Functional coverage rate data compression and storage method, system and device and storage medium

Abstract

The invention relates to the technical field of electronic design automation, in particular to a function coverage rate data compression and storage method, system and device and a storage medium. Collecting the coverage rate of each coverage point or cross coverage and the hit times of each bin subordinate to the coverage point or the cross coverage; acquiring a coverage rate grade and a corresponding data structure; dynamically selecting a target data structure according to the target coverage rate grade of the coverage rate of the target coverage point or the cross coverage; operating a corresponding target data structure through a dynamic storage selector according to the target data structure, wherein the target data structure comprises a flag bit of the current data structure and storage structure data; wherein the storage structure data comprises at least one of the serial number of the bin and the hit frequency of the bin, and the storage overhead is remarkably reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic design automation, in particular to a functional coverage data compression storage method, system, device and storage medium. BACKGROUND

[0002] In the chip verification process, the functional coverage is an important indicator to measure the completeness of verification, and its data is usually organized and counted in the form of a complete bin set formed by cover points and cross coverages. In the prior art, the storage of functional coverage data generally uses a unified data structure and a fixed length data type to simplify data management and access logic. However, in a complex design scenario, a single cover point may contain tens of thousands of bins or even more, and the cross coverage between multiple cover points will cause the total number of bins to grow exponentially, thereby generating a large-scale coverage data entity. Each bin needs to independently record its identification information (such as index) and hit count metadata, and as the bin size expands rapidly, the amount of related data increases significantly. In order to ensure that the numbering space is sufficient and to avoid numerical overflow problems caused by a large number of bins, the existing scheme usually uses a fixed width data type such as uint64_t to store the type ID and hit count of the bin, that is, each field occupies 8 bytes. Although this method ensures the integrity and consistency of data representation and access, in the case where the actual index value or ID value of a large number of bins is much smaller than the maximum value that can be represented by 64 bits, high-bit redundant bits are generally present, causing serious storage space waste. Excessive memory overhead not only increases resource consumption during simulation, but also significantly reduces simulation efficiency, and poses a serious challenge to the storage, transmission and subsequent convergence analysis of coverage data. The fixed format storage of functional coverage data currently results in a large amount of storage space waste, making it difficult to meet the needs of efficient and compact data storage in large-scale verification scenarios. Therefore, there is an urgent need for a data compression storage method that reduces storage space waste. SUMMARY

[0003] In view of the above technical problems, the technical scheme adopted by the present application is as follows: a functional coverage data compression storage method, the method comprising the following steps: S100, based on the hierarchical structure of the functional coverage, traversing each cover point or cross coverage in the same level, collecting the hit count of each bin therein, and calculating the coverage of each cover point and cross coverage according to the hit count.

[0004] S200, obtaining the coverage level and its corresponding data structure.

[0005] S300, dynamically selecting a target data structure according to a target coverage rate level to which the target coverage point or the cross-coverage coverage rate falls.

[0006] S400, operating a corresponding target data structure by a dynamic storage selector according to the target data structure, the target data structure including a flag bit of a current data structure and storage structure data; wherein the storage structure data includes at least one of a bin number and a hit number thereof.

[0007] In addition, the application further provides a functional coverage rate data compression storage system, which includes: a unified data access interface, which receives a storage or reading request for functional coverage rate data sent by an upper application; a dynamic storage selector, which responds to the request, acquires a target distribution type according to a current object coverage rate, dynamically selects a target storage adapter from a plurality of storage adapters based on the target distribution type, and forwards the request to the selected target storage adapter; and a plurality of storage adapters, each of which is connected with and operates a type of data structure to complete the storage or reading of the functional coverage rate data.

[0008] In addition, the application further provides a non-transitory computer readable storage medium, which stores at least one instruction or at least one program, the at least one instruction or the at least one program is loaded and executed by a processor to implement the above method.

[0009] In addition, the application further provides an electronic device, which includes a processor and the above non-transitory computer readable storage medium.

