A parallel simulation method and device based on GPU resources

Abstract

The present invention discloses a parallel simulation method and device based on GPU resources. The method includes: obtaining hardware resource information and simulation task information to be processed; the hardware resource information includes a plurality of node resource information; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information; analyzing and processing the hardware resource information and the simulation task information to be processed to obtain target resource analysis information and target task analysis information; performing matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular, to a parallel simulation method and device based on GPU resources. Background Art

[0002] With the continuous development of computer technology, simulation technology has been widely used in fields such as scientific research, engineering design, industrial manufacturing, and biomedicine. Traditional simulation methods mainly rely on CPUs for computing. However, as the complexity and scale of simulation models continue to increase, the computing power of CPUs gradually becomes difficult to meet the requirements of efficient simulation. Due to its powerful parallel processing capabilities, GPUs have gradually become an important tool for high-performance computing. GPUs have a large number of cores and can process multiple tasks simultaneously, thus significantly improving computing efficiency. However, there are still some problems in current GPU simulation technologies, such as unbalanced resource allocation during multi-GPU parallelism, low data transfer efficiency, and unstable parallel performance. These problems limit the application of GPUs in complex simulation tasks. Therefore, a parallel simulation method and device based on GPU resources are provided to improve simulation efficiency and simulation resource utilization. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a parallel simulation method and device based on GPU resources, which are beneficial to improving simulation efficiency and simulation resource utilization.

[0004] To solve the above technical problem, in the first aspect of an embodiment of the present invention, a parallel simulation method based on GPU resources is disclosed, and the method includes:

[0005] Obtain hardware resource information and information on simulation tasks to be processed; the hardware resource information includes information on a number of node resources; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information;

[0006] Analyze and process the hardware resource information and the information on simulation tasks to be processed to obtain target resource analysis information and target task analysis information; the target resource analysis information includes a number of sequentially arranged first target resource information; the first target resource information includes a first resource serial number and M pieces of target resource availability information; the target task analysis information includes a number of simulation task resource requirement information;

[0007] Perform matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information.

[0008] In a second aspect of the embodiments of the present invention, a parallel simulation device based on GPU resources is disclosed. The device includes:

[0009] An acquisition module, configured to acquire hardware resource information and information of simulation tasks to be processed; the hardware resource information includes information of a plurality of node resources; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information;

[0010] A first processing module, configured to analyze and process the hardware resource information and the information of the simulation tasks to be processed, so as to obtain target resource analysis information and target task analysis information; the target resource analysis information includes a plurality of sequentially arranged first target resource information; the first target resource information includes a first resource serial number and M pieces of target resource available information; the target task analysis information includes a plurality of simulation task resource requirement information;

[0011] A second processing module, configured to perform matching processing on the target resource analysis information and the target task analysis information, so as to obtain target matching result information; the target matching result information includes a plurality of target resource task matching result information.

[0012] In a third aspect of the present invention, another parallel simulation device based on GPU resources is disclosed. The device includes:

[0013] A memory storing executable program code;

[0014] A processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory and executes some or all of the steps in the parallel simulation method based on GPU resources disclosed in the first aspect of the embodiments of the present invention.

[0016] In a fourth aspect of the present invention, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer instructions, which are used to execute some or all of the steps in the parallel simulation method based on GPU resources disclosed in the first aspect of the embodiments of the present invention when the computer instructions are called. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic diagram of the scenario of the parallel simulation system based on GPU resources provided by an embodiment of the present invention;

[0019] Figure 2 It is a schematic flowchart of a parallel simulation method based on GPU resources disclosed by an embodiment of the present invention;

[0020] Figure 3 It is a schematic structural diagram of a parallel simulation device based on GPU resources disclosed by an embodiment of the present invention;

[0021] Figure 4 It is a schematic structural diagram of another parallel simulation device based on GPU resources disclosed by an embodiment of the present invention. Detailed implementation manners

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0023] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0024] Referring to "embodiment" in this article means that a specific feature, structure or characteristic described in combination with the embodiment may be included in at least one embodiment of the present invention. The phrase appears in various positions in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0025] In this application, the term "exemplary" is used to mean "serving as an example, illustration, or instance". Any embodiment described as "exemplary" in this application is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can realize this application without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of this application with unnecessary details. Therefore, this application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in this application.

