Parallel simulation regression method and system based on cluster server

Abstract

The invention relates to the technical field of simulation regression systems, and provides a parallel simulation regression method and system based on cluster servers. The method comprises the following steps: classifying simulation tasks according to task types, and determining a dependency relationship among a plurality of simulation tasks; generating a parallel simulation task list through a cluster server management tool according to the dependency relationship among the plurality of simulation tasks; and delivering the parallel simulation task list to a cluster server, and running simulation tasks by the cluster server to generate a simulation result. The parallel simulation regression system is conveniently constructed by utilizing the scheduling function of the cluster server, the dependency relationship of various types of tasks can be processed, the degree of parallelism of the simulation regression system is increased to the maximum extent, and the method has universality and can be widely applied to different scenes.

Description

Technical Field

[0001] The present invention generally relates to the technical field of simulation regression systems. Specifically, the present invention relates to a parallel simulation regression method and system based on a cluster server. Background Art

[0002] During the chip development process, simulation regression of chip design code is the main means of testing code quality and the core system of chip front-end verification. Through simulation regression results, potential chip design problems can be discovered, so that problems can be located and repaired in a timely manner to avoid irreparable losses after chip tape-out.

[0003] Traditionally, simulation regression systems are based on cluster servers. The simulation tasks are submitted to the cluster servers, which execute the tasks and write the results back to generate simulation results. Given the limited cluster server resources, how to quickly complete the simulation regression tasks, thereby accelerating chip development iterations and reducing chip R&D cycles is the main challenge of the simulation regression system.

[0004] Some common simulation regression systems currently only allow one type of task to be executed at a time, and simulation tasks of multiple types need to be executed separately. In addition, some simulation regression systems, although they can support the execution of multiple types of tasks, require the development of complex intermediate scripts for task scheduling, which increases the difficulty of development and makes it difficult to perfectly solve the problem of task parallel scheduling, thus reducing the execution efficiency of the simulation regression system. Summary of the invention

[0005] The applicant has found through research that due to the diversity of chip design code simulation tasks, it is difficult for conventional simulation regression systems to perfectly handle the parallelism and dependencies of simulation tasks. In the prior art, one regression is usually used to execute one type of task, or complex task scheduling scripts need to be developed, which increases the difficulty of project development, reduces the execution efficiency of the simulation regression system, and is not conducive to the rapid iteration of chip project development.

[0006] In order to at least partially solve the above problems in the prior art, the present invention proposes a parallel simulation regression method for a cluster server, comprising:

[0007] Classify simulation tasks according to task types and determine the dependencies between multiple simulation tasks;

[0008] generating a parallel simulation task list through a cluster server management tool according to the dependencies between the plurality of simulation tasks; and

[0009] The parallel simulation task list is delivered to a cluster server, and the cluster server runs the simulation tasks to generate simulation results.

[0010] In one embodiment of the present invention, it is provided that classifying the simulation tasks according to the task types includes: classifying the simulation tasks according to the simulation objects and simulation stages of the simulation tasks.

[0011] In one embodiment of the present invention, it is provided that the simulation object includes: chip design code, chip environment code and library file.

[0012] In one embodiment of the present invention, it is provided that, according to the dependency relationship between the plurality of simulation tasks, generating a parallel simulation task list by a cluster server management tool includes:

[0013] Enables different simulation tasks of the simulation object to run in parallel; and

[0014] The simulation tasks with the same simulation object are shared during the compilation phase, and the simulation phases are run in parallel.

[0015] In one embodiment of the present invention, generating a parallel simulation task list by a cluster server management tool includes:

[0016] Submit simulation tasks to cluster servers in parallel through cluster server management tools; and

[0017] Set the execution conditions of the simulation task through the cluster server management tool.

[0018] In one embodiment of the present invention, it is provided that the cluster server-based parallel simulation regression method further includes:

[0019] A regression result analysis is performed based on the simulation results, and a regression result report is output.

[0020] In one embodiment of the present invention, it is provided that the regression result report includes: regression start time, simulation time, number of simulation tasks, number of simulation passed tasks, number of simulation failed tasks, number of simulation invalid tasks and regression pass rate.