[0010] The application has at least the following beneficial effects: The embodiment of the application provides a functional coverage rate data compression storage method, system, device and storage medium, which dynamically selects a data structure according to a level to which a coverage point or cross-coverage coverage rate falls, discards a traditional single data structure and data type for storing bin coverage, and dynamically adjusts a data structure according to different coverage rates, fully utilizes the characteristics of the coverage rate, realizes coverage rate data compression, reduces storage space, and significantly reduces storage overhead, so as to achieve the purpose of storing related bin data by using an optimal data structure. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0012] Figure 1 A functional coverage data compression storage method flow chart is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0014] Unless otherwise defined, all technical and scientific terms used in the embodiments of the present application have the same meanings as commonly understood by a person of ordinary skill in the art.

[0015] Please refer to Figure 1 which shows a functional coverage data compression storage method, the method comprising the following steps: S100, based on the hierarchy of functional coverage, traversing each cover point or cross coverage in the same level, collecting the hit times of each bin therein, and calculating the coverage of each cover point and cross coverage according to the hit times.

[0016] Among them, the cover point (Coverpoint) and the cross coverage (Cross) are the core structures of the functional coverage model, which are used to count the hit of the value of the variable (or expression) and its combination. Both of them contain one or more bins as the basic unit to record the hit times. Specifically, in the functional coverage model, the cover point (Coverpoint) and the cross coverage (Cross) are the core coverage constructs, which are used to define and count the hit of the specific value or value combination. Among them, the cover point (Coverpoint): the basic statistical unit for dividing and sampling the value space of a single variable or expression. It monitors the occurrence times of the target value or value interval by defining a set of bins. Among them, the cross coverage (Cross): the statistical unit for joint sampling of the value combination of two or more cover points. It automatically generates cross bins by calculating the Cartesian product of the values of each cover point, in order to monitor the occurrence of specific scenarios or state combinations. Among them, each cover point or cross coverage contains one or more bins as the basic container for finally recording the specific hit times.

[0017] Among them, in the coverage collection process, bin is a pre-defined container for classifying and counting the values or states of the sampling points, and the calculation of the coverage directly depends on whether each bin is hit by the sampling value.

[0018] The hierarchy of the function coverage can be abstractly represented by a tree model, and the nodes thereof are as follows: a root node (Root Node), an intermediate node (Intermediate Node), a child node (Child Node), and a leaf node (Leaf Node). The root node (Root Node) represents the entire function coverage model or a top-level covergroup instance set. It is the logical starting point of all coverage data. The intermediate node (Intermediate Node) is acted by a covergroup instance. The covergroup is a unit for encapsulating the coverage behavior of a specific interface, module, or function point. The child node (Child Node) is a coverpoint and a cross defined inside the covergroup. The coverpoint samples the value of a single variable or expression, and the cross monitors the combination of the values of multiple coverpoints. The leaf node (Leaf Node) is a bin, which is the smallest unit of coverage statistics. Each bin defines one or more values or value levels of a coverpoint or a cross. The hit count of the bin directly reflects the number of times the corresponding design state is triggered by the test stimulus.

[0019] The coverage rate of the coverpoint or the cross coverage is the percentage of the number of bins that have been hit in the coverpoint or the cross coverage to the total number of all defined bins under the coverpoint or the cross coverage, and is used to measure the completeness of the verification.

[0020] The hit count of the bin refers to the cumulative count triggered when the value of the sampled signal or variable meets the value condition or combination condition defined by the bin during simulation. This value is the most basic atomic data unit in the function coverage statistics.

[0021] In an embodiment, the step of collecting the coverage rate and the hit count of all bins thereunder includes: traversing the hierarchy, identifying all covergroup instances; obtaining all coverpoints or cross coverages under each covergroup; independently performing the following steps for each coverpoint or cross coverage: reading the coverage rate of the current coverpoint or cross coverage, traversing the bins under the current coverpoint or cross coverage, and collecting the original hit count of all bins. The collected coverage rate and hit count of the bins are organized into an intermediate data structure according to the coverpoint or cross coverage and the hierarchical relationship, for subsequent compressed storage.

[0022] S200, obtaining the coverage rate level and the corresponding data structure.