[0026] It should be noted that since the method of the embodiment of this application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time, in essence, is time information. It can be understood that in subsequent embodiments, if dimensions, quantities, positions, etc. are mentioned, they are all corresponding data existences for the computer device to process, and specific details are not elaborated here.

[0027] It should be noted that a brief introduction to the artificial intelligence-related technologies that may be involved in this application is provided. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines to enable the machines to have the functions of perception, reasoning, and decision-making.

[0028] Artificial intelligence technology is an interdisciplinary subject that involves a wide range of fields, including both hardware-level technologies and software-level technologies. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0029] Computer Vision Technology (CV) Computer vision is a science that studies how to enable machines to "see". More specifically, it refers to machine vision that uses cameras and computers to replace human eyes for target recognition and measurement, and further performs graphic processing to make the computer-processed images more suitable for human eyes to observe or be transmitted to instruments for detection. As a scientific discipline, computer vision research involves related theories and technologies, aiming to build artificial intelligence systems that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, etc. It also includes common biometric recognition technologies such as face recognition and fingerprint recognition.

[0030] Unimodal information is data of only one type, such as one of the data information types like text, image, audio, video, electromagnetic signal, etc. Multimodal information is data information that includes at least two types of unimodal information. Further, multimodal information is applicable to complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information of multiple modalities, higher performance and accuracy can usually be achieved in tasks.

[0031] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model usually refers to a model with hundreds of millions to trillions of parameters. The model usually needs to be trained on a large-scale dataset and requires a large amount of computing resources for optimization and adjustment. Large models are usually used to solve complex natural language processing, computer vision, and speech recognition tasks. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. In the embodiments of this application, the large model can be large language models such as ChatGPT, BERT, XLNet, Zhipu Model, Claude, Moonshot AI Model, ChatGLM Model, Tongwen Qianyi Model, MiniMax Model, Spark Model, Llama Model, 360GPT Model, Qwen Model, Baichuan Model, Lark Model, vivoLM Model, and Wenxin Yiyan. The embodiments of this application do not make any limitations.

[0032] The embodiments of this application provide a parallel simulation method, device, computer device, and computer-readable storage medium based on GPU resources, which will be described in detail below.

[0033] Please refer to Figure 1 , Figure 1A scenario schematic diagram of the parallel simulation system based on GPU resources provided by the embodiments of the present application. The parallel simulation system based on GPU resources may include a computer device 100, and a parallel simulation device based on GPU resources is integrated in the computer device 100, such as Figure 1 the computer device in

[0034] In the embodiments of the present application, the computer device 100 is mainly used to obtain hardware resource information and information about the simulation tasks to be processed; the hardware resource information includes information about several node resources; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information;

[0035] Analyze and process the hardware resource information and the information about the simulation tasks to be processed to obtain target resource analysis information and target task analysis information; the target resource analysis information includes several sequentially arranged pieces of first target resource information; the first target resource information includes a first resource serial number and M pieces of target resource available information; the target task analysis information includes several simulation task resource requirement information;

[0036] Perform matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information.

[0037] It can improve the simulation efficiency and the utilization rate of simulation resources.

[0038] In the embodiments of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. Among them, the cloud server is composed of a large number of computers or network servers based on cloud computing (Cloud Computing).

[0039] It can be understood that the computer device 100 used in the embodiments of the present application may be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device, which has a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may specifically be a desktop terminal or a mobile terminal, and the computer device 100 may specifically also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0040] Those skilled in the art can understand,Figure 1 The application environment shown is only one application scenario of the solution of this application, and does not constitute a limitation on the application scenarios of the solution of this application. Other application environments may also include more or fewer computer devices than those shown in Figure 1 For example, Figure 1 only one computer device is shown in . It can be understood that the parallel simulation system based on GPU resources may also include one or more other services, which are not specifically limited here.

[0041] In addition, as shown in Figure 1 the parallel simulation system based on GPU resources may also include a memory 200 for storing data, such as image data, location information, etc.

[0042] It should be noted that Figure 1 the schematic diagram of the scenario of the parallel simulation system based on GPU resources shown is only an example. The parallel simulation system and scenario described in the embodiments of this application are for more clearly explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those of ordinary skill in the art know that with the evolution of the parallel simulation system based on GPU resources and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are equally applicable to similar technical problems.

[0043] The present invention discloses a parallel simulation method and device based on GPU resources, which are beneficial to improving simulation efficiency and simulation resource utilization rate. The following will be described in detail respectively.

