CN116089181A - Automatic testing method for GPU systemization - Google Patents
Automatic testing method for GPU systemization Download PDFInfo
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Abstract
The invention is applicable to the technical field of GPU testing, and provides an automatic testing method for GPU systemization, which comprises the following steps: step S1, setting a plurality of test dimensions, and constructing a corresponding test algorithm for each test dimension; step S2, after the GPU to be tested is initialized, executing a corresponding test algorithm according to different test dimensions, recording the test process of each test dimension in real time, and simultaneously storing the test result, and correlating the test result of the same GPU to be tested; and S3, comparing and displaying the test results of the GPUs with different models in a visual mode. The method is applicable to all types of GPUs, and by designing a plurality of test dimensions and constructing a corresponding test algorithm for each test dimension, the automatic test of each dimension is realized, and the systematic test of the functions, the performances and the like of the GPU can be carried out; in addition, the testing process is recorded in real time and compressed into a log file, so that a user can conveniently follow up the testing process; and finally, the test result is stored as an excel file, so that the user can perform visual analysis on GPU data of different models conveniently.
Description
Technical Field
The invention belongs to the technical field of GPU testing, and particularly relates to an automatic testing method for GPU systemization.
Background
In recent years, the technology of domestic GPU software and hardware is developed vigorously, a large number of domestic GPUs are developed in the market successively, and the application ecology of the domestic GPU software and hardware is limited, but no existing automatic GPU test program can be suitable for all domestic and non-domestic GPUs, the requirements of test coverage rate are often not met by a tester manually according to the requirements, the test process is also complicated, the time consumption is long, and the difficulty is brought to the evaluation work of the functions, the performances and the applicability of the GPU.
The application publication number CN109446004A discloses a method for automatically testing the GPU, which utilizes an open source testing tool NVQUAL to carry out GPU testing, and in addition, the testing method of the invention is only applicable to NVIDIA display cards and cannot adapt to new cards of more manufacturers and models. Therefore, it is necessary to design an automatic GPU testing method with better versatility and applicability.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an automatic testing method for GPU systemization, which aims to solve the technical problems of poor universality and applicability of the existing testing method.
The invention adopts the following technical scheme:
the automatic testing method for GPU systemization comprises the following steps:
step S1, setting a plurality of test dimensions, and constructing a corresponding test algorithm for each test dimension;
step S2, after the GPU to be tested is initialized, executing a corresponding test algorithm according to different test dimensions, recording the test process of each test dimension in real time, and simultaneously storing the test result, and correlating the test result of the same GPU to be tested;
and S3, comparing and displaying the test results of the GPUs with different models in a visual mode.
Further, the test dimension in step S1 includes basic information acquisition, functional test, performance test, and application test.
Further, the basic information acquisition comprises the steps of acquiring the current GPU model, CPU architecture, system version, kernel version, current test resolution, opneGL version and OpenCL version information to be tested; the functional test comprises the steps of judging whether the model GPU supports audio output or not, and judging the supporting condition of the model GPU on a main stream hard decoding frame, a main stream output mode and different video formats; the performance test comprises testing main stream 2D and 3D programs, pixel filling rate, texture filling rate, calculation force and limit performance, wherein the limit performance test comprises obtaining the number of the most supported video output and the number of the most operated 3D test cases by adopting a multi-process method; the application program test comprises judging the application support condition of the GPU to the GpuTest, the Unine Heaven and the DigitalEarth under the current test environment.
Further, the specific process of step S2 is as follows:
s21, inserting the GPU to be tested into a host computer, starting up, loading a GPU driver, compiling and running a test main program;
s22, for each test dimension, a corresponding test algorithm is called, test results are obtained in a corresponding mode, the test results of each test dimension are respectively stored as excel files named corresponding to the test dimension, and meanwhile, the test results of different dimensions are stored in an excel total file named by the GPU.
Further, in step S22, for basic information acquisition, GPU model, CPU architecture, system version, kernel version, current test resolution, opneGL version, openCL version information of the current test GPU are sequentially acquired through keyword searching, intercepting, and matching, and the acquired result is stored in an excel file named in the current test dimension, and meanwhile is stored in an excel total file named by the GPU.
Further, in step S22, for the functional test, whether the GPU supports audio output is first determined by searching for the audio device, and the determination result is stored; then carrying out decoding and output mode support condition test, in the test process, adopting mpv commands to sequentially play videos of different video formats based on different decoding frames and output modes aiming at the main stream decoding frames and the output modes, judging whether the videos can be normally played by detecting whether mpv processes exist in the playing process, judging whether character strings supporting hard decoding exist in a mode of searching, intercepting and matching keywords if the videos can be normally played, and judging that the model GPU supports the current hard decoding frames and the output modes for the videos of the current formats if the video strings supporting hard decoding exists, otherwise, judging that the model GPU does not support the current hard decoding frames and the output modes; and finally, respectively storing the test results of the video format, the hard decoding framework, the output mode and the supporting condition in an excel file named by the current test dimension, and simultaneously storing the test results in an excel total file named by the GPU.