[0023] Wherein, the distribution type can be directly determined according to the coverage level, and different data structures are selected for compression storage according to the different distribution types.

[0024] Wherein, the object to be processed for this compression is indicated in the data structure, which can be a data category to be stored selected according to the distribution characteristics of the bin, including: bin number, hit number, continuous index number range and single-bit flag. Wherein, the continuous index number range includes the start index and the end index, or the start index and the length. Wherein, the single-bit flag is used to mark the complete coverage. The application realizes the optimized use of storage resources by configuring different target information types for different distribution types. As an example, the target information is "bin number", "bin number and hit number", "hit number", or "only record the identifier of complete coverage".

[0025] Wherein, the data structure is used to indicate the organization form of the compressed data, such as hash table, grayscale map, block compression or single-bit identifier, etc.

[0026] In an embodiment, the coverage rate is divided into 4 coverage levels from low to high, the coverage rate in the 1st coverage level is low, which is a sparse matrix, and most of the bins are not covered; therefore, the data of the covered bins is stored by hash table, and the number of the bin is the key and the hit number is the value in the recorded key-value pair. The coverage rate in the 2nd coverage level is moderate, at this time a certain proportion of bins are covered, and a certain continuous area is covered; therefore, the bin data in the continuous area is recorded by block compression, and the information of the discrete bins outside the continuous area is recorded by combining hash table or grayscale map. The coverage rate in the 3rd coverage level is large, at this time most of the bins are covered; therefore, only the bin number of the uncovered bins is recorded by hash table due to the coverage rate; if the difference of the covered bins is greater than the preset difference threshold, the grayscale map can be selected for separate storage. The 4th coverage level is 100%, which is complete coverage, and the state of complete coverage is marked by 1 bit.

[0027] It should be noted that different data structures are dynamically selected for compression storage according to the coverage level, and the coverage rate data is efficiently organized and compressed, so that the bin data of different distribution types can adopt the optimal storage scheme.

[0028] It should be noted that other configuration methods between the distribution type, the coverage level and the data structure also fall within the protection scope of the application.

[0029] S300, dynamically selecting a target data structure according to the target coverage rate level to which the target coverage point or the intersection coverage rate falls.

[0030] It should be noted that the embodiment of the present application dynamically adjusts the data structure according to the distribution type of different coverage situations to compress the huge bin data and reduce the occupied storage space.

[0031] S400, operating the corresponding target data structure according to the target data structure through a dynamic storage selector, the target data structure including a flag bit of a current data structure and storage structure data; wherein the storage structure data includes at least one of a number of bins and a hit frequency.

[0032] In an embodiment, in S400, the data structure for storing the number of bins includes an object type flag bit, a storage level flag bit of the number, and an index number of the layer where the object is located; the data structure for storing the hit frequency includes a data length level flag bit and the hit frequency.

[0033] The object type includes coverage groups, coverage points, cross coverage, and bins. Each object corresponds to a storage level of a number.

[0034] The storage level refers to a preset storage capacity level for the number of a certain object type. Each storage level corresponds to a fixed bit length and is marked by a binary number. The storage level of the number of different object types can be different. For example, the storage level of the number of bins is divided into four levels and is represented by two bits, wherein the first storage level corresponds to a bin number range of 0 to 1023, occupying 10 bits of storage space; the second storage level corresponds to a bin number range of 1024 to 16383, occupying 14 bits of storage space; the third storage level corresponds to a bin number range of 16384 to 262143, occupying 18 bits of storage space; and the fourth storage level corresponds to a bin number range of 262144 to 1048575, occupying 20 bits of storage space. By introducing the storage level mechanism, the optimal storage bit number can be dynamically selected according to the actual range of the number, avoiding the space waste caused by uniform high bit width, and significantly improving the storage efficiency.

[0035] The index number is the index of the same type of object at the same level.

[0036] It should be noted that the object type flag bit and the storage level flag bit of the number are represented by corresponding bits, respectively. For example, when there are a total of four objects, two bits are used to represent the four objects. When the storage level of the number of bins is divided into four levels, two bits are used to represent the four level bits. When the storage level of the number of coverage points is divided into two levels, one bit is used to represent the two levels.

