Ecological maturity scoring method and device for RISC-V basic software

Abstract

The embodiment of the invention relates to the technical field of basic software ecological maturity measurement, and provides an ecological maturity scoring method and device for RISC-V basic software, and the method comprises the steps: installing and operating to-be-tested RISC-V basic software on a first target hardware platform and a second target hardware platform respectively; based on the operation data of the to-be-tested RISC-V basic software on the first target hardware platform and the second target hardware platform, calculating maturity scores of multiple dimensions; and based on the maturity score of each dimension, calculating to obtain an ecological maturity score of the to-be-tested RISC-V basic software. Therefore, the ecological development level of the RISC-V basic software can be quantitatively measured, and comprehensiveness, expandability and operability are achieved.

Description

Technical Field

[0001] This invention relates to the field of basic software ecosystem maturity measurement technology, and in particular to a method and apparatus for scoring the ecosystem maturity of RISC-V basic software. Background Technology

[0002] RISC-V is an open instruction set architecture (ISA) that is gradually being applied in various fields such as high-performance computing, data centers, and embedded systems due to its openness, scalability, and low cost. However, the RISC-V software ecosystem is still under development and lags significantly behind mature architectures such as ARM in terms of performance optimization, compilation adaptation, feature support, open-source activity, and heterogeneous computing support.

[0003] Existing RISC-V software ecosystem evaluation methods mostly focus on single performance metrics, lacking a multi-dimensional, quantifiable, and cross-sectionally comparable evaluation system, making it difficult to comprehensively reflect the maturity of the ecosystem. Furthermore, the lack of a systematic comparison method with mainstream mature architectures (such as ARM) also limits the objectivity and operability of the evaluation results.

[0004] Therefore, there is an urgent need for a RISC-V basic software ecosystem maturity assessment method that is based on multi-dimensional indicators, quantifiable and calculable, and comparable to reference architectures, in order to comprehensively, objectively, and scalably measure the development level of the RISC-V software ecosystem. Summary of the Invention

[0005] This invention provides a method and apparatus for scoring the ecological maturity of RISC-V basic software, which addresses the shortcomings of existing technologies that only provide one-sided and qualitative assessments of RISC-V software and lack quantitative, comparable, and multi-dimensional maturity benchmarks. It enables a quantitative measurement of the development level of the RISC-V basic software ecosystem and has comprehensiveness, scalability, and operability.

[0006] This invention provides a method for scoring the ecosystem maturity of RISC-V basic software, comprising: The RISC-V basic software to be tested was installed and run on the first target hardware platform and the second target hardware platform, respectively. Based on the running data of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, maturity scores in multiple dimensions are calculated. Based on the maturity score of each dimension, the ecosystem maturity score of the RISC-V basic software to be tested is calculated.

[0007] In one possible implementation, the method further includes: The multiple dimensions include performance advancement, compilation quality, and memory advancement; Obtain the speedup ratio of the task execution wall time for multiple test cases on the first target hardware platform and the second target hardware platform, and calculate the first geometric mean. The performance advancement score is obtained by weighting the first geometric mean with the first standardized weighting coefficient. Obtain the speedup ratio of the number of CPU instructions for the multiple test cases on the first target hardware platform and the second target hardware platform, and calculate the second geometric mean. The compilation quality score is obtained by weighting the second geometric mean with the second standardized weighting coefficient. Obtain the speedup ratio of cache read misses for the multiple test cases on the first target hardware platform and the second target hardware platform, and calculate the third geometric mean. The memory advancement score is obtained by weighting the calculation based on the third geometric mean and the third standardized weighting coefficient.

[0008] In one possible implementation, the method further includes: The multiple dimensions also include functional completeness, open-source development activity, and heterogeneous computing support; Obtain the pass rate of the same test case on the first target hardware platform and the second target hardware platform respectively; The ratio of execution pass rate is calculated and weighted by the fourth standardized weighting coefficient to obtain the functional integrity score; Obtain the number of patches for the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, respectively; The ratio of the number of patches is calculated and weighted by the fifth standardized weighting coefficient to obtain the open source development activity score. Obtain the number of heterogeneous computing supports of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, respectively; The ratio of heterogeneous computing support is calculated, and then weighted using the sixth standardized weighting coefficient to obtain the heterogeneous computing support score.