[0044] Embodiment 1

[0045] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of a parallel simulation method based on GPU resources disclosed in an embodiment of the present invention. Among them, Figure 2 the described parallel simulation method based on GPU resources is applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention.

[0046] As shown in Figure 2 , the parallel simulation method based on GPU resources may include the following operations:

[0047] 101. Obtain hardware resource information and.

[0048] In the embodiments of the present invention, the hardware resource information includes several node resource information; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information.

[0049] 102. Analyze and process the hardware resource information and the simulation task information to be processed, and obtain target resource analysis information and target task analysis information.

[0050] In an embodiment of the present invention, the target resource analysis information includes several first target resource information arranged in sequence; the first target resource information includes a first resource serial number and M pieces of target resource availability information; the target task analysis information includes several simulation task resource requirement information.

[0051] 103. Perform matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information.

[0052] It should be noted that the above M is an integer not less than 0, and the embodiments of the present invention do not make any limitations.

[0053] It should be noted that the above first resource information and the second resource information corresponding to the first resource information represent the GPU resource situation and the video memory resource situation corresponding to the server node, and the embodiments of the present invention do not make any limitations.

[0054] It should be noted that the above first GPU resource information and the second GPU resource information respectively represent the total amount of GPU resources and the available amount of GPU resources corresponding to a single hardware device, and the embodiments of the present invention do not make any limitations.

[0055] It should be noted that the above first storage resource information and the second storage resource information respectively represent the total amount of video memory resources and the available amount of video memory resources corresponding to a single hardware device, and the embodiments of the present invention do not make any limitations.

[0056] It should be noted that the above target matching result information is used to indicate that the simulation tasks corresponding to the target matching result information are deployed in parallel on different GPUs for parallel simulation, so as to improve the utilization efficiency of hardware resources and the simulation efficiency, and the embodiments of the present invention do not make any limitations.

[0057] It can be seen that implementing the parallel simulation method based on GPU resources described in the embodiments of the present invention is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0058] In an optional embodiment, the above analyzing and processing the hardware resource information and the simulation task information to be processed, and obtaining the target resource analysis information and the target task analysis information includes:

[0059] Perform resource availability analysis and processing on the hardware resource information to obtain target resource analysis information;

[0060] Perform parsing processing on the simulation task information to be processed to obtain target task analysis information.

[0061] It should be noted that the above parsing and processing of the simulation task information to be processed can be based on a large model to decompose the parsing and processing of the simulation task information input by the user, so as to obtain the simulation task resource requirement information (task number, GPU resource requirement, video memory requirement, etc.) corresponding to multiple different simulation tasks, or it can be based on the parsing rules formulated by the user. The embodiments of the present invention do not make limitations.

[0062] It can be seen that implementing the parallel simulation method based on GPU resources described in the embodiments of the present invention is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0063] In another optional embodiment, resource availability analysis and processing are performed on the hardware resource information to obtain target resource analysis information, including:

[0064] For any node resource information in the hardware resource information, the first resource calculation model is used to perform calculation processing on the node resource information to obtain the node resource analysis result value corresponding to the node resource information;

[0065] Among them, the first resource calculation model is:

[0066]

[0067] In the formula, JDFX represents the node resource analysis result value; A j1 and a i1 respectively represent the first GPU resource information corresponding to the j1th first resource information and the second GPU resource information corresponding to the i1th first resource information in the node resource information; B j2 and b i2 respectively represent the second storage resource information corresponding to the j2th first resource information and the second storage resource information corresponding to the i2th second resource information in the node resource information; x1 and x2 respectively represent the first calculation coefficient and the second calculation coefficient;

[0068] Sort all the node resource analysis result values from large to small to obtain the node resource analysis result information;

[0069] For any node resource analysis result value in the node resource analysis result information, based on the position serial number of the node resource analysis result value in the node resource analysis result information and the node resource information corresponding to the node resource analysis result value, the first target resource information corresponding to the node resource analysis result value is determined.

[0070] It should be noted that the above first calculation coefficient and second calculation coefficient are numerical values between 0 and 1, and the sum of the two is 1. The embodiments of the present invention do not make limitations.