[0037] As an example, assume there are four types of objects: covergroup, coverpoint, crosscover, and bin. The covergroup, crosscover, and bin numbers are stored at four levels, while the coverpoint numbers are stored at two levels. The type flags for the four objects are: 00 for covergroup, 01 for coverpoint, 10 for crosscover, and 11 for bin. The storage level flags for the four levels are: 00 for the first level, 01 for the second level, 10 for the third level, and 11 for the fourth level. The storage level flags for the two levels are: 0 for the first level and 1 for the second level. So, when a bin is numbered "11000101101000," the top two bits ("11") indicate it is a bin, the third and fourth bits ("00") indicate it is the first level, and "0101101000" is the index of the current bin within the same hierarchy. When a cover point is numbered "0101001", the top two bits "01" indicate that the object is a cover point, the third high bit "0" indicates that the storage level of the number is the first level, and "1001" is the index of the same type of object in the level where the current cover point is located.

[0038] It should be noted that the most appropriate data type is dynamically selected to store the number according to the size of the number, so as to achieve the purpose of eliminating redundant space.

[0039] The data length level refers to the level of storage capacity preset for the number of hits. Each data length level corresponds to a fixed bit length and is marked by a binary number. For different sizes of hits, the number of bits required varies, so the appropriate data length level can be dynamically selected. As an example, assume that the data length level is divided into 4 levels. 00 represents the first data length level, occupying 1 bit of storage space; 01 represents the second data length level, occupying 4 bits of storage space; 10 represents the third data length level, occupying 16 bits of storage space; 11 represents the fourth data length level, occupying 27 bits of storage space.

[0040] In one embodiment, in S400, when all bins under the target coverage point or cross coverage are not hit, the hit count for each bin is not stored. This approach can further save space overhead.

[0041] In an embodiment, in S400, when the hit times of all bins under the target coverage point or the intersecting coverage are the same, the hit times are not stored separately for each bin, but are associated with the coverage point or the intersecting coverage; if all coverage points and intersecting coverages in the same level are associated with the same hit times, the hit times are promoted and associated with the common parent node of all coverage points and intersecting coverages in the same level; and the aggregation is performed layer by layer upwards until the hit times are no longer the same or the root node is reached. It should be noted that this method takes advantage of the repetition and level consistency that may exist in the coverage rate data, and significantly reduces redundant storage by aggregating upwards.

[0042] In an embodiment, in S400, when the hit times of multiple consecutive index numbered bins under the target coverage point are the same, the consecutive index number range of the bins is obtained, and the bins in the consecutive index number range no longer store the hit times separately, but the index information of the consecutive index number range and the common hit times thereof are associated with the first bin of the target range. Range aggregation storage of consecutive index and same hit time bins is also called block compression, which is a very efficient and practical local deduplication compression technology that combines spatial continuity and value consistency in two dimensions to achieve precise redundancy elimination.

[0043] In an embodiment, in S400, when the number of high-frequency bins under the target coverage point is greater than a preset number threshold of bins, the high-frequency bins are bins with hit times greater than a preset number threshold, and each hit time corresponding to all high-frequency bins is stored in a compression dictionary; wherein the compression dictionary is established by the following steps: S441, obtaining the hit times of high-frequency bins to determine a set of all non-repeating hit times; S442, determining an encoding bit width M according to the number N of types of the set of hit times, wherein M is the smallest integer satisfying 2 M ≥ N; S443, assigning a unique M-bit binary code to each hit time; S444, establishing a mapping relationship between the hit times and the M-bit binary codes to form an encoding dictionary.

[0044] It should be noted that for hit times with large values and repeated hit times, only one copy needs to be defined in the dictionary table, and binary codes are used instead of original hit times when associating hit times with bins.