[0009] In one possible implementation, the method further includes: The performance advancement score is calculated using the first formula, which is: ; in, The first standardized weighting coefficient, It is the first geometric mean; The compilation quality score is calculated using a second formula, which is: ; in, The second standardized weighting coefficient, It is the second geometric mean; The memory advancement score is calculated using a third formula, which is: ; in, The third standardized weighting coefficient, It is the third geometric mean.

[0010] In one possible implementation, the method further includes: The functional integrity score is calculated using a fourth formula, which is: ; in, The fourth standardized weighting coefficient, This represents the pass rate of the same test case on the second target hardware platform. This indicates the pass rate of the same test case on the first target hardware platform; The open-source development activity score is calculated using the fifth formula, which is: ; in, The fifth standardized weighting coefficient, This indicates the number of patches for the RISC-V base software under test on the second target hardware platform. This indicates the number of patches for the RISC-V base software under test on the first target hardware platform; The heterogeneous computing support score is calculated using a sixth formula, which is: ; in, The sixth standardized weighting coefficient. This indicates the number of heterogeneous computing supports of the RISC-V basic software under test on the second target hardware platform. This indicates the number of heterogeneous computing supports of the RISC-V base software to be tested on the first target hardware platform.

[0011] In one possible implementation, the method further includes: Normalize the maturity scores for each dimension and draw a spider diagram; The total ecosystem maturity score of the RISC-V base software to be tested is determined based on the spider diagram. The total ecosystem maturity score is used to compare multiple base software horizontally or to track the ecosystem progress of the same software in different version cycles vertically.

[0012] This invention also provides an ecosystem maturity scoring device for RISC-V basic software, comprising the following modules: The installation module is used to install and run the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, respectively. The calculation module is used to calculate maturity scores in multiple dimensions based on the running data of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform. The scoring module is used to calculate the ecosystem maturity score of the RISC-V basic software to be tested based on the maturity score of each dimension.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the RISC-V basic software ecosystem maturity scoring method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the RISC-V basic software ecosystem maturity scoring method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the RISC-V basic software ecosystem maturity scoring method as described above.

[0016] The present invention provides a method and apparatus for scoring the ecosystem maturity of RISC-V basic software. This involves installing and running the RISC-V basic software to be tested on a first target hardware platform and a second target hardware platform, respectively. Based on the running data of the RISC-V basic software to be tested on the first and second target hardware platforms, maturity scores across multiple dimensions are calculated. Based on the maturity scores of each dimension, an ecosystem maturity score for the RISC-V basic software to be tested is calculated. Compared to existing technologies that only provide a one-sided and qualitative assessment of RISC-V software, lacking quantitative, comparable, and multi-dimensional maturity benchmarks, this solution provides a quantifiable measure of the RISC-V basic software ecosystem development level, possessing comprehensiveness, scalability, and operability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts illustrating the RISC-V basic software ecosystem maturity scoring method provided by this invention.

[0019] Figure 2 This is the second flowchart of the RISC-V basic software ecosystem maturity scoring method provided by this invention.

[0020] Figure 3 This is a spider diagram showing the ecological maturity of PyTorch, the basic software provided in this invention.

[0021] Figure 4 This is a schematic diagram of the ecological maturity scoring device for RISC-V basic software provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0025] Figure 1 This is one of the flowcharts illustrating the RISC-V basic software ecosystem maturity scoring method provided by this invention, such as... Figure 1 As shown, the method includes the following: S11. Install and run the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform respectively.

[0026] The embodiments of the present invention are combined with Figure 2 The second flowchart illustrating the RISC-V basic software ecosystem maturity scoring method will be explained in detail.

[0027] First, the RISC-V architecture was chosen as the primary target hardware platform, and the ARM architecture as the secondary target hardware platform, as these two architectures exhibit similar performance. The primary target hardware platform serves to run the RISC-V base software under test and obtain actual operational data; the secondary target hardware platform serves as a benchmark reference platform for comparative evaluation with the RISC-V platform. By comparing the performance of the same RISC-V base software (such as PyTorch) on both RISC-V and ARM hardware platforms, the gap between the RISC-V ecosystem and the mature ARM ecosystem is quantitatively assessed, resulting in a quantifiable maturity score.

[0028] Install the operating system and the RISC-V basic software to be tested on the RISC-V hardware platform and the ARM hardware platform, respectively.

[0029] S12. Based on the running data of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, calculate the maturity score in multiple dimensions.