[0071] It should be noted that the above calculation and processing of the node resource information using the first resource calculation model to obtain the node resource analysis result value corresponding to the node resource information is an analysis of the available situation of the overall hardware resources of the server node. Therefore, the GPU resources and video memory resources of the entire server node are used as the denominator, and then the sum of all available GPU resources and video memory resources is used as the numerator for calculation, so as to evaluate the available situation of the resources of the entire node, rather than summing up after evaluating individual GPU hardware. This can avoid the problem of inaccurate overall resource evaluation caused by the differences in GPU resources themselves, thereby improving the accuracy and efficiency of the evaluation of the overall node resources, and further improving the simulation efficiency and simulation resource utilization rate. The embodiments of the present invention do not make limitations in this regard.

[0072] It should be noted that the above sorting of all node resource analysis result values from large to small to obtain the node resource analysis result information is to sort the server nodes according to resource availability, so that the servers with better overall resource availability can give priority to ensuring simulation tasks. Then, in the case where the guarantee ability of the previous nodes is insufficient, the nodes sorted later are used for supplementary guarantee to improve the efficiency of simulation task guarantee. The embodiments of the present invention do not make limitations in this regard.

[0073] It can be seen that implementing the parallel simulation method based on GPU resources described in the embodiments of the present invention is beneficial to improving the simulation efficiency and simulation resource utilization rate.

[0074] In another optional embodiment, based on the position serial number of the node resource analysis result value in the node resource analysis result information and the node resource information corresponding to the node resource analysis result value, determining the first target resource information corresponding to the node resource analysis result value includes:

[0075] Determining the position serial number of the node resource analysis result value in the node resource analysis result information as the first resource serial number of the first target resource information corresponding to the node resource analysis result value;

[0076] For any first resource information in the node resource information corresponding to the node resource analysis result value, using the second resource calculation model to calculate and process the first resource information and the second resource information corresponding to the first resource information to obtain the target resource available information corresponding to the first resource information;

[0077] Among them, the second resource calculation model is:

[0078]

[0079] Wherein, ZYKY represents the available information of the target resource; AA and aa respectively represent the first GPU resource information and the second GPU resource information in the first resource information; bb and BB respectively represent the first storage resource information and the second storage resource information in the second resource information; x3 and x4 respectively represent the third calculation coefficient and the fourth calculation coefficient.

[0080] It should be noted that the above-mentioned third calculation coefficient and fourth calculation coefficient are values between 0 and 1, and the sum of the two is 1. The embodiments of the present invention do not make any limitations.

[0081] It should be noted that the above-mentioned calculation and processing of the first resource information and the second resource information corresponding to the first resource information by using the second resource calculation model separately analyzes the resource available status of the hardware device corresponding to a single GPU resource. Therefore, the single GPU resource amount and the video memory resource amount are used as the denominators for separate calculations, without considering the situation of the entire server, thereby improving the efficiency and accuracy of the availability analysis and evaluation of GPU resources. The embodiments of the present invention do not make any limitations.

[0082] It can be seen that implementing the parallel simulation method based on GPU resources described in the embodiments of the present invention is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0083] In yet another alternative embodiment, the target resource analysis information and the target task analysis information are matched to obtain target matching result information, including:

[0084] The simulation task resource requirement information in the target task analysis information is classified to obtain target resource requirement information; the target resource requirement information includes first target requirement information and second target requirement information; the first target requirement information includes N sequentially arranged first sub-target requirement information; the second target requirement information includes L sequentially arranged second sub-target requirement information;

[0085] Judge whether both N and L are greater than 0 to obtain a zero value judgment result;

[0086] When the zero value judgment result is yes, based on the order of the first resource serial numbers from small to large, one target resource available information in the first target resource information is sequentially selected as the resource available information to be matched;

[0087] Based on the resource available information to be matched and the target resource requirement information, the target matching result information is determined;

[0088] When the zero value judgment result is no, the processing flow corresponding to the zero value judgment result is ended.

[0089] It should be noted that both N and L are integers not less than 0, and their values can be dynamically changed. The embodiments of the present invention do not make any limitations.

[0090] It should be noted that the above first sub-goal requirement information indicates that the simulation task requires exclusive use of GPU resources. Further, the sorting of the above first sub-goal requirement information is based on the parsed simulation task numbers, and the embodiments of the present invention do not make any limitations.

[0091] It should be noted that the above second sub-goal requirement information indicates the requirement for multiple simulation tasks to be performed on the same GPU without the need to exclusively occupy GPU resources for simulation. Further, the sorting of the above second sub-goal requirement information is based on the parsed simulation task numbers, and the embodiments of the present invention do not make any limitations.