[0021] The present invention also proposes a parallel simulation regression system based on a cluster server, comprising:

[0022] A simulation task classification module, which is configured to classify simulation tasks according to task types;

[0023] A dependency combing module, configured to determine the dependency between multiple simulation tasks;

[0024] A task list generating module, which is configured to generate a simulation task list according to the dependency relationship between the plurality of simulation tasks, wherein the simulation tasks are arranged by a cluster server management tool;

[0025] A task delivery module, configured to deliver the simulation tasks in the simulation task list to the cluster server in parallel;

[0026] A cluster server configured to run simulation tasks in parallel and generate simulation result files;

[0027] A regression result analysis module, configured to analyze the simulation result file and generate a simulation report; and

[0028] A regression report module is configured to perform statistical output on the regression results according to the simulation report.

[0029] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps according to the method when executed by a processor.

[0030] The present invention also provides a computer system, comprising:

[0031] a processor configured to execute machine-executable instructions; and

[0032] A memory having machine executable instructions stored thereon, wherein the machine executable instructions, when executed by a processor, perform the steps according to the method

[0033] In one embodiment of the present invention, it is provided that

[0034] The present invention has at least the following beneficial effects: the present invention utilizes the scheduling function of the cluster server itself to conveniently construct a parallel simulation regression system, which can handle the dependencies of various types of tasks, thereby maximizing the parallelism of the simulation regression system, and has versatility and can be widely used in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To further illustrate the advantages and features of various embodiments of the present invention, a more detailed description of various embodiments of the present invention will be presented with reference to the accompanying drawings. It will be understood that these drawings only depict typical embodiments of the present invention and are therefore not to be considered as limiting the scope thereof. In the accompanying drawings, for clarity, identical or corresponding parts will be represented by identical or similar reference numerals.

[0036] Figure 1 A schematic diagram of a computer system implementing the system and / or method according to the present invention is shown.

[0037] Figure 2 A schematic flow chart of a parallel simulation regression method based on a cluster server in one embodiment of the present invention is shown.

[0038] Figure 3A schematic diagram of a simulation object in one embodiment of the present invention is shown.

[0039] Figure 4 A schematic diagram of the simulation stage in one embodiment of the present invention is shown.

[0040] Figure 5 A schematic diagram of a simulation task in an embodiment of the present invention is shown.

[0041] Figure 6 A schematic diagram showing an example of running a simulation task in an embodiment of the present invention is shown.

[0042] Figure 7 A module schematic diagram of a cluster server-based parallel simulation regression system in one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0043] It should be noted that the components in the figures may be shown exaggeratedly for the sake of illustration and are not necessarily true to scale. In the figures, identical or functionally identical components are provided with the same reference numerals.

[0044] In the present invention, unless otherwise specified, "arranged on...", "arranged above..." and "arranged above..." do not exclude the existence of an intermediate between the two. In addition, "arranged on or above..." merely indicates the relative positional relationship between two components, and in certain cases, such as after reversing the product direction, it can also be converted into "arranged below or below...", and vice versa.

[0045] In the present invention, each embodiment is only intended to illustrate the aspects of the present invention and should not be construed as limiting.

[0046] In the present invention, unless otherwise specified, the quantifiers "a", "an" and "an" do not exclude the presence of a plurality of elements.

[0047] It should also be noted that in the embodiments of the present invention, for the sake of clarity and simplicity, only a portion of the parts or components may be shown, but those of ordinary skill in the art will understand that under the teachings of the present invention, the required parts or components may be added according to the needs of the specific scenario. In addition, unless otherwise specified, the features in different embodiments of the present invention may be combined with each other. For example, a feature in the second embodiment may be used to replace a corresponding or functionally identical or similar feature in the first embodiment, and the resulting embodiment also falls within the disclosure scope or recorded scope of the present application.

[0048] It should also be noted that within the scope of the present invention, the terms "same", "equal", "equal to" and the like do not mean that the values ​​of the two are absolutely equal, but allow a certain reasonable error, that is, the terms also cover "substantially the same", "substantially equal", "substantially equal to". By analogy, in the present invention, the terms "perpendicular to", "parallel to" and the like indicating directions also cover the meanings of "substantially perpendicular to" and "substantially parallel to".

[0049] In the present application, the term "configuration" refers to the setting of the shape, structure, material and / or function of the target object to achieve the desired technical effect, wherein "configuration" includes a variety of alternative technical means for achieving the technical effect, which become apparent under the teaching of the present application.