[0030] The ARM architecture is designated as the baseline architecture, and RISC-V is used as the target architecture for the maturity measurement of the underlying software in six dimensions.

[0031] The six dimensions include: Performance Advancement (PA), Compilation Quality (CQ), Memory Performance (MP), Functional Integrity (FI), Open-source Development Activity (ODA), and Heterogeneous Computing Support (HCS).

[0032] Performance Advancement (PA) evaluates the degree of computational performance optimization of the underlying software on the RISC-V platform. Each framework has... Each test case. For any test case... Based on the duration of the task execution. The overall speedup ratio is obtained as follows: The geometric mean of the speedup ratio for all test cases is: In this embodiment of the invention, the PA dimension is assigned a standardized weight coefficient. This coefficient contributes proportionally to the overall maturity score, as shown in the following formula: For example, if the average acceleration ratio is 100, then the PA score is If the average acceleration ratio is 10, then the PA score is: If the average acceleration ratio is 1, then the PA score is: .

[0033] Compilation Quality (CQ) evaluates the build stability, compilation success rate, and compatibility of the underlying software on the RISC-V platform. For any test case... Based on the number of instructions executed by the CPU. The acceleration ratio is obtained as follows: The geometric mean of the ratio of the number of instructions in all test cases is: In this embodiment of the invention, the CQ dimension is assigned a standardized weight coefficient. This coefficient contributes proportionally to the overall maturity score, as shown in the formula: The storage performance (MP) evaluation software assesses memory usage, resource scheduling efficiency, and other parameters on the RISC-V platform. For any test case... Based on the number of cache read misses (L1-dcache-load-misses) The acceleration ratio is obtained as follows: The geometric mean of the cache read miss ratio for all test cases is: In this embodiment of the invention, the MP dimension is assigned a standardized weight coefficient. This coefficient contributes proportionally to the overall maturity score, as shown in the formula: Functional Integrity (FI) assesses whether the main functional modules and system call support for the RISC-V platform are fully implemented. and These represent the pass rates for executing test cases on the ARM and RISC-V platforms, respectively, with values ​​ranging from [value range missing]. The FI dimension is assigned a standardized weight coefficient. This coefficient contributes proportionally to the overall maturity score, as shown in the formula: Open-source development activity (ODA) reflects the health and sustainability of the RISC-V open-source ecosystem, based on indicators such as code update frequency, community participation, and the number of contributors. and These represent the cumulative number of patches for the ARM and RISC-V frameworks in the open-source communities of the underlying software, respectively. The ODA dimension is assigned a standardized weighting coefficient. This coefficient contributes proportionally to the overall maturity score, as shown in the formula: Heterogeneous Computing Support (HCS) assesses the RISC-V underlying software ecosystem's ability to evaluate hardware acceleration support for GPUs, NPUs, and Vector Extensions (such as RVV). and These represent the heterogeneous support rate of the test software on ARM and RISC-V platforms, respectively. The HCS dimension is assigned a standardized weight coefficient. This coefficient contributes proportionally to the overall maturity score, as shown in the formula: S13. Based on the maturity score of each dimension, calculate the ecosystem maturity score of the RISC-V basic software to be tested.

[0034] Based on the scores across the six dimensions above, a score for the maturity of the basic software ecosystem is obtained. : The scores of the above six dimensions are normalized to obtain a spider diagram of the maturity score of the basic software ecosystem.

[0035] The following section uses the basic software PyTorch to evaluate the maturity score of the RISC-V ecosystem.

[0036] As shown in Table 1, SOPHON SG and Kunpeng 920-4826 were selected as the hardware platforms for the RISC-V framework and ARM framework, respectively, and the operating system was openEuler 2403 LTS.

[0037] Table 1 CPU SOPHON SG2042 Kunpeng 920 processor XuanTie C920 TaiShan V110 Instruction set architecture RV64GCV ARMv8.2 CPU core count 64 64 CPU clock speed 2GHz 2GHz L1 I-cache 64KB 64KB L1 D-cache 64KB 64KB L2 cache 1MB shared by 4 cores 2MB shared by each of the 4 cores L3 cache 128MB 128MB operating system openEuler 24.03 openEuler 24.03 The test cases used by PyTorch are sourced from: https: / / github.com / pytorch / examples, with a total of 103 test cases.

[0038] Performance advancement, compilation quality, and memory efficiency were evaluated using the Perf tool on the SOPHON SG2042 and Kunpeng 920-4826 platforms.