[0045] As an example, if there are four large hit counts, 100000, 150000, 180000, and 200000, and the number of bins containing these hit counts is greater than the preset number threshold of bins, for example, the preset number threshold of bins is 100 bins. The total number of bits required to occupy is: (2+7) x 100 x 4 = 11600. Where 2 represents the level type mark, and 27 represents the 27-bit bits required for the fourth to store the hit count. When the dictionary table scheme is used, a dictionary table is created according to the hit counts that can appear: binary code 00 represents 100000, binary code 01 represents 150000, binary code 10 represents 180000, and binary code 11 represents 200000. After using the dictionary table, the total number of bits required to occupy is: 2 x 100 x 4 = 800.

[0046] It should be noted that by dynamically constructing a compression dictionary of hit counts, efficient encoding of high-frequency count values is achieved. It fully utilizes the aggregation characteristics of hit counts in coverage data, significantly reducing the storage overhead of high-frequency regions.

[0047] In an embodiment, the objects in the numbered compressed storage format are coverage groups, cross-coverage groups, or coverage points in the functional coverage data.

[0048] It should be noted that by selecting a suitable data structure according to the coverage, the number of bins can be reduced; and by designing the composition of the data structure, the data storage of each bin can be further reduced. Through these two dimensions, the data storage is comprehensively reduced, thereby reducing the storage space occupation and achieving the purpose of data compression.

[0049] In summary, the embodiment of the present application provides a functional coverage data compression storage method, which dynamically selects a data structure according to the level of the coverage point or cross-coverage coverage. It discards the traditional single data structure and data type for storing the coverage of bins, but dynamically adjusts the data structure according to different coverage. It fully utilizes the characteristics of the coverage, realizes the compression of the coverage data, reduces the storage space, and significantly reduces the storage overhead, thereby achieving the purpose of storing the relevant bin data using the optimal data structure.

[0050] In order to support any other data structure that can be used to store bins, the present application provides embodiment two.

[0051] Embodiment two A functional coverage data compression storage system, characterized in that the system comprises: a unified data access interface, a dynamic storage selector, and a plurality of storage adapters.

[0052] Further, the unified data access interface receives a storage or read request for the functional coverage data sent by the upper application.

[0053] Further, the dynamic storage selector responds to the request, and acquires a target data structure according to the coverage of the current object, dynamically selects a target storage adapter from the plurality of storage adapters based on the target data structure, and forwards the request to the selected target storage adapter.

[0054] The dynamic storage selector selects a suitable storage structure to store the bin data according to the distribution type of the coverage and the hit frequency of the current object, and appends a corresponding storage structure flag bit before each data block to identify the storage mode adopted by the current data block, so as to facilitate the dynamic storage selector to call the corresponding storage adapter to perform the read and write operation of the data.

[0055] It should be noted that the object and the data structure in embodiment one are also applicable to embodiment two.

[0056] Further, each of the plurality of storage adapters connects and operates one type of data structure to complete the storage or read of the functional coverage data.

[0057] In an implementation, the data structure for storing data includes a storage structure flag bit, a data block length, and storage structure data. As an example, assuming that there are four types of data structures, for the four types of data structures: the storage structure flag bit 00 represents a sparse storage structure, the storage structure flag bit 01 represents a moderate storage structure, the storage structure flag bit 10 represents a dense storage structure, and the storage structure flag bit 11 represents a full-coverage storage structure. It should be noted that when the data file is a binary file, the data structure for storing data includes a storage structure flag bit and storage structure data, and does not need to separately add a data block length, because the data block length is automatically added when the binary file is generated, and does not need to be manually added again. The read and write operation of the data can be performed by the corresponding adapter through the preposed storage structure flag bit. The number of bits of the storage structure flag bit depends on the number of storage adapters that can be supported by the current system. If there is a new bin storage structure, only the corresponding storage adapter needs to be implemented to support the read and write operation of the structure, and the existing outer access API does not need to be modified, so that the function extension can be realized.

[0058] The storage adapter connects and operates one type of data structure, which is the data structure in embodiment one, and will not be described again.

[0059] It should be noted that the storage method implemented by the system when executed is the method provided in embodiment one, and will not be described again.

[0060] The embodiment of the present application further provides a non-transitory computer readable storage medium, which can be arranged in an electronic device to store at least one instruction or at least one program for implementing a method related to the method in the method embodiment, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided by the above embodiment.