[0050] In addition, the numbering of the steps of the methods of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps can be executed in different orders.

[0051] The present invention will be further described below in conjunction with specific embodiments with reference to the accompanying drawings.

[0052] Figure 1 FIG. 1 shows a computer system 100 for implementing the system and / or method according to the present invention. Unless otherwise specified, the method and / or system according to the present invention may be implemented in Figure 1 The present invention can be implemented in the computer system 100 shown in the figure to achieve the purpose of the present invention, or the present invention can be implemented in a distributed manner in multiple computer systems 100 according to the present invention through a network, such as a local area network or the Internet. The computer system 100 of the present invention can include various types of computer systems, such as handheld devices, laptop computers, personal digital assistants (PDAs), multi-processor systems, microprocessor-based or programmable consumer electronic devices, network PCs, minicomputers, mainframes, network servers, tablet computers, etc.

[0053] like Figure 1 As shown, the computer system 100 includes a processor 111, a system bus 101, a system memory 102, a video adapter 105, an audio adapter 107, a hard disk drive interface 109, an optical drive interface 113, a network interface 114, and a universal serial bus (USB) interface 112. The system bus 101 can be any of several types of bus structures, such as a memory bus or a memory controller, a peripheral bus, and a local bus using various types of bus architectures. The system bus 101 is used for communication between various bus devices. In addition to Figure 1In addition to the bus devices or interfaces shown in , other bus devices or interfaces are also conceivable. System memory 102 includes read-only memory (ROM) 103 and random access memory (RAM) 104, wherein ROM 103 can, for example, store basic input / output system (BIOS) data for basic routines for implementing information transmission at startup, and RAM 104 is used to provide the system with a running memory with a faster access speed. Computer system 100 also includes a hard disk drive 109 for reading and writing hard disk 110, an optical drive interface 113 for reading and writing optical media such as CD-ROM, etc. Hard disk 110 can, for example, store an operating system and application programs. The drive and its associated computer-readable medium provide non-volatile storage of computer-readable instructions, data structures, program modules and other data for computer system 100. Computer system 100 can also include a video adapter 105 for image processing and / or image output, which is used to connect output devices such as display 106. The computer system 100 may further include an audio adapter 107 for audio processing and / or audio output, which is used to connect output devices such as speakers 108. In addition, the computer system 100 may further include a network interface 114 for network connection, wherein the network interface 114 may be connected to the Internet 116 through a network device such as a router 115, wherein the connection may be wired or wireless. In addition, the computer system 100 may further include a universal serial bus interface (USB) 112 for connecting peripheral devices, wherein the peripheral devices include, for example, a keyboard 117, a mouse 118, and other peripheral devices, such as a microphone, a camera, etc.

[0054] When the present invention Figure 1 When implemented on the computer system 100, the dependencies of various types of tasks can be processed, thereby maximizing the parallelism of the simulation regression system, and the system is versatile and can be widely used in different scenarios.

[0055] Furthermore, the embodiments may be provided as a computer program product that may include one or more machine-readable media having machine-executable instructions stored thereon, which, when executed by one or more machines such as a computer, a computer network or other electronic device, may cause the one or more machines to perform operations according to the embodiments of the present invention. The machine-readable medium may include, but is not limited to, a floppy disk, an optical disk, a CD-ROM (compact disk read-only memory) and a magneto-optical disk, a ROM (read-only memory), a RAM (random access memory), an EPROM (erasable programmable read-only memory), an EEPROM (electrically erasable programmable read-only memory), a magnetic or optical card, a flash memory, or other types of media / machine-readable media suitable for storing machine-executable instructions.

[0056] In addition, various embodiments may be downloaded as a computer program product, where the program may be transmitted from a remote computer (e.g., a server) to a requesting computer (e.g., a client) via a communication link (e.g., a modem and / or a network connection) using one or more data signals implemented and / or modulated by a carrier wave or other propagation medium. Thus, the machine-readable medium used herein may include such a carrier wave, but this is not required.