[0039] 1) Regarding performance advancement, taking the BERT test case as an example, its speedup ratio is: Geometric mean of time speedup ratio on the wall for all 103 test cases: Performance Advancement (PA) Score: 2) Regarding compilation quality, taking BERT test cases as an example, its speedup ratio is: The geometric mean of the CPU instruction execution speedup ratio for all 103 test cases is: Compilation Quality (CQ) Score: 3) Regarding memory speedup, taking the BERT test case as an example, its speedup ratio is: The average speedup ratio for cache misses across all 103 test cases is: Memory Advancement (MP) Score: 4) Regarding functional integrity, the total number of test cases is 103, and the number of test cases for the ARM platform (Kunpeng 920-4826) is 87, representing a percentage of: The RISC-V platform (SOPHON SG2042) supports a total of 84 test cases, representing a percentage of: Functional Integrity (FI) Score: 5) Regarding the activity of open source development, the PyTorch open source project address is: https: / / github.com / pytorch.

[0040] The total number of submissions for the ARM architecture (as of June 30, 2025) is 1887. The total number of submissions for the RISC-V architecture (as of June 30, 2025) is: 4 Open Source Development Activity (ODA) Score: 6) Regarding heterogeneous computing support, the support levels are shown in Table 2 below. The ARM platform supports 4 heterogeneous computing hardware items, with a support rate of 100%; the RISC-V platform supports 3 heterogeneous computing hardware items, with a support rate of 75%, as shown in Table 2. Table 2

[0041] Heterogeneous Computing Support (HCS) Score: In summary, the overall score for the maturity of PyTorch's RISC-V ecosystem is as follows: The PyTorch ecosystem maturity spider diagram is as follows: Figure 3 As shown.

[0042] The RISC-V basic software ecosystem maturity scoring method provided by this invention involves installing and running the RISC-V basic software to be tested on a first target hardware platform and a second target hardware platform, respectively; calculating maturity scores across multiple dimensions based on the running data of the RISC-V basic software to be tested on the first and second target hardware platforms; and calculating the ecosystem maturity score of the RISC-V basic software to be tested based on the maturity score of each dimension. Compared with the shortcomings of existing technologies that only provide a one-sided and qualitative evaluation of RISC-V software and lack quantitative, comparable, and multi-dimensional maturity scales, this method enables a quantifiable measurement of the RISC-V basic software ecosystem development level, and has comprehensiveness, scalability, and operability.

[0043] The following describes the RISC-V basic software ecological maturity scoring device provided by the present invention. The RISC-V basic software ecological maturity scoring device described below and the RISC-V basic software ecological maturity scoring method described above can be referred to in correspondence.

[0044] Figure 4 This is a schematic diagram of the ecological maturity scoring device for RISC-V basic software provided by the present invention, specifically including: Installation module 401 is used to install and run the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, respectively. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0045] The calculation module 402 is used to calculate maturity scores across multiple dimensions based on the runtime data of the RISC-V basic software under test on the first and second target hardware platforms. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0046] The scoring module 403 is used to calculate the ecosystem maturity score of the RISC-V basic software to be tested based on the maturity score of each dimension. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0047] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute an ecosystem maturity scoring method for RISC-V basic software. This method includes: installing and running the RISC-V basic software to be tested on a first target hardware platform and a second target hardware platform, respectively; calculating maturity scores for multiple dimensions based on the running data of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform; and calculating the ecosystem maturity score of the RISC-V basic software to be tested based on the maturity score for each dimension.

[0048] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0049] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the RISC-V basic software ecosystem maturity scoring method provided by the above methods. The method includes: installing and running the RISC-V basic software to be tested on a first target hardware platform and a second target hardware platform, respectively; calculating maturity scores for multiple dimensions based on the running data of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform; and calculating the ecosystem maturity score of the RISC-V basic software to be tested based on the maturity score for each dimension.

[0050] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an ecosystem maturity scoring method for RISC-V basic software provided by the methods described above. This method includes: installing and running the RISC-V basic software to be tested on a first target hardware platform and a second target hardware platform, respectively; calculating maturity scores for multiple dimensions based on the running data of the RISC-V basic software to be tested on the first and second target hardware platforms; and calculating the ecosystem maturity score of the RISC-V basic software to be tested based on the maturity scores for each dimension.