[0092] It can be seen that implementing the parallel simulation method based on GPU resources described in the embodiments of the present invention is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0093] In an alternative embodiment, based on the available information of the resources to be matched and the requirement information of the target resources, the target matching result information is determined, including:

[0094] Based on the magnitude relationship between the target resource available value corresponding to the target resource available information in the available information of the resources to be matched and 1, all the target resource available information in the available information of the resources to be matched is classified and sorted to obtain the first available information of the resources to be matched and the second available information of the resources to be matched; the first available information of the resources to be matched includes P first sub-resource available information distributed in sequence; the second available information of the resources to be matched includes Q second sub-resource available information distributed in sequence;

[0095] Based on the first available information of the resources to be matched, the second available information of the resources to be matched, and the requirement information of the target resources, the target matching result information is determined.

[0096] It should be noted that the above P and Q are integers not less than 0. Further, their values change dynamically, and the embodiments of the present invention do not make any limitations.

[0097] It should be noted that the above target resource available value represents the available state of the current hardware resources of the hardware device, and its value is a positive number greater than 0 and not greater than 1. The embodiments of the present invention do not make any limitations. Further, when the target resource available value is 1, it indicates that the hardware device has not been used for the simulation task, and it is classified as the first sub-resource available information. If it is less than 1, it is classified as the second sub-resource available information to achieve secondary classification of the hardware resources. So that when performing the simulation task later, if the simulation task requires exclusive use of GPU resources, it is selected from the first available information of the sub-resources, and there is no need to analyze and judge the second available information of the sub-resources, improving the efficiency and accuracy of resource allocation. The embodiments of the present invention do not make any limitations.

[0098] It should be noted that the above first sub-resource available information is sorted according to the computing power of the corresponding GPU, and the embodiments of the present invention do not make any limitations.

[0099] It should be noted that the above second sub-resource available information is sorted from large to small according to the available value of the target resource, and the embodiments of the present invention do not make any limitations. Further, the available value of the target resource characterizes the comprehensive available situation of the GPU and the video memory. Therefore, sorting from large to small is to preferentially use better hardware resources with available resources for the simulation task to ensure the efficient execution of the simulation task, and the embodiments of the present invention do not make any limitations.

[0100] It can be seen that implementing the parallel simulation method based on GPU resources described in the embodiments of the present invention is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0101] In another optional embodiment, based on the first to-be-matched resource available information, the second to-be-matched resource available information, and the target resource requirement information, the target matching result information is determined, including:

[0102] Judge whether P is greater than 0 to obtain the first numerical judgment result;

[0103] When the first numerical judgment result is yes, judge whether N is greater than 0 to obtain the second numerical judgment result;

[0104] When the second numerical judgment result is yes, sequentially select a first sub-target requirement information from the first target requirement information as the first to-be-processed requirement information, and delete the first sub-target requirement information corresponding to the first to-be-processed requirement information from the first target requirement information;

[0105] Judge whether the priority corresponding to the first to-be-processed requirement information is the first priority to obtain the priority judgment result;

[0106] When the priority judgment result is yes, sequentially select a first sub-resource available information from the first to-be-matched resource available information as the target resource task matching result information corresponding to the first to-be-processed requirement information;

[0107] Delete the first sub-resource available information corresponding to the target resource task matching result information from the first to-be-matched resource available information, and trigger the judgment of whether P is greater than 0 to obtain the first numerical judgment result;

[0108] When the priority judgment result is no, inversely select a first sub-resource available information from the first to-be-matched resource available information as the target resource task matching result information corresponding to the first to-be-processed requirement information;

[0109] Delete the first sub-resource availability information corresponding to the target resource task matching result information from the first to-be-matched resource availability information, and trigger the execution of determining whether P is greater than 0 to obtain a first numerical judgment result;

[0110] When the second numerical judgment result is no, determine whether L is greater than 0 to obtain a third numerical judgment result;

[0111] When the third numerical judgment result is yes, sequentially select a second sub-goal requirement information from the second target requirement information as the second to-be-processed requirement information;

[0112] Determine whether there is second sub-resource availability information that meets the resource matching condition in the second to-be-matched resource availability information to obtain an existence judgment result;