[0057] In the present invention, each module of the system according to the present invention can be implemented using software, hardware, firmware or a combination thereof. When the module is implemented using software, the function of the module can be implemented by a computer program flow, for example, the module can be implemented by a code segment (such as a code segment of a language such as C, C++) stored in a storage device (such as a hard disk, a memory, etc.), wherein the corresponding function of the module can be implemented when the code segment is executed by a processor. When the module is implemented using hardware, the function of the module can be implemented by setting a corresponding hardware structure, for example, the function of the module can be implemented by hardware programming a programmable device such as a field programmable gate array (FPGA), or the function of the module can be implemented by designing an application-specific integrated circuit (ASIC) including electronic devices such as a plurality of transistors, resistors and capacitors. When the module is implemented using firmware, the function of the module can be written into a read-only memory such as an EPROM or EEPROM of the device in the form of a program code, and the corresponding function of the module can be implemented when the program code is executed by a processor. In addition, some functions of the module may need to be implemented by separate hardware or by collaboration with the hardware, for example, the detection function is implemented by corresponding sensors (such as proximity sensors, acceleration sensors, gyroscopes, etc.), the signal transmission function is implemented by corresponding communication devices (such as Bluetooth devices, infrared communication devices, baseband communication devices, Wi-Fi communication devices, etc.), the output function is implemented by corresponding output devices (such as displays, speakers, etc.), and so on.

[0058] Figure 2 FIG. 2 shows a schematic flow chart of a parallel simulation regression method based on a cluster server in one embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0059] Step 201: Classify simulation tasks according to task types, and determine dependencies between multiple simulation tasks.

[0060] Step 202: Generate a parallel simulation task list through a cluster server management tool according to the dependency relationship between the multiple simulation tasks.

[0061] Step 203: delivering the parallel simulation task list to a cluster server, and having the cluster server run the simulation tasks to generate simulation results.

[0062] Step 204: perform regression result analysis based on the simulation results, and output a regression result report.

[0063] The following describes in detail each step of the method.

[0064] In step 201, simulation tasks are classified according to task types, and dependencies between multiple simulation tasks are determined. Simulation task classification and dependency analysis are very important tasks. Generally, analysis is performed from two dimensions: simulation object and simulation stage.

[0065] Figure 3 FIG. 1 is a schematic diagram showing a simulation object in one embodiment of the present invention. Figure 3 As shown in the figure, the simulation object refers to the content contained in the simulation task, which is generally composed of chip design code, chip environment code and library files. Due to the verification level and the diversity of verification objects, the simulation object is often not single. In addition, even for the same chip design code, in order to improve the verification efficiency, the code is often replaced with an empty shell to generate multiple simulation objects. There is no dependency between simulation tasks of different simulation objects, and they can run in parallel in regression. The dependency between different simulation tasks of the same simulation object needs to be considered in combination with the simulation stage.

[0066] Figure 4 FIG. 2 shows a schematic diagram of the simulation stage in one embodiment of the present invention. Figure 4 As shown in the figure, the simulation stage is related to the specific simulation software. Generally speaking, common simulation software will divide the simulation process into several interdependent steps. Taking the simulation tool VCS as an example, the simulation process can be divided into two or three steps according to the configuration. After the simulation is started, the previous simulation stage needs to be completed before the subsequent simulation stage can be started.

[0067] Figure 5 FIG. 1 is a schematic diagram showing a simulation task in one embodiment of the present invention. Figure 5 As shown, in order to simplify the explanation, taking the two-step method as an example, the compilation phase (i.e., the first simulation step) of multiple simulation tasks corresponding to the same simulation object is shared. One regression only needs to start the compilation phase of one simulation task, and other simulation tasks wait for the completion of the compilation phase before proceeding to the subsequent phase. The simulation phase (i.e., the second simulation step) of multiple simulation tasks of the same simulation object can be run in parallel. When the previous compilation phase is completed, the simulation phases of all simulation tasks can run in parallel.

[0068] In step 202, a simulation task list is generated through a cluster server management tool according to the dependencies between the multiple simulation tasks. The tasks are arranged according to the sorted dependency information, and the parallelism of the simulation tasks needs to be fully utilized. Except for the simulation tasks belonging to the same simulation object, the simulation stage must wait for the compilation stage to be completed, and other tasks can be arranged to run in parallel. The following principles are mainly followed: simulation tasks of different simulation objects run in parallel; simulation tasks of the same simulation object are processed by stage, shared in the compilation stage, and run in parallel in the simulation stage.