Further, in step S22, for the performance test, performing a conventional performance test and a limiting performance test on the GPU in sequence, wherein in the conventional performance test, main stream 2D and 3D test cases, pixel filling rate test cases, texture filling rate test cases and computing power test cases are tested and run in sequence, in the test process, whether a corresponding process exists is firstly judged, and if so, a test result is obtained by means of keyword searching, searching and matching; and after the conventional performance test is finished, automatically performing an ultimate performance test, namely continuously increasing the number of main stream 2D and 3D test cases and video playing by adopting a multi-process method, judging whether the corresponding test case process number is synchronously increased, and finally respectively storing the test case name and the test result in an excel file named by the current test dimension and simultaneously storing the test case name and the test result in an excel total file named by a GPU.
Further, in step S22, for the application test, the GpuTest, the united heel, and the digitalEarth application are sequentially run, and by determining whether there is a corresponding process to obtain a result of whether the GPU supports the application under the current test environment, the application name and the test result are respectively stored in an excel file named in the current test dimension, and simultaneously stored in an excel total file named in the GPU.
The beneficial effects of the invention are as follows: the invention provides an automatic test method for GPU systemization, which comprises the steps of acquiring basic information, testing functions, testing performance and testing supporting conditions of application programs, designing a corresponding test algorithm for each dimension, specifically constructing a corresponding test case set for each test dimension, realizing the automatic test of each dimension, comprehensively testing the functions, the performances, the supporting conditions of the application programs and the like of the GPU, storing test results as excel files, and tracking the test process in real time; the method is suitable for all types of GPUs, has better universality and applicability, performs multi-dimensional test on the GPU in terms of functions, performances, application program supporting conditions and the like by adopting an automatic test method, saves test results as excel files, reduces manual participation, facilitates visual comparison analysis of various indexes of the GPUs of different types by users, and is beneficial to better evaluation, selection and design of the GPU by the users.
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Fig. 1 is a flowchart of an automatic test method for GPU systemization according to an embodiment of the present invention.
FIG. 2 is a flowchart of a basic information acquisition algorithm provided by an embodiment of the present invention.
FIG. 3 is a flowchart of a functional test algorithm provided by an embodiment of the present invention.
FIG. 4 is a flowchart of a performance test algorithm provided by an embodiment of the present invention.
FIG. 5 is a flowchart of an embodiment of the present invention for providing an application support case test algorithm.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
Fig. 1 shows a flow of an automatic test method for GPU systemization provided by an embodiment of the present invention, and only a portion related to the embodiment of the present invention is shown for convenience of explanation.
As shown in fig. 1, the automatic testing method for GPU systemization provided in this embodiment includes the following steps:
step S1, setting a plurality of test dimensions, and constructing a corresponding test algorithm for each test dimension.
The test dimensions include basic information acquisition, functional testing, performance testing, and application testing. And then constructing a corresponding test algorithm aiming at the test dimension, and sequentially calling the test algorithms under different dimensions in the main program to test the current GPU when the automatic test is executed subsequently.
The basic information testing algorithm comprises the steps of obtaining the information content of the current GPU model to be tested, the CPU architecture, the system version, the kernel version, the current testing resolution, the OpneGL version and the OpenCL version. The functional test comprises the steps of judging whether the model GPU supports audio output or not, and judging the supporting condition of the model GPU on a main stream hard decoding frame, a main stream output mode and different video formats; the performance test comprises testing main stream 2D and 3D programs, pixel filling rate, texture filling rate, calculation force and limit performance, wherein the limit performance test comprises obtaining the number of the most supported video output and the number of the most operated 3D test cases by adopting a multi-process method; the application program test comprises judging the application support condition of the GPU to the GpuTest, the Unine Heaven and the DigitalEarth under the current test environment.
Step S2, after the GPU to be tested is initialized, executing a corresponding test algorithm according to different test dimensions, recording the test process of each test dimension in real time, and simultaneously storing the test result, and correlating the test result of the same GPU to be tested;
the method comprises the following steps:
s21, inserting the GPU to be tested into a host computer, starting up, loading a GPU driver, compiling and running a test main program;
s22, for each test dimension, a corresponding test algorithm is called, test results are obtained in a corresponding mode, the test results of each test dimension are respectively stored as excel files named corresponding to the test dimension, and meanwhile, the test results of different dimensions are stored in an excel total file named by the GPU.
Specifically, as shown in fig. 2, for basic information acquisition, GPU model, CPU architecture, system version, kernel version, current test resolution, opneGL version, openCL version information of the current test GPU are sequentially acquired through keyword searching, intercepting, and matching, and the acquired result is stored in an excel file named in the current test dimension, and meanwhile is stored in an excel total file named by the GPU.
As shown in fig. 3, for the functional test, firstly, whether the GPU supports audio output is judged by searching for the audio device, and the judgment result is saved; then carrying out decoding and output mode support condition test, in the test process, aiming at main stream hard decoding frames such as vaapi, vdpau and the like and main stream output modes such as x11, xv, GPU and the like, adopting mpv commands to sequentially play videos of different video formats such as h264, h265, mpeg2, mpeg4 and the like based on different hard decoding frames and output modes, judging whether the video can be normally played by detecting whether an mpv process exists in the playing process, if so, judging whether a character string supporting hard decoding exists in modes such as keyword searching, intercepting and matching and the like, if so, judging that the model GPU supports the current hard decoding frame and the output mode for the video of the current format, otherwise, judging that the model GPU does not support the current hard decoding frame and the output mode; and finally, respectively storing the test results of the video format, the hard decoding framework, the output mode and the supporting condition in an excel file named by the current test dimension, and simultaneously storing the test results in an excel total file named by the GPU.
As shown in fig. 4, for performance testing, performing conventional performance testing and ultimate performance testing on the GPU in sequence, wherein in the conventional performance testing, main stream 2D and 3D test cases, pixel filling rate test cases, texture filling rate test cases and computing power test cases are tested and run in sequence, in the testing process, whether a corresponding process exists is firstly judged, and if so, testing results are obtained in modes of keyword searching, matching and the like; and after the conventional performance test is finished, automatically performing an ultimate performance test, namely continuously increasing the number of mainstream 2D and 3D test cases and video playing by adopting a multi-process method, judging whether the process numbers of the corresponding test cases are synchronously increased, respectively storing the test cases and test results in an excel file named by the current test dimension, and simultaneously storing the test cases and the test results in an excel total file named by the GPU.
As shown in fig. 5, for the application test, the GPUs test, the united leave and the digitalEarth application program are sequentially run, and by judging whether there is a corresponding process, whether the GPU supports the result of the application program in the current test environment is obtained, the application program name and the test result are respectively stored in an excel file named in the current test dimension, and meanwhile, are stored in an excel total file named in the GPU.
And S3, comparing and displaying the test results of the GPUs with different models in a visual mode.
The user initially sets the visual analysis method and the data, and the data is automatically displayed in the initially set visual mode after the test is completed.
And the different types of GPUs are tested under the same testing environment, and the initially set visualization method is used for comparing the differences of the different GPUs under various testing dimensions, so that the functional and performance comparison requirements among the different types of GPUs can be met.
In conclusion, the invention constructs the GPU testing algorithm with different dimensions based on basic information, functions, performance, application program supporting conditions and the like, sequentially calls the testing algorithm with each dimension in the main program, and realizes the comprehensive evaluation of the GPU functions, the performance, the application program supporting conditions and the like in multiple dimensions. And secondly, the GPU automatic test can be used as a general test method, and the comparison requirements of functions, performances and the like among different types of GPUs are met. In addition, the testing processes of different latitudes are recorded in real time in a log mode, and the testing processes are automatically compressed into log files, so that a user can conveniently follow up the testing processes. Finally, the data in the testing process are automatically analyzed by keyword interception, matching and other methods in the testing process to obtain a testing result, and the testing result is batched and summarized and stored in an excel mode, so that the visual analysis of the GPU from multiple dimensions by a user can be met.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. An automatic testing method for GPU systemization, which is characterized by comprising the following steps:
step S1, setting a plurality of test dimensions, and constructing a corresponding test algorithm for each test dimension;
step S2, after the GPU to be tested is initialized, executing a corresponding test algorithm according to different test dimensions, recording the test process of each test dimension in real time, and simultaneously storing the test result, and correlating the test result of the same GPU to be tested;
and S3, comparing and displaying the test results of the GPUs with different models in a visual mode.
2. The automatic test method for GPU systemization according to claim 1, wherein the test dimension in step S1 includes basic information acquisition, function test, performance test, and application test.
3. The automatic testing method of GPU systemization according to claim 2, wherein the basic information obtaining includes obtaining a current GPU model to be tested, a CPU architecture, a system version, a kernel version, a current testing resolution, an OpneGL version, an OpenCL version; the functional test comprises judging whether the model GPU supports audio output or not, and judging the supporting condition of the model GPU on each main stream hard decoding frame, each main stream output mode and different video formats; the performance test comprises the steps of testing main stream 2D and 3D programs, pixel filling rate, texture filling rate, calculation force and ultimate performance; the method comprises the steps of obtaining the number of the most supported video output and the number of the most running 2D and 3D test cases by adopting a multi-process method; the application program test comprises judging the application support condition of the GPU to the GpuTest, the Unine Heaven and the DigitalEarth under the current test environment.
4. The automatic testing method for GPU system according to claim 3, wherein said step S2 comprises the following steps:
s21, inserting the GPU to be tested into a host computer, starting up, loading a GPU driver, compiling and running a test main program;
s22, for each test dimension, a corresponding test algorithm is called, test results are obtained in a corresponding mode, the test results of each test dimension are respectively stored as excel files named corresponding to the test dimension, and meanwhile, the test results of different dimensions are stored in an excel total file named by the GPU.
5. The automatic test method for GPU systemization according to claim 4, wherein in step S22, for basic information acquisition, the GPU model, CPU architecture, system version, kernel version, current test resolution, opneGL version and OpenCL version information of the currently tested GPU are sequentially acquired through keyword searching, intercepting and matching, and the acquired result is stored in an excel file named in the current test dimension and simultaneously stored in an excel total file named by the GPU.
6. The automatic test method for GPU systemization according to claim 4, wherein in step S22, for the functional test, whether the GPU supports audio output is first judged by searching for audio equipment, and the judgment result is stored; then carrying out decoding and output mode support condition test, in the test process, adopting mpv commands to sequentially play videos of different video formats based on different decoding frames and output modes aiming at the main stream decoding frames and the output modes, judging whether the videos can be normally played by detecting whether mpv processes exist in the playing process, judging whether character strings supporting hard decoding exist in a mode of searching, intercepting and matching keywords if the videos can be normally played, and judging that the model GPU supports the current hard decoding frames and the output modes for the videos of the current formats if the video strings supporting hard decoding exists, otherwise, judging that the model GPU does not support the current hard decoding frames and the output modes; and finally, respectively storing the test results of the video format, the hard decoding framework, the output mode and the supporting condition in an excel file named by the current test dimension, and simultaneously storing the test results in an excel total file named by the GPU.
7. The automatic test method of GPU systemization according to claim 4, wherein in step S22, for performance test, conventional performance test and ultimate performance test are sequentially performed on the GPU, wherein in conventional performance test, main stream 2D and 3D test cases, pixel filling rate test cases, texture filling rate test cases and computing power test cases are sequentially tested and run, whether corresponding processes exist is firstly judged in the test process, and if yes, test results are obtained through keyword searching, searching and matching modes; and after the conventional performance test is finished, automatically performing an ultimate performance test, namely continuously increasing the number of main stream 2D and 3D test cases and video playing by adopting a multi-process method, judging whether the corresponding test case process number is synchronously increased, and finally respectively storing the test case name and the test result in an excel file named by the current test dimension and simultaneously storing the test case name and the test result in an excel total file named by a GPU.
8. The automatic test method of GPU systemization according to claim 4, wherein in step S22, for application program test, gpuTest, unigine Heaven, digitalEarth application program are sequentially run, and by judging whether there is a corresponding process to obtain the result of whether the GPU supports the application program under the current test environment, the name of the application program and the test result are respectively stored in an excel file named with the current test dimension, and simultaneously stored in an excel total file named with GPU.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116302764A (en) * | 2023-05-22 | 2023-06-23 | 北京麟卓信息科技有限公司 | Texture filling rate testing method based on minimum data filling |
| CN117234827A (en) * | 2023-11-14 | 2023-12-15 | 武汉凌久微电子有限公司 | Multi-platform automatic test method and system based on domestic graphic processor |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116302764A (en) * | 2023-05-22 | 2023-06-23 | 北京麟卓信息科技有限公司 | Texture filling rate testing method based on minimum data filling |
| CN116302764B (en) * | 2023-05-22 | 2023-07-18 | 北京麟卓信息科技有限公司 | Texture filling rate testing method based on minimum data filling |
| CN117234827A (en) * | 2023-11-14 | 2023-12-15 | 武汉凌久微电子有限公司 | Multi-platform automatic test method and system based on domestic graphic processor |
| CN117234827B (en) * | 2023-11-14 | 2024-02-13 | 武汉凌久微电子有限公司 | Multi-platform automatic test method and system based on domestic graphic processor |
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