[0113] When the existence judgment result is yes, use the second sub-resource availability information with the earliest sorting in the resource matching condition as the target resource task matching result information corresponding to the second to-be-processed requirement information;

[0114] Delete the second sub-goal requirement information corresponding to the second to-be-processed requirement information from the second target requirement information, and trigger the execution of determining whether L is greater than 0 to obtain a third numerical judgment result;

[0115] When the existence judgment result is no, determine whether the second sub-goal requirement information corresponding to the second to-be-processed requirement information is the last second sub-goal requirement information in the second target requirement information to obtain a first sequence judgment result;

[0116] When the first sequence judgment result is no, trigger the execution of sequentially selecting a second sub-goal requirement information from the second target requirement information as the second to-be-processed requirement information;

[0117] When the first sequence judgment result is yes, trigger the execution of determining whether both N and L are greater than 0 to obtain a zero value judgment result;

[0118] When the third numerical judgment result is no, trigger the execution of determining whether both N and L are greater than 0 to obtain a zero value judgment result;

[0119] When the first numerical judgment result is no, trigger the execution of determining whether L is greater than 0 to obtain a third numerical judgment result.

[0120] It should be noted that first determining whether P is greater than 0 and then determining whether N is greater than 0 ensures that when there is a simulation task that requires exclusive GPU resources, the hardware node with an unused GPU is selected first. Otherwise, the next hardware node will be selected without the need to perform matching analysis between the first sub-goal requirement information and the first sub-resource availability information, improving the efficiency and accuracy of resource allocation. The embodiments of the present invention do not make limitations.

[0121] It should be noted that determining whether to sequentially select GPUs with high resources or inversely select GPUs with relatively few resources to ensure simulation tasks according to the above priorities (the first priority indicates that the simulation task is an important simulation task that needs to be guaranteed with priority. If it is not the first priority, hardware with relatively few GPUs can be used to guarantee it, which improves the reliability of parallel simulation of simulation tasks by hardware resources and also improves the effective utilization rate of resources) can improve the efficiency and accuracy of parallel simulation resource allocation. The embodiments of the present invention do not make any limitations in this regard.

[0122] It should be noted that the above resource matching conditions indicate that the GPU resource amount corresponding to the second sub-resource available information is greater than the GPU resource amount required by the simulation task, and the video memory resource amount corresponding to the second sub-resource available information is greater than the video memory resource amount required by the simulation task. The embodiments of the present invention do not make any limitations in this regard.

[0123] It can be seen that implementing the parallel simulation method based on GPU resources described in the embodiments of the present invention is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0124] Embodiment 2

[0125] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a parallel simulation device based on GPU resources disclosed in the embodiments of the present invention. Among them, Figure 3 the described device can be applied to a management system, such as a local server or a cloud server for management, etc. The embodiments of the present invention do not make any limitations in this regard. As Figure 3 shown, the device may include:

[0126] An acquisition module 201, configured to acquire hardware resource information and information of simulation tasks to be processed; the hardware resource information includes several node resource information; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information;

[0127] A first processing module 202, configured to analyze and process the hardware resource information and the information of simulation tasks to be processed to obtain target resource analysis information and target task analysis information; the target resource analysis information includes several sequentially arranged first target resource information; the first target resource information includes a first resource serial number and M pieces of target resource available information; the target task analysis information includes several simulation task resource requirement information;

[0128] The second processing module 203 is configured to perform matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information; the target matching result information includes a plurality of target resource task matching result information.

[0129] It can be seen that implementing Figure 3 the described parallel simulation device based on GPU resources is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0130] In another optional embodiment, as Figure 3 shown, analyzing and processing the hardware resource information and the simulation task information to be processed to obtain target resource analysis information and target task analysis information, including:

[0131] Performing resource availability analysis processing on the hardware resource information to obtain target resource analysis information;

[0132] Performing parsing processing on the simulation task information to be processed to obtain target task analysis information.

[0133] It can be seen that implementing Figure 3 the described parallel simulation device based on GPU resources is beneficial to improving the simulation efficiency and the utilization rate of simulation resources.