[0069] Figure 6 FIG. 1 is a schematic diagram showing an example of running a simulation task in one embodiment of the present invention. Figure 6 As shown in the figure, this example includes three groups of simulation tasks of simulation objects. Simulation object A includes four simulation tasks, simulation object B includes two simulation tasks, and simulation object C includes three simulation tasks. Since the three groups of simulation tasks belong to different simulation objects, they are run in parallel. Figure 6 As shown in the figure, when the regression starts, all simulation tasks start to run the compilation phase at the same time. Within each group of simulation tasks, once the compilation phase is completed, all simulation tasks in the group run the simulation phase at the same time. This maximizes the use of parallelism and improves the efficiency of the regression system.

[0070] It is not easy to generate a task list. Figure 5 The ideal task running scheduling arrangement shown often requires the development of complex script files for processing, and is often error-prone and requires repeated debugging and iteration.

[0071] To solve this problem, the present invention generates and arranges the regression simulation task table through the functions of the cluster server management tool. Although different cluster server management tools are used differently, the functions and principles provided by various tools are basically the same, so the method is universal.

[0072] Take the LSF (Load Sharing Facility) tool as an example. It provides the functions of task submission and task dependency. Through the parallel task submission function, the simulation tasks to be run can be stored in a file in the form of a list. Using the file submission method and the package submission function, all tasks can be submitted to the cluster server at the same time. Through the task execution condition function, the conditions for starting the simulation task after it is submitted to the cluster server can be set, such as waiting for a task to end.

[0073] By using the above functions of the LSF tool, regression simulation tasks can be submitted in parallel and related dependencies can be processed, so as to easily form a suitable task list. No special process processing scripts or programs are required. The generated task list can be as follows:

[0074] -q job_q-J simulation task A1_compile

[0075] -q job_q-J simulation task A1_simulation -w″ended(simulation task A1_compile)″

[0076] -q job_q-J simulation task A2_simulation -w″ended(simulation task A1_compile)″

[0077] -q job_q-J simulation task A3_simulation -w″ended(simulation task A1_compile)″

[0078] -q job_q-J simulation task A4_simulation -w″ended(simulation task A1_compile)″

[0079] -q job_q-J simulation task B1_compile

[0080] -q job_q-J simulation task B1_simulation -w″ended(simulation task B1_compile)″

[0081] -q job_q-J simulation task B2_simulation -w″ended(simulation task B1_compile)″

[0082] -q job_q-J simulation task C1_compile

[0083] -q job_q-J simulation task C1_simulation -w″ended(simulation task C1_compile)″

[0084] -q job_q-J simulation task C2_simulation -w″ended(simulation task C1_compile)″

[0085] -q job_q-J simulation task C3_simulation -w″ended(simulation task C1_compile)″

[0086] In step 203, the simulation task list is delivered to the cluster server, and the cluster server runs the simulation task to generate simulation results. Taking the LSF tool as an example, it supports task packaging and delivery in a list manner. The task list can be submitted through the following command:

[0087] busb-pack simulation task list

[0088] In step 204, a regression result analysis is performed based on the simulation result, and a regression report is output. Based on the result file of the server simulation task running, the state of the simulation task execution, the type of error that occurred, the resources consumed and other data are analyzed, and a regression report file is output. The regression report usually contains information such as regression start time, simulation time, number of simulation tasks, number of simulation passed tasks, number of simulation failed tasks, number of simulation invalid tasks, regression pass rate and the like.

[0089] Figure 7 FIG. 1 shows a schematic diagram of a module of a parallel simulation regression system based on a cluster server in one embodiment of the present invention. Figure 7 As shown, the system includes a simulation task classification module 701, a dependency combing module 702, a task list generation module 703, a task delivery module 704, a cluster server 705, a regression result analysis module 706 and a regression report module 707.

[0090] In some embodiments, the simulation task classification module 701 is configured to classify simulation tasks according to task types, where the classification can be performed from two dimensions: simulation object and simulation stage.

[0091] In some embodiments, the dependency combing module 702 is configured to determine the dependencies between multiple simulation tasks, which is the key to parallel simulation. It is necessary to sort out the dependencies between different simulation tasks to increase the simulation parallelism as much as possible.