[0051] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0052] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for scoring the ecological maturity of RISC-V basic software, characterized in that, include: The RISC-V basic software to be tested was installed and run on the first target hardware platform and the second target hardware platform, respectively. Based on the running data of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, maturity scores in multiple dimensions are calculated. Based on the maturity score of each dimension, the ecosystem maturity score of the RISC-V basic software to be tested is calculated.

2. The method according to claim 1, characterized in that, The multiple dimensions include performance advancement, compilation quality, and memory advancement; Based on the runtime data of the RISC-V basic software under test on the first and second target hardware platforms, a maturity score is calculated across multiple dimensions, including: Obtain the speedup ratio of the task execution wall time for multiple test cases on the first target hardware platform and the second target hardware platform, and calculate the first geometric mean. The performance advancement score is obtained by weighting the first geometric mean with the first standardized weighting coefficient. Obtain the speedup ratio of the number of CPU instructions for the multiple test cases on the first target hardware platform and the second target hardware platform, and calculate the second geometric mean. The compilation quality score is obtained by weighting the second geometric mean with the second standardized weighting coefficient. Obtain the speedup ratio of cache read misses for the multiple test cases on the first target hardware platform and the second target hardware platform, and calculate the third geometric mean. The memory advancement score is obtained by weighting the calculation based on the third geometric mean and the third standardized weighting coefficient.

3. The method according to claim 1, characterized in that, The multiple dimensions also include functional completeness, open-source development activity, and heterogeneous computing support; The calculation of maturity scores across multiple dimensions based on the runtime data of the RISC-V basic software under test on the first and second target hardware platforms also includes: Obtain the pass rate of the same test case on the first target hardware platform and the second target hardware platform respectively; The ratio of execution pass rate is calculated and weighted by the fourth standardized weighting coefficient to obtain the functional integrity score; Obtain the number of patches for the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, respectively; The ratio of the number of patches is calculated and weighted by the fifth standardized weighting coefficient to obtain the open source development activity score. Obtain the number of heterogeneous computing supports of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, respectively; The ratio of heterogeneous computing support is calculated, and then weighted using the sixth standardized weighting coefficient to obtain the heterogeneous computing support score.

4. The method according to claim 2, characterized in that, The method further includes: The performance advancement score is calculated using the first formula, which is: ； in, The first standardized weighting coefficient, It is the first geometric mean; The compilation quality score is calculated using a second formula, which is: ； in, The second standardized weighting coefficient, It is the second geometric mean; The memory advancement score is calculated using a third formula, which is: ； in, The third standardized weighting coefficient, It is the third geometric mean.

5. The method according to claim 3, characterized in that, The method further includes: The functional integrity score is calculated using a fourth formula, which is: ； in, The fourth standardized weighting coefficient. This represents the pass rate of the same test case on the second target hardware platform. This indicates the pass rate of the same test case on the first target hardware platform; The open-source development activity score is calculated using the fifth formula, which is: ； in, The fifth standardized weighting coefficient, This indicates the number of patches for the RISC-V base software under test on the second target hardware platform. This indicates the number of patches for the RISC-V base software under test on the first target hardware platform; The heterogeneous computing support score is calculated using a sixth formula, which is: ； in, The sixth standardized weighting coefficient, This indicates the number of heterogeneous computing supports of the RISC-V basic software under test on the second target hardware platform. This indicates the number of heterogeneous computing supports of the RISC-V base software to be tested on the first target hardware platform.

6. The method according to any one of claims 1-5, characterized in that, The ecosystem maturity score of the RISC-V basic software under test is calculated based on the maturity score of each dimension, including: Normalize the maturity scores for each dimension and draw a spider diagram; The total ecosystem maturity score of the RISC-V base software to be tested is determined based on the spider diagram. The total ecosystem maturity score is used to compare multiple base software horizontally or to track the ecosystem progress of the same software in different version cycles vertically.

7. A RISC-V basic software ecosystem maturity scoring device, characterized in that, include: The installation module is used to install and run the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform, respectively. The calculation module is used to calculate maturity scores in multiple dimensions based on the running data of the RISC-V basic software to be tested on the first target hardware platform and the second target hardware platform. The scoring module is used to calculate the ecosystem maturity score of the RISC-V basic software to be tested based on the maturity score of each dimension.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the RISC-V basic software ecological maturity scoring method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the RISC-V basic software ecological maturity scoring method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the RISC-V basic software ecological maturity scoring method as described in any one of claims 1 to 6.

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