component is constructed.
Further, the UI interaction layer further includes a parameter area, and the method further includes: receiving a module display instruction, and displaying the corresponding parameters of the module in the parameter area; and/or receiving a parameter modification instruction, modifying the corresponding parameter in the parameter area, and synchronously storing the parameter to the workflow component.
Further, the cloud server is connected with the super computer through the message queue forwarding server, and the message queue forwarding server is used for sending the workflow component to the super computer.
Further, the super computer includes a login node, a computation cluster and a storage cluster, and the super computer sequentially converts data of each module in the workflow component into a super computer executable instruction and executes the instruction according to a preset execution sequence corresponding to the workflow component, and maps an execution result obtained after the workflow component is executed to the cloud server, including:
the message queue forwarding server sends the workflow component to a login node;
the login node sequentially converts the received data of each module in the workflow assembly into command line instructions executable by the super computer according to the preset execution sequence and sends the command line instructions to the computing cluster;
the computing cluster is used for executing the command line instruction and storing an execution result obtained after the whole workflow component is executed into the storage cluster;
the cloud server is connected with the storage cluster and used for acquiring the execution result from the computing cluster.
Further, an interface program is arranged between the cloud server and the super computer, and the interface program is used for converting data of each module in the workflow component into command line instructions which can be executed by the super computer.
Further, the interface program includes a first interface program installed at the cloud server and a second interface program installed at the login node, wherein,
the first interface program is used for converting the data of each module in the workflow assembly into a target data structure and then forwarding the target data structure to the message queue forwarding server;
the second program interface is used for converting the target data structure into command line instructions which can be executed by the super computer, and the target data structure can be serialized and deserialized.
Further, the login node is specifically configured to convert the target data structure into a script form specified by the supercomputer, package the script form into a command line instruction that can be recognized by the supercomputer, analyze a target parameter from the command line instruction, and send the target parameter to a corresponding computing resource in the computing cluster according to the command line instruction for execution.
Further, the super computer mounts the execution result to the cloud server in a form of a network disk.
Compared with the prior art, the invention has obvious advantages and beneficial effects. By means of the technical scheme, the workflow processing method based on the super computer can achieve considerable technical progress and practicability, has industrial wide utilization value, and at least has the following advantages:
the invention directly constructs the workflow component by interacting with the browser and runs the workflow component on the super computer, thereby improving the interaction efficiency and the calculation efficiency, facilitating the cooperation of the front end and the back end and reducing the interaction cost. And the user can directly interact with the super computer through the browser without mastering the use rule of the corresponding super computer, and the super computer executes the operation, so that the user experience is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic diagram of a workflow processing method based on a supercomputer according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given to a specific implementation and effects of a workflow processing method based on a supercomputer according to the present invention with reference to the accompanying drawings and preferred embodiments.
An embodiment of the present invention provides a workflow processing method based on a supercomputer, as shown in fig. 1, including the following steps:
step S1, the cloud server constructs a workflow component based on an instruction received by a user terminal browser, wherein the workflow component comprises a plurality of modules arranged according to a preset execution sequence;
step S2, the cloud server sends the workflow component to a super computer;
step S3, the super computer sequentially converts the data of each module in the workflow assembly into instructions executable by the super computer and executes the instructions according to the preset execution sequence corresponding to the workflow assembly, and maps the execution result obtained after the whole workflow assembly is executed to the cloud server;
and step S4, the cloud server sends the execution result to the browser of the user terminal for display.
As an example, the step S1 includes:
step S11, building a module set and a flow chart set on the cloud server, wherein the module set comprises one or more of parameter ID information, a maximum value, a minimum value, parameter options and default parameters, and the flow chart set comprises a node set and a connection set;
step S12, a UI interaction layer is built on the browser, instructions are received through the UI interaction layer, and workflow components are built by acquiring data corresponding to the instructions in a module set and a flow chart set from the cloud server based on the instructions.
As an example, the UI interaction layer includes a module pool and a modeling area, the module pool includes a plurality of packaged modules, and data or programs corresponding to each module are stored on a cloud server. Step S12 further includes:
step S121, receiving a module dragging instruction, dragging a corresponding module in the module pool to a modeling area, and correspondingly updating service flow data;
step S122, receiving a module dragging instruction, automatically generating a connecting line from one module to another module, and recording information of a source point and a destination point of the connection;
and S123, repeatedly executing the steps according to the preset execution sequence until the workflow component is built.