[0092] In some embodiments, the task list generation module 703 is configured to generate a simulation task list according to the dependency relationship between the multiple simulation tasks, wherein the tasks need to be arranged in conjunction with a simulation queue management tool.

[0093] In some embodiments, the task delivery module 704 is configured to deliver the simulation tasks in the simulation task list to the cluster server 705 .

[0094] In some embodiments, the cluster server 705 is configured to run simulation tasks and generate simulation result files.

[0095] In some embodiments, the regression result analysis module 706 is configured to analyze the simulation result file and generate a simulation report.

[0096] In some embodiments, the regression reporting module 707 is configured to perform statistical output of the regression results according to the simulation report.

[0097] The present invention can efficiently utilize the parallelism and dependency of regression simulation tasks, maximize the parallel operation of simulation tasks, greatly improve regression efficiency, and accelerate verification iteration. In addition, by utilizing the built-in functions of the cluster server management tool, a regression simulation task list can be conveniently generated and submitted, avoiding complex process script tool development and debugging, and greatly reducing the difficulty of system development. The present invention can be applied to various common simulation tools and cluster server management tools, and has good versatility.

[0098] Although various embodiments of the present invention are described above, it should be understood that they are presented as examples only and not as limitations. It is obvious to those skilled in the relevant art that various combinations, modifications and changes can be made thereto without departing from the spirit and scope of the present invention. Therefore, the breadth and scope of the present invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should only be defined according to the attached claims and their equivalents.

Claims

1. A parallel simulation regression method based on cluster servers, characterized in that: The following steps are involved: Classify simulation tasks according to task types and determine the dependencies between multiple simulation tasks; Generate a parallel simulation task list through a cluster server management tool according to the dependencies between the multiple simulation tasks; as well as The parallel simulation task list is delivered to a cluster server, and the cluster server runs the simulation tasks to generate simulation results.

2. The parallel simulation regression method based on cluster servers according to claim 1, characterized in that: Classifying simulation tasks according to task types includes: classifying simulation tasks according to simulation objects and simulation stages of the simulation tasks.

3. The parallel simulation regression method based on cluster servers according to claim 2 is characterized in that: The simulation object includes: chip design code, chip environment code and library file.

4. The parallel simulation regression method based on cluster servers according to claim 2 is characterized in that: Generating a parallel simulation task list by a cluster server management tool according to the dependencies between the multiple simulation tasks includes: Enables different simulation tasks of the simulation object to run in parallel; and The simulation tasks with the same simulation object are shared during the compilation phase, and the simulation phases are run in parallel.

5. The parallel simulation regression method based on cluster servers according to claim 1, characterized in that: Generate a parallel simulation task list through the cluster server management tool, including: Submit simulation tasks to cluster servers in parallel through cluster server management tools; and Set the execution conditions of the simulation task through the cluster server management tool.

6. The parallel simulation regression method based on cluster servers according to claim 1, characterized in that: Also includes: A regression result analysis is performed based on the simulation results, and a regression result report is output.

7. The cluster server-based parallel simulation regression method according to claim 1, characterized in that: The regression result report includes: regression start time, simulation time, number of simulation tasks, number of simulation passed tasks, number of simulation failed tasks, number of simulation invalid tasks and regression pass rate.

8. A parallel simulation regression system based on cluster servers, characterized in that: include: A simulation task classification module, which is configured to classify simulation tasks according to task types; A dependency combing module, configured to determine the dependency between multiple simulation tasks; A task list generating module, which is configured to generate a simulation task list according to the dependency relationship between the plurality of simulation tasks, wherein the simulation tasks are arranged by a cluster server management tool; A task delivery module, configured to deliver the simulation tasks in the simulation task list to the cluster server in parallel; A cluster server configured to run simulation tasks in parallel and generate simulation result files; A regression result analysis module, configured to analyze the simulation result file and generate a simulation report; as well as A regression report module is configured to perform statistical output on the regression results according to the simulation report.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by a processor, performs the steps of the method according to any one of claims 1 to 7.

10. A computer system, characterized in that: include: a processor configured to execute machine-executable instructions; as well as A memory having machine executable instructions stored thereon, wherein the machine executable instructions, when executed by a processor, perform the steps of the method according to any one of claims 1 to 7.

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