[0134] In yet another optional embodiment, as Figure 3 shown, performing resource availability analysis processing on the hardware resource information to obtain target resource analysis information, including:

[0135] For any node resource information in the hardware resource information, using the first resource calculation model to perform calculation processing on the node resource information to obtain a node resource analysis result value corresponding to the node resource information;

[0136] Wherein, the first resource calculation model is:

[0137]

[0138] In the formula, JDFX represents the node resource analysis result value; A j1 and a i1 respectively represent the first GPU resource information corresponding to the j1th first resource information and the second GPU resource information corresponding to the i1th first resource information in the node resource information; B j2 and b i2 respectively represent the second storage resource information corresponding to the j2th first resource information and the second storage resource information corresponding to the i2th second resource information in the node resource information; x1 and x2 respectively represent the first calculation coefficient and the second calculation coefficient;

[0139] Sort the analysis result values of all node resources from largest to smallest to obtain node resource analysis result information;

[0140] For any node resource analysis result value in the node resource analysis result information, based on the position serial number of the node resource analysis result value in the node resource analysis result information and the node resource information corresponding to the node resource analysis result value, determine the first target resource information corresponding to the node resource analysis result value.

[0141] It can be seen that implementing Figure 3 the described parallel simulation device based on GPU resources is beneficial to improving simulation efficiency and simulation resource utilization rate.

[0142] In yet another alternative embodiment, as Figure 3 shown, based on the position serial number of the node resource analysis result value in the node resource analysis result information and the node resource information corresponding to the node resource analysis result value, determining the first target resource information corresponding to the node resource analysis result value includes:

[0143] Determine the position serial number of the node resource analysis result value in the node resource analysis result information as the first resource serial number of the first target resource information corresponding to the node resource analysis result value;

[0144] For any first resource information in the node resource information corresponding to the node resource analysis result value, use the second resource calculation model to perform calculation processing on the first resource information and the second resource information corresponding to the first resource information to obtain the target resource available information corresponding to the first resource information;

[0145] Wherein, the second resource calculation model is:

[0146]

[0147] In the formula, ZYKY represents the target resource available information; AA and aa respectively represent the first GPU resource information and the second GPU resource information in the first resource information; bb and BB respectively represent the first storage resource information and the second storage resource information in the second resource information; x3 and x4 respectively represent the third calculation coefficient and the fourth calculation coefficient.

[0148] It can be seen that implementing Figure 3 the described parallel simulation device based on GPU resources is beneficial to improving simulation efficiency and simulation resource utilization rate.

[0149] In yet another alternative embodiment, as Figure 3 shown, perform matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information, including:

[0150] Classify the simulation task resource requirement information in the target task analysis information to obtain target resource requirement information; the target resource requirement information includes first target requirement information and second target requirement information; the first target requirement information includes N sequentially arranged first sub-target requirement information; the second target requirement information includes L sequentially arranged second sub-target requirement information;

[0151] Judge whether both N and L are greater than 0 to obtain a zero value judgment result;

[0152] When the zero value judgment result is yes, based on the order of the first resource serial numbers from small to large, sequentially select one target resource available information in the first target resource information as the resource available information to be matched;

[0153] Based on the resource available information to be matched and the target resource requirement information, determine the target matching result information;

[0154] When the zero value judgment result is no, end the processing flow corresponding to the zero value judgment result.

[0155] It can be seen that implementing Figure 3 the described parallel simulation device based on GPU resources is beneficial to improving simulation efficiency and simulation resource utilization rate.

[0156] In another alternative embodiment, as Figure 3 shown, based on the resource available information to be matched and the target resource requirement information, determine the target matching result information, including:

[0157] Based on the size relationship between the target resource available value corresponding to the target resource available information in the resource available information to be matched and 1, classify and sort all the target resource available information in the resource available information to be matched to obtain first resource available information to be matched and second resource available information to be matched; the first resource available information to be matched includes P sequentially distributed first sub-resource available information; the second resource available information to be matched includes Q sequentially distributed second sub-resource available information;

[0158] Based on the first resource available information to be matched, the second resource available information to be matched and the target resource requirement information, determine the target matching result information.

[0159] It can be seen that implementing Figure 3 the described parallel simulation device based on GPU resources is beneficial to improving simulation efficiency and simulation resource utilization rate.