[0061] The embodiment of the present application further provides an electronic device, comprising a processor and the aforementioned non-transitory computer readable storage medium.

[0062] The embodiment of the present application further provides a computer program product, which comprises program codes, and when the program product is run on an electronic device, the program codes are used to make the electronic device execute the steps in the method according to various exemplary embodiments of the present application described in the specification.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions.

[0064] Although some specific embodiments of the present application have been described in detail by examples, those skilled in the art should understand that the above examples are only for illustration, but not for limiting the scope of the present application. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present application. The scope of the present application is defined by the appended claims.

Claims

1. A functional coverage data compression storage method, characterized by, The method comprises the following steps: S100, based on the hierarchy of the functional coverage, traversing each coverage point and cross coverage in the same level, collecting the hit number of each bin, and calculating the coverage of each coverage point and cross coverage according to the hit number; S200, obtaining the coverage level and the corresponding data structure; S300, dynamically selecting the target data structure according to the target coverage level to which the coverage of the target coverage point or cross coverage falls; S400, operating the corresponding target data structure through a dynamic storage selector according to the target data structure, wherein the target data structure comprises a flag bit of the current data structure and storage structure data; The storage structure data comprises at least one of the bin number and the hit number.

2. The method of claim 1, wherein, In S400, the data structure for storing the bin number comprises an object type flag bit, a storage level flag bit of the number, and an index number of the layer where the object is located; the data structure for storing the hit number comprises a data length level flag bit and the hit number.

3. The method of claim 1, wherein, In S400, when all bins under the target coverage point or cross coverage are not hit, the hit number of each bin is not stored.

4. The method of claim 1, wherein, In S400, when the hit numbers of all bins under the target coverage point or cross coverage are the same, the hit number of each bin is not stored individually, but the hit number is associated with the coverage point or cross coverage; if all coverage points and cross coverages in the same level are associated with the same hit number, the hit number is promoted and associated with the common parent node of all coverage points and cross coverages in the same level; in this way, the hit number is aggregated layer by layer upwards until the hit number is no longer the same or the root node is reached.

5. The method of claim 1, wherein, In S400, when the hit numbers of multiple bins with consecutive index numbers under the target coverage point or cross coverage are the same, the consecutive index number range of the bins is obtained, and the bins in the consecutive index number range no longer store the hit number individually, but the consecutive index number range information and the common hit number are associated with the first bin of the target level.

6. The method of claim 1, wherein, In S400, when the number of high-frequency bins under the target coverage point or cross coverage is greater than a preset number threshold of bins, the high-frequency bins are bins with a hit number greater than a preset number threshold, and each hit number corresponding to all high-frequency bins is stored in a compression dictionary; the establishment of the compression dictionary comprises the following steps: S441, obtaining the hit number of the high-frequency bin, and determining a set of all non-repeated hit numbers; S442, determining an encoding bit width M according to the category number N of the hit number set, wherein M is the minimum integer satisfying 2 M ≥ N. S443, assigning a unique M-bit binary code to each hit number; S444, establishing a mapping relationship between the hit number and the M-bit binary code to form a coding dictionary.

7. The method of claim 1, wherein, The object in the compressed storage format of the number is a coverage group, a coverage point, or a cross coverage in the functional coverage data.

8. A functional coverage data compression storage system, characterized by, The system comprises: a unified data access interface configured to receive a storage or reading request for the functional coverage data sent by an upper application; a dynamic storage selector, responsive to the request, and according to a coverage of the current object, obtaining a target data structure, and based on the target data structure, dynamically selecting a target storage adapter from a plurality of storage adapters, and forwarding the request to the selected target storage adapter; a plurality of storage adapters, each storage adapter connected to and operating on a type of data structure to accomplish the storage or reading of the functional coverage data. 9.A non-transitory computer-readable storage medium having stored therein at least one instruction or at least one piece of program, characterized in that, The at least one instruction or the at least one program is loaded and executed by the processor to implement the method of any one of claims 1-7.

10. An electronic device, comprising: A non-transitory computer readable storage medium as claimed in claim 9, and a processor.

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