CN116166466A - Rendering card frame detection method, electronic device and computer storage medium - Google Patents
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Abstract
The invention relates to the technical field of film and television rendering processing, and particularly discloses a rendering card frame detection method, electronic equipment and a computer storage medium, wherein the method comprises the following steps: firstly, acquiring first rendering data and second rendering data in a rendering task, wherein the first rendering data is the rendering data of a frame which is already completed in the rendering task, and the second rendering data is the rendering data of a current frame in the rendering task; then, predicting to obtain the current frame completion time based on the first rendering data and the second rendering data; if the actual rendering time length of the current frame is longer than the current frame completion time length, determining that the card frame exists in the rendering task. Therefore, whether the card frame exists in the rendering task is accurately detected, the rendering task can be ended by related staff, and the task is restarted or canceled, so that the situation that the machine resource and the user time are wasted is avoided.
Description
Technical Field
The invention relates to the technical field of video rendering processing, in particular to a rendering card frame detection method, electronic equipment and a computer storage medium.
Background
With development of cloud computing technology, more and more rendering tasks are rendered through a cloud rendering platform. The user can upload the rendering scene file to be rendered through the cloud rendering platform, the platform generates a rendering task, rendering service is provided for the user, and finally the rendering result file is output to the user.
Each rendering task job consists of a plurality of frames to be rendered, and in the rendering process of the frames, the cloud rendering platform dispatches a specific rendering server to render specific frames. In the rendering process, sometimes, the machine CPU occupies too high, rendering software can cause card frames due to error card owner and the like, namely, certain frames are blocked and cannot be completed, so that the frames cannot be rendered normally, if the card frame condition is not detected in time, the rendering task is always in a blocked state, machine resources and time of a user are wasted, and in the prior art, a method for detecting the card frames is not available, so that the card frames can be found out in time and processed in time, and the waste of the machine resources and the time of the user is avoided.
Disclosure of Invention
Therefore, the present invention is directed to a method for detecting a frame of a rendering card, an electronic device and a computer storage medium, so as to solve the problem that the frame of the rendering card seriously wastes machine resources and user time because the frame of the rendering card cannot be detected accurately at present.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a method for detecting a frame of a rendering card, including:
acquiring first rendering data and second rendering data in a rendering task, wherein the first rendering data is the rendering data of a frame which is already completed in the rendering task, and the second rendering data is the rendering data of a current frame in the rendering task;
predicting to obtain the current frame completion time based on the first rendering data and the second rendering data;
and if the actual rendering time length of the current frame is longer than the current frame completion time length, determining that the rendering task has a card frame.
Further, the first rendering data includes: the completion time length and rendering hardware information of the completed frame; the second rendering data includes: rendering hardware information of the current frame;
the predicting, based on the first rendering data and the second rendering data, a current frame completion duration includes:
calculating and determining a prediction coefficient based on the finishing time length of the finished frame and rendering hardware information;
and calculating the completion time length of the current frame based on the prediction coefficient and the rendering hardware information of the current frame.
Further, the number of completed frames is a plurality; the rendering hardware information of the completed frame includes: the deduction performance coefficient and the hardware performance value of the rendering device corresponding to the completed frame are used for completing the rendering operation of the completed frame;
the calculating and determining a prediction coefficient based on the finishing time length of the finished frame and rendering hardware information comprises the following steps:
taking the product of the deduction performance coefficient of rendering equipment corresponding to a finished frame and the performance value of a rendering device as a first calculation factor of the finished frame;
taking the quotient of the finishing time length of the finished frame and the first calculation factor of the finished frame as the second calculation factor of the finished frame;
taking the average of all the second calculation factors of the completed frames as the prediction coefficient.
Further, the calculating, based on the prediction coefficient and the rendering hardware information of the current frame, the current frame completion duration includes:
calculating a first calculation factor of the current frame, wherein the first calculation factor of the current frame is the product of a deduction performance coefficient and a hardware performance value of rendering equipment for rendering the current frame;
and taking the product of the first calculation factor of the current frame and the prediction coefficient as the current frame completion time length.
Further, before the acquiring the first rendering data and the second rendering data in the rendering task, the method further includes:
acquiring a plurality of groups of operation parameters of equipment for performing the rendering task at a preset frequency, wherein one group of operation parameters comprises CPU utilization rate and used memory value;
if the operation parameters are the same, determining that the rendering task is blocked;
and if the operation parameters are different, starting to acquire the first rendering data and the second rendering data in the rendering task.