[0160] In another alternative embodiment, as Figure 3 shown, based on the first resource available information to be matched, the second resource available information to be matched and the target resource requirement information, determine the target matching result information, including:

[0161] Judge whether P is greater than 0 to obtain the first numerical judgment result;

[0162] When the first numerical judgment result is yes, judge whether N is greater than 0 to obtain the second numerical judgment result;

[0163] When the second numerical judgment result is yes, sequentially select a first sub-goal requirement information from the first target requirement information as the first requirement information to be processed, and delete the corresponding first sub-goal requirement information from the first target requirement information;

[0164] Judge whether the priority corresponding to the first requirement information to be processed is the first priority to obtain the priority judgment result;

[0165] When the priority judgment result is yes, sequentially select a first sub-resource availability information from the first available resource information to be matched as the target resource task matching result information corresponding to the first requirement information to be processed;

[0166] Delete the corresponding first sub-resource availability information of the target resource task matching result information from the first available resource information to be matched, and trigger the execution of judging whether P is greater than 0 to obtain the first numerical judgment result;

[0167] When the priority judgment result is no, inversely select a first sub-resource availability information from the first available resource information to be matched as the target resource task matching result information corresponding to the first requirement information to be processed;

[0168] Delete the corresponding first sub-resource availability information of the target resource task matching result information from the first available resource information to be matched, and trigger the execution of judging whether P is greater than 0 to obtain the first numerical judgment result;

[0169] When the second numerical judgment result is no, judge whether L is greater than 0 to obtain the third numerical judgment result;

[0170] When the third numerical judgment result is yes, sequentially select a second sub-goal requirement information from the second target requirement information as the second requirement information to be processed;

[0171] Judge whether there is a second sub-resource availability information that meets the resource matching condition in the second available resource information to be matched to obtain the existence judgment result;

[0172] When the existence judgment result is yes, use the second sub-resource availability information with the earliest sorting in the resource matching conditions as the target resource task matching result information corresponding to the second requirement information to be processed;

[0173] Delete the second sub-goal requirement information corresponding to the second to-be-processed requirement information from the second target requirement information, and trigger the execution of a judgment on whether L is greater than 0 to obtain a third numerical judgment result;

[0174] When there is a judgment result of no, judge whether the second sub-goal requirement information corresponding to the second to-be-processed requirement information is the last second sub-goal requirement information in the second target requirement information to obtain a first sequence judgment result;

[0175] When the first sequence judgment result is no, trigger the execution of sequentially selecting a second sub-goal requirement information from the second target requirement information as the second to-be-processed requirement information;

[0176] When the first sequence judgment result is yes, trigger the execution of a judgment on whether both N and L are greater than 0 to obtain a zero-value judgment result;

[0177] When the third numerical judgment result is no, trigger the execution of a judgment on whether both N and L are greater than 0 to obtain a zero-value judgment result;

[0178] When the first numerical judgment result is no, trigger the execution of a judgment on whether L is greater than 0 to obtain a third numerical judgment result.

[0179] It can be seen that implementing Figure 3 the parallel simulation device based on GPU resources described is conducive to improving simulation efficiency and simulation resource utilization rate.

[0180] Embodiment III

[0181] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of another parallel simulation device based on GPU resources disclosed in an embodiment of the present invention. Among them, Figure 4 the described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. As Figure 4 shown, the device may include:

[0182] A memory 301 storing executable program code;

[0183] A processor 302 coupled to the memory 301;

[0184] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the parallel simulation method based on GPU resources described in Embodiment I.

[0185] Embodiment IV

[0186] An embodiment of the present invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange. The computer program causes a computer to execute the steps in the parallel simulation method based on GPU resources described in Embodiment 1.

[0187] Embodiment 5

[0188] An embodiment of the present invention discloses a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in the parallel simulation method based on GPU resources described in Embodiment 1.

[0189] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0190] Through the specific descriptions of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the parts that contribute to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically-erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc memories, a magnetic disk memory, a magnetic tape memory, or any other computer-readable medium capable of carrying or storing data.

[0191] Finally, it should be noted that: The parallel simulation method and device based on GPU resources disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than limiting them; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A parallel simulation method based on GPU resources, characterized in that, The method includes: Obtaining hardware resource information and simulation task information to be processed; the hardware resource information includes several node resource information; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information; Analyzing and processing the hardware resource information and the simulation task information to be processed to obtain target resource analysis information and target task analysis information; the target resource analysis information includes several sequentially arranged first target resource information; the first target resource information includes a first resource serial number and M pieces of target resource available information; the target task analysis information includes several simulation task resource requirement information; Performing matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information; Among them, the analyzing and processing the hardware resource information and the simulation task information to be processed to obtain target resource analysis information and target task analysis information includes: Performing resource availability