As an example, the UI interaction layer further includes a parameter area, and the method further includes step S124, receiving a module display instruction, and displaying a parameter corresponding to the module in the parameter area; and/or step S125, receiving a parameter modification instruction, modifying the corresponding parameter in the parameter area, and synchronously storing the parameter to the workflow component.
As an example, the cloud server is connected to the super computer through the message queue forwarding server, and the message queue forwarding server is configured to send the workflow component to the super computer.
In order to ensure the security of the super computer, the message queue forwarding server is further configured to filter the received instruction in the workflow component according to a preset rule and send the filtered instruction to the super computer. As an example, an instruction mapper and an instruction filtering list are configured in the message queue forwarding server, where a preset filtering instruction is stored in the instruction filtering list; the instruction mapper is used for mapping the workflow component into an instruction to be filtered, the instruction filter is used for comparing the instruction to be filtered with the filtering list, if the instruction to be filtered is stored in the filtering list, the instruction to be filtered is not forwarded, otherwise, the instruction to be filtered is forwarded to the super computer. The message queue forwarding server is arranged between the cloud server and the super computer, so that the direct interconnection of the cloud server and the login node can be avoided, and through instruction filtering operation, instructions for deleting and changing important parameter instructions and other instructions which possibly affect the safety of the super computer can be filtered, so that misoperation can be avoided, and the safety of the system is improved.
The supercomputer includes a login node, a computation cluster, and a storage cluster, and the step S3 includes:
step S31, the message queue forwarding server sends the workflow component to a login node;
step S32, the login node converts the received data of each module in the workflow assembly into command line instructions executable by the super computer in sequence according to the preset execution sequence and sends the command line instructions to the computing cluster;
the computing cluster is used for executing the command line instruction and storing an execution result obtained after the whole workflow component is executed into the storage cluster;
the cloud server is connected with the storage cluster and used for acquiring the execution result from the computing cluster.
As an example, an interface program is provided between the cloud server and the super computer, and the interface program is used for converting data of each module in the workflow component into command line instructions which can be executed by the super computer.
The interface programs comprise a first interface program installed on the cloud server and a second interface program installed on the login node, wherein the first interface program is used for converting data of each module in the workflow component into a target data structure and then forwarding the target data structure to the message queue forwarding server; the second program interface is used for converting the target data structure into a command line instruction which can be executed by the super computer, the target data structure can be serialized and deserialized to facilitate analysis and generation of the instruction, the target data structure can be in a json format, an XML format and the like, can also be a self-defined data structure, and only needs to realize serialization and deserialization.
As an example, the login node is specifically configured to convert the target data structure into a script form specified by the supercomputer, package the script form into a command line instruction that can be recognized by the supercomputer, analyze a target parameter from the command line instruction, and send the target parameter to a corresponding computing resource in the computing cluster according to the command line instruction for execution. Specifically, the method can be implemented by executing a four-level scheduling through a login node, and specifically includes: the service layer obtains a target data structure, the scheduling layer converts the target data structure into a script form specified by the super computer, the packaging layer packages the script converted by the scheduling layer into a command line instruction which can be identified by the super computer, and the base layer analyzes a target parameter from the command line instruction and sends the target parameter to a corresponding computing resource in the computing cluster for execution.
As an example, the supercomputer may mount the execution result to the cloud server in a form of a network disk, so as to achieve mutual access of data of the supercomputer storage cluster and the cloud server, and directly map the result onto the cloud server. The mounting process can be specifically realized by techniques such as Samba (information service block), NFS (Network File System), JumpServer, SSHFS, and the like.