Further, before the acquiring the first rendering data and the second rendering data in the rendering task, the method further includes:
acquiring the memory utilization rate of equipment for performing the rendering task;
if the memory utilization rate is greater than or equal to a first preset threshold value, determining that the rendering task is blocked;
and if the memory usage rate is smaller than the first preset threshold value, starting to acquire first rendering data and second rendering data in the rendering task.
Further, before the acquiring the first rendering data and the second rendering data in the rendering task, the method further includes:
acquiring rendering time lengths of all frames in the rendering task;
if all the rendering durations are greater than a second preset threshold, starting to acquire first rendering data and second rendering data in the rendering task.
Further, the completed frames include a preset number of completed frames adjacent to the current frame.
In a second aspect, an embodiment of the present application provides an electronic device, including a memory, a calculator, and a computer program stored in the memory and capable of running on the processor, where the computer program, when executed by the processor, implements a rendering card frame detection method as described above at a preset frequency.
In a third aspect, embodiments of the present application provide a computer storage medium having a computer program stored thereon, which when executed by a processor causes the processor to perform a rendering card frame detection method as described above.
The technical scheme provided by the invention has at least the following beneficial effects:
the embodiment of the application provides a rendering card frame detection method, electronic equipment and a computer storage medium, wherein the method comprises the following steps: firstly, acquiring first rendering data and second rendering data in a rendering task, wherein the first rendering data is the rendering data of a frame which is already completed in the rendering task, and the second rendering data is the rendering data of a current frame in the rendering task; then, predicting to obtain the current frame completion time based on the first rendering data and the second rendering data; if the actual rendering time length of the current frame is longer than the current frame completion time length, determining that the card frame exists in the rendering task. In this way, by presetting the completion time length of the current frame being rendered and comparing the completion time length with the actual rendering time length of the current frame, when the actual rendering time length is longer than the predicted completion time length, the current frame is determined to be a card frame, whether the card frame exists in the rendering task is accurately detected, the rendering task can be finished from related staff, the task is restarted or canceled, and the situation that the real rendering time length is always in the card frame is avoided, and machine resources and user time are wasted.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a rendering card frame detection method according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for detecting a frame of a rendering card according to another embodiment of the present invention;
fig. 3 is a schematic block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
In the process of film and television animation rendering or cloud rendering, rendering resources generally comprise scene resource files which are uploaded by a user and need to be read before rendering, and result files which are obtained after the rendering is finished and are output to the user.
Wherein, a rendering platform such as a cloud rendering platform generates a task for each rendering submitted by a user at the cloud platform, the task having a specific ID number representation. After the rendering task is created, the cloud platform allocates computing resources, namely rendering resources, and frames in the task are respectively rendered. Under normal conditions, each frame is normally rendered, and the cloud platform outputs a rendering result file, but under some abnormal conditions, such as insufficient machine memory in the frame rendering process, the machine CPU occupies too high or other reasons can cause the phenomenon that some frames cannot be normally rendered or the frame rendering time is abnormally long (at this time, some frames may be already rendered), and a card frame exists. Therefore, the method and the device find the card frame in time, and have important significance for avoiding wasting machine resources and user time in the rendering process.
Based on the above, the embodiment of the invention provides a rendering card frame detection method, electronic equipment and a computer storage medium.
Method embodiment:
fig. 1 is a flow chart of a rendering card frame detection method according to an embodiment of the present invention, referring to fig. 1, the embodiment may include the following steps:
step S101, acquiring first rendering data and second rendering data in a rendering task.
The first rendering data is the rendering data of the frame which is already completed in the rendering task, and the second rendering data is the rendering data of the current frame in the rendering task.
Specifically, the first rendering data is the data of the frame that has completed rendering in the rendering task, and may include the completion duration and rendering hardware information of the completed frame (the frame that has completed rendering). It should be noted that, the rendering hardware generally includes computing resources of the cloud platform, such as the number of computers that complete the frame, the number of CPUs or cores that are specifically utilized, and the number of GPUs or cores.
Step S102, predicting and obtaining the current frame completion time based on the first rendering data and the second rendering data.
Specifically, according to the calculation resources and the completion time length used when the frame rendering is completed in the same rendering task, the relation between the calculation resources and the completion time length, that is, how much calculation resources are used for one frame, and what the corresponding completion time length is likely.
Step S103, judging whether a card frame exists in the rendering task or not based on the current frame completion time length and the actual rendering time length of the current frame.
Specifically, according to the obtained relation between the computing resource for rendering a frame and the time before completing rendering the frame, and the computing resource of the current frame, which is the current frame being rendered and is obtained in real time, the time of completing the current frame can be predicted. Meanwhile, the time of the current frame in the rendering process can be directly obtained from the platform, the rendering time is compared with the predicted current frame completion time, and if the rendering time is larger than the predicted current frame completion time, the existence of the card frame is determined.
In some embodiments, the decision condition may be relaxed a little more for error reasons, thereby ensuring that the result of whether a card frame is present is more reliable. For example, only after the actual rendering time of the current frame is longer than the preset range or the preset coefficient multiple of the completion time of the current frame, if the actual rendering time of the current frame exceeds 50% of the predicted completion time of the current frame, the frame (i.e. the current frame) is marked as a card frame.
In the method for detecting the frame of the rendering card, first rendering data and second rendering data in a rendering task are obtained, wherein the first rendering data is the rendering data of a frame which is already completed in the rendering task, and the second rendering data is the rendering data of a current frame in the rendering task; then, predicting to obtain the current frame completion time based on the first rendering data and the second rendering data; if the actual rendering time length of the current frame is longer than the current frame completion time length, determining that the card frame exists in the rendering task. In this way, by presetting the completion time length of the current frame being rendered and comparing the completion time length with the actual rendering time length of the current frame, when the actual rendering time length is longer than the predicted completion time length, the current frame is determined to be a card frame, whether the card frame exists in the rendering task is accurately detected, the rendering task can be finished from related staff, the task is restarted or canceled, and the situation that the real rendering time length is always in the card frame is avoided, and machine resources and user time are wasted.
In some embodiments, a prediction coefficient may be calculated by a plurality of groups of completion time lengths corresponding to completed frames and computing resources used for rendering, where the coefficient is represented by a relationship between the completion time lengths and the computing resources. After the prediction coefficient is determined through the completion time length and the calculation resources of a plurality of groups of completed frames, the predicted completion time length of the current frame, namely the current frame completion time length, is calculated based on the prediction coefficient and the calculation resources of the current frame. Therefore, the method is simple and effective, and the prediction efficiency is greatly improved.
In some embodiments, the computing resource may be determined by rendering hardware information, for example, the rendering hardware information may include a CPU core number or a GPU core number for rendering, and the prediction result obtained by performing the corresponding computation through the core number and the computation performance of the corresponding single CPU and GPU is more accurate.
In other embodiments, the deduction performance coefficient of the machine hardware can be introduced in the calculation process for deduction calculation, wherein the coefficient is a coefficient according to the performance equipment of the machine hardware, and the better the performance of the machine hardware is, the higher the coefficient is.
For example, in one complete implementation, based on the completion time y1 of the completed frame y1 and the CPU or GPU core number A1 for completing the frame y1 and the deduction performance coefficient B1 for completing the frame y1 machine, the completion time y2 of the completed frame y2 and the CPU or GPU core number A2 for completing the frame y2 and the deduction performance coefficient B2 for completing the frame y2 machine, the completion time yn of the completed frame yn and the CPU or GPU core number An for completing the frame yn and the deduction performance coefficient Bn for completing the frame yn machine, the calculation process or formula for calculating the prediction coefficient k may be as follows:
k＝(y1/(A1*B1)+y2/(A2*B2)+...+yn/(An*Bn))/n；
wherein, a1×b1 is the first calculation factor of the y1 frame, and y 1/(a 1×b1) is the second calculation factor of the y1 frame.
After the prediction coefficient is obtained by calculation, the calculation formula for obtaining the completion time length of the current frame based on the CPU or GPU accounting A of the current frame and the deduction performance coefficient B of the machine for completing the current frame can be as follows:
y=a×b×k; wherein a×b is the first calculation factor of the current frame.
For example: examples: assuming that the current task is cpu rendering (GPU rendering is the same), the deduction coefficient of performance B is 1, the cpu core number of the machine is 64, only the frame with the frame number of 6 is still being rendered, other frames are already rendered, the frame with the frame number of 6 being rendered is determined as the current frame, the completion time of the frame which is already being rendered in the rendering task is obtained, for example, the frame with the frame number of 5 before the frame with the frame number of 6 is completed, and the completion time is 1000s,1010s, 10200 s,1005s and 1002s respectively; the 5 frames after the current frame are finished into frames, and the finishing time is respectively 1050s, 10200 s,1030s and 1040s;
at this time, the calculation process of the prediction coefficient k is as follows: k= ((1000/64 x 1) + (1010/64 x 1) + (1020/64 x 1) + (1005/64 x 1) + (1002/64 x 1) + (1050/64 x 1) + (1020/64 x 1) + (1030/64 x 1) + (1040/64 x 1))/10;
the prediction coefficient k= 15.9796875 is obtained.
The prediction time y=64×1×k= 1022.7s for the current frame, i.e., frame No. 6.
It should be noted that, in some implementations of the present application, the selection of the completed frame may be other manners, for example, 10 frames before the current frame, or 10 frames after the current frame, etc. However, in practical application, because the frame numbers have too many intervals (such as the first frame and the last frame) and have larger difference in completion time, the prediction error is smaller by using the front and rear 5 frame completion frames, so that the front and rear 5 frame completion frames of the current rendering frame are screened out to make the prediction of the completion time, and the prediction result is more accurate.
On the basis, in other embodiments of the present application, before the first rendering data and the second rendering data in the rendering task are acquired, multiple sets of operation parameters of the device for performing the rendering task may be acquired at a preset frequency, where one set of operation parameters includes a CPU usage rate and a memory used value, and is used to roughly determine in advance whether a card frame may exist.
Specifically, if the operation parameters of the plurality of groups are the same, determining that the rendering task has a jam, directly judging that the task has a jam at the moment, and recording the state without subsequently acquiring the first rendering data and the second rendering data and predicting the first rendering data and the second rendering data in the process, thereby saving computing resources; if the operation parameters are different, that is, if the operation parameters cannot be determined whether the jam exists, the first rendering data and the second rendering data in the rendering task are acquired at the moment, and the calculation judgment is performed, so that whether the jam exists or not is further judged, namely prediction calculation or prediction judgment is performed.
For example, detecting the utilization rate of CG software (rendered software) cpu of the current rendered frame and whether the used memory is unchanged for 3 times (i.e. three sets of operation parameters are acquired, for example), if so, directly marking the card frame, ending the card frame judgment, otherwise, accurately judging whether the card frame exists based on the first rendered data and the second rendered data, which is described in detail in the above embodiments and will not be described herein. Wherein the value may be obtained by a downstream program and reported to the platform service.
In other embodiments of the present application, before obtaining the first rendering data and the second rendering data in the rendering task to make the prediction judgment, obtaining the memory usage of the device for performing the rendering task may be further included; if the memory utilization rate is greater than or equal to a first preset threshold value, determining that the rendering task is blocked, and marking the reason of the blocked frame, namely that the memory abnormality leads to the blocked frame; if the memory usage rate is smaller than a first preset threshold value, the first rendering data and the second rendering data in the rendering task are acquired, and further subsequent prediction calculation is performed, so that calculation resources are saved in the scene.
Specifically, firstly, a first preset threshold value of the equipment is 99.8%, then a frame in rendering is checked through a preset platform, the memory utilization rate of a machine where a corresponding frame (current frame) is located is obtained, whether the current frame is a card frame or not is directly judged based on the real-time memory utilization rate and the size of 99.8%, if the current frame is more than 99.8%, the frame is directly judged to be the card frame, and the reason of the card frame is that the memory is abnormal; if the prediction result does not exceed 99.8%, the prediction determination may be performed based on the first rendering data and the second rendering data in the above embodiment. Therefore, when the memory causes the card frame, the prediction judgment can not be performed, and the computing resource is saved in the scene.
In other embodiments of the present application, before the first rendering data and the second rendering data in the rendering task are obtained to perform prediction judgment, the method may further include obtaining rendering durations of all frames in the rendering task, judging whether a card frame exists based on the rendering durations of the frames, and marking a reason of the card frame when judging that the card frame exists.
Specifically, if all rendering durations are smaller than a second preset threshold, for example, 900 seconds, determining that the rendering task does not have any jamming, and at this time, acquiring the first rendering data and the second rendering data and performing prediction judgment; if the rendering time length is greater than or equal to the second preset threshold value, the existence of the card frame is initially determined, then the first rendering data and the second rendering data in the rendering task are acquired, prediction judgment is further carried out based on the first rendering data and the second rendering data, and whether the card frame really exists or not is determined.
It should be noted that, in the frames in actual rendering, if the rendering time that the card frame generally shows is long, according to the rendering experience, the frame with the general rendering time longer than 900 seconds may be the card frame, then the frame (the frame with the rendering process longer than 900 seconds) may be obtained as the second rendering data, the data of the frame that has been completed before and after the preset number of frames is used as the first rendering data, then the prediction calculation is performed based on the scheme in the above embodiment, and whether the frame is the card frame is judged.
It will be appreciated that frames exceeding 900 seconds, if completed, may be determined directly that the frame is not a stuck frame, without subsequent predictive calculations; and over 900 seconds and still in the rendered state, it is necessary to determine whether it is a card frame or not through the above prediction calculation.
In addition, in the present application, since the first rendering data may be data of a preset number of frames immediately before and after the current frame, such as five frames before and after, before performing the prediction calculation, it may be further determined whether there are frames after the current rendering frame, such as 5 frames before and after the current rendering frame, according to the frame number arrangement. If yes, the frame can be normally rendered under the normal condition, and the frame is rendered, so that the probability of card frame is larger, and the following prediction calculation can be performed; if not, the judgment can be ended, and the judgment is re-performed after the preset time length.
Further, when it is preliminarily determined that there may be a card frame (i.e., in addition to the determination that there is a card frame and the determination that there is no card frame based on the above-described determination), it may be determined whether the total number of completed frames of the current task is greater than a third preset value such as 15, and whether the ratio of the completed frames ((the number of completed frames/the total number of tasks) ×100%) is greater than a fourth preset value such as 60%, if so, the subsequent acquisition of the first rendering data and the second rendering data is performed, thereby performing a predictive determination, and if not, the subsequent predictive determination is not performed. Therefore, the method ensures that the finished frames with enough quantity are subjected to subsequent prediction judgment, and ensures the accuracy of the prediction judgment.
In the following, a detailed description of a rendering card frame detection method provided in the present application is provided with a complete implementation process, and fig. 2 is a schematic flow chart of a rendering card frame detection method provided in another embodiment of the present application, and as shown in fig. 2, may include:
firstly, detecting CG software (rendered software) cpu utilization rate of a current rendered frame, and if the used memory is unchanged for 3 times, directly marking a card frame, otherwise, executing the next step. The value is obtained by the downstream program and reported to the platform service.
Then, the platform checks the frame in rendering and obtains the memory utilization rate of the machine where the corresponding frame is located. If the value is greater than 99.8%, the frame is marked as a card frame because of a memory exception. Otherwise, executing the next step.
And filtering out frames with rendering time longer than 900 seconds from all the frames in rendering, and rendering the frames with the card according to rendering experience. If this is not the case, the judgment is ended.
And then according to the frame number, whether the front and rear 5 frames of the current rendered frame have the rendered frames or not is judged. If so, the frame can be normally rendered under the normal condition, and the frame is rendered, so that the probability of card frame is larger, and the next judgment is carried out. If not, the judgment is ended.
And judging whether the current task completion frame number is more than 15 and the completion frame number ratio ((the completion frame number/the total task frame number) is 100%) is more than 60%, and if so, performing the next judgment. If not, ending the judgment.
And then screening out the front and rear 5 frame completion frames of the current rendering frame, and predicting the completion time. Because the frame numbers are too far apart (such as the first frame and the last frame) and the finishing time difference is larger, the prediction error is smaller by using the finishing frames of the front and the back 5 frames.
Finally, if the current rendering time exceeds the predicted time by more than 50% according to the predicted time, the frame is marked as a card frame. Otherwise, ending the judgment.
In the process, the card frame real-time detection can be realized by presetting a timer to be carried out within a specified time length such as 15 minutes, and scanning the task being rendered by the platform every 15 minutes through the platform timer.
In addition, the detection can be based on the computing resource, and a plurality of rendering tasks or a plurality of frames in rendering states in the same rendering task can be detected at the same time, whether a certain rendering frame is a card frame or not can be accurately detected through the method, the detected card frame can be displayed in a concentrated mode, an operator, namely related staff, can find out in time and find out a corresponding machine to perform corresponding processing, and therefore the rendering efficiency of a client can be greatly improved
According to the rendering card frame detection method, firstly, whether a card frame exists is primarily judged through information of hardware operation, and when the card frame exists or the card frame does not exist is judged based on the hardware information, a detection result is obtained; and when the information based on the hardware operation cannot accurately judge whether the card frame exists, calculating based on the first rendering data and the second rendering data, predicting the current frame completion time length, and comparing the current frame completion time length with the actual rendering time length so as to determine whether the card frame exists. Therefore, the frame clamping phenomenon can be accurately detected in various scenes, so that when a frame clamping exists, a warning is timely sent out for relevant staff to process, and the situation that the rendering task is always in the rendering process and machine resources and user time are wasted is avoided.
Electronic device embodiment:
based on a general inventive concept, an embodiment of the present invention also provides an electronic device including a memory 32 and a calculator 31, and a computer program stored on the memory 32 and executable on the processor 31, which when executed by the processor 31, implements the rendering card frame detection method as mentioned in the above method embodiment.
In some embodiments, the above-described functionality may be implemented by a timer or the like, by which real-time detection is implemented by scanning the task being rendered by the platform every 15 minutes.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
Computer storage media embodiments:
based on the same inventive concept, embodiments of the present application also provide a computer storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform a rendering card frame detection method as mentioned in the above method embodiments.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. A method for detecting a rendering card frame, comprising:
acquiring first rendering data and second rendering data in a rendering task, wherein the first rendering data is the rendering data of a frame which is already completed in the rendering task, and the second rendering data is the rendering data of a current frame in the rendering task;
predicting to obtain the current frame completion time based on the first rendering data and the second rendering data;
and if the actual rendering time length of the current frame is longer than the current frame completion time length, determining that the rendering task has a card frame.
2. The rendering card frame detection method according to claim 1, wherein the first rendering data includes: the completion time length and rendering hardware information of the completed frame; the second rendering data includes: rendering hardware information of the current frame;
the predicting, based on the first rendering data and the second rendering data, a current frame completion duration includes:
calculating and determining a prediction coefficient based on the finishing time length of the finished frame and rendering hardware information;
and calculating the completion time length of the current frame based on the prediction coefficient and the rendering hardware information of the current frame.
3. The rendering card frame detection method according to claim 2, wherein the number of completed frames is a plurality; the rendering hardware information of the completed frame includes: the deduction performance coefficient and the hardware performance value of the rendering device corresponding to the completed frame are used for completing the rendering operation of the completed frame;
the calculating and determining a prediction coefficient based on the finishing time length of the finished frame and rendering hardware information comprises the following steps:
taking the product of the deduction performance coefficient of rendering equipment corresponding to a finished frame and the performance value of a rendering device as a first calculation factor of the finished frame;
taking the quotient of the finishing time length of the finished frame and the first calculation factor of the finished frame as the second calculation factor of the finished frame;
taking the average of all the second calculation factors of the completed frames as the prediction coefficient.
4. The method for detecting a frame of a rendering card according to claim 3, wherein the calculating the current frame completion time based on the prediction coefficient and the rendering hardware information of the current frame includes:
calculating a first calculation factor of the current frame, wherein the first calculation factor of the current frame is the product of a deduction performance coefficient and a hardware performance value of rendering equipment for rendering the current frame;
and taking the product of the first calculation factor of the current frame and the prediction coefficient as the current frame completion time length.
5. The method of claim 1, further comprising, prior to the acquiring the first rendering data and the second rendering data in the rendering task:
acquiring a plurality of groups of operation parameters of equipment for performing the rendering task at a preset frequency, wherein one group of operation parameters comprises CPU utilization rate and used memory value;
if the operation parameters are the same, determining that the rendering task is blocked;
and if the operation parameters are different, starting to acquire the first rendering data and the second rendering data in the rendering task.
6. The method of claim 1, further comprising, prior to the acquiring the first rendering data and the second rendering data in the rendering task:
acquiring the memory utilization rate of equipment for performing the rendering task;
if the memory utilization rate is greater than or equal to a first preset threshold value, determining that the rendering task is blocked;
and if the memory usage rate is smaller than the first preset threshold value, starting to acquire first rendering data and second rendering data in the rendering task.
7. The method of claim 1, further comprising, prior to the acquiring the first rendering data and the second rendering data in the rendering task:
acquiring rendering time lengths of all frames in the rendering task;
if all the rendering durations are greater than a second preset threshold, starting to acquire first rendering data and second rendering data in the rendering task.
8. The rendering card frame detection method according to claim 1, wherein the completed frames include a preset number of completed frames adjacent to the current frame.
9. An electronic device comprising a memory and a processor, and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the rendering card frame detection method according to any one of claims 1 to 8 at a preset frequency.
10. A computer storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, causes the processor to perform the rendering card frame detection method according to any of claims 1 to 8.
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| CN202310258310.2A CN116166466A (en) | 2023-03-06 | 2023-03-06 | Rendering card frame detection method, electronic device and computer storage medium |
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| CN202310258310.2A CN116166466A (en) | 2023-03-06 | 2023-03-06 | Rendering card frame detection method, electronic device and computer storage medium |
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