The embodiment of the invention directly constructs the workflow component by interacting with the browser and runs the workflow component on the super computer, thereby improving the interaction efficiency and the calculation efficiency, facilitating the cooperation of the front end and the back end and reducing the interaction cost. And the user can directly interact with the super computer through the browser without mastering the use rule of the corresponding super computer, and the super computer executes the operation, so that the user experience is improved.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A workflow processing method based on a super computer is characterized by comprising the following steps:
the method comprises the steps that a cloud server constructs a workflow component based on an instruction received by a user terminal browser, wherein the workflow component comprises a plurality of modules arranged according to a preset execution sequence;
the cloud server sends the workflow component to a super computer;
the super computer sequentially converts data of each module in the workflow components into instructions executable by the super computer and executes the instructions according to a preset execution sequence corresponding to the workflow components, and maps an execution result obtained after the whole workflow components are executed to the cloud server;
and the cloud server sends the execution result to a browser of the user terminal for displaying.
2. The supercomputer-based workflow processing method of claim 1,
the cloud server constructs a workflow component based on an instruction received by a user terminal browser, and the method comprises the following steps:
building a module set and a flow chart set on the cloud server, wherein the module set comprises one or more of parameter ID information, a maximum value, a minimum value, parameter options and default parameters, and the flow chart set comprises a node set and a connection set;
and constructing a UI interaction layer on the browser, receiving an instruction through the UI interaction layer, and acquiring data corresponding to the instruction in a module set and a flow chart set from the cloud server based on the instruction to construct a workflow component.
3. The supercomputer-based workflow processing method of claim 2,
the UI interaction layer comprises a module pool and a modeling area, the UI interaction layer receives an instruction, and a workflow component is constructed by acquiring data corresponding to the instruction in a module set and a flow chart set from the cloud server based on the instruction, and the method comprises the following steps:
receiving a module dragging instruction, dragging a corresponding module in the module pool to a modeling area, and correspondingly updating service flow data;
receiving a module dragging instruction, automatically generating a connecting line from one module to another module, and recording information of a source point and a destination point of the connection;
and repeatedly executing the steps according to the preset execution sequence until the workflow component is constructed.
4. The supercomputer-based workflow processing method of claim 2,
the UI interaction layer further includes a parameter area, and the method further includes: receiving a module display instruction, and displaying the corresponding parameters of the module in the parameter area; and/or receiving a parameter modification instruction, modifying the corresponding parameter in the parameter area, and synchronously storing the parameter to the workflow component.
5. The supercomputer-based workflow processing method of claim 1,
the cloud server is connected with the super computer through the message queue forwarding server, and the message queue forwarding server is used for sending the workflow component to the super computer.
6. The supercomputer-based workflow processing method of claim 5,
the super computer includes login node, calculation cluster and storage cluster, the super computer according to the corresponding predetermined execution order of workflow subassembly, in proper order with the data conversion of each module in the workflow subassembly is super computer executable instruction and execution, and with whole the execution result that obtains after the workflow subassembly execution is accomplished maps to on the cloud ware, include:
the message queue forwarding server sends the workflow component to a login node;
the login node sequentially converts the received data of each module in the workflow assembly into command line instructions executable by the super computer according to the preset execution sequence and sends the command line instructions to the computing cluster;
the computing cluster is used for executing the command line instruction and storing an execution result obtained after the whole workflow component is executed into the storage cluster;
the cloud server is connected with the storage cluster and used for acquiring the execution result from the computing cluster.
7. The supercomputer-based workflow processing method of claim 6,
an interface program is arranged between the cloud server and the super computer and used for converting data of each module in the workflow assembly into command line instructions capable of being executed by the super computer.
8. The supercomputer-based workflow processing method of claim 7,
the interface program includes a first interface program installed at the cloud server and a second interface program installed at the login node, wherein,
the first interface program is used for converting the data of each module in the workflow assembly into a target data structure and then forwarding the target data structure to the message queue forwarding server;
the second program interface is used for converting the target data structure into command line instructions which can be executed by the super computer, and the target data structure can be serialized and deserialized.
9. The supercomputer-based workflow processing method of claim 6,
the login node is specifically used for converting the target data structure into a script form designated by the super computer, then packaging the script form into a command line instruction which can be identified by the super computer, then analyzing a target parameter from the command line instruction, and sending the target parameter to a corresponding computing resource in the computing cluster according to the command line instruction for execution.
10. The supercomputer-based workflow processing method of claim 1,
and the super computer mounts the execution result to the cloud server in a network disk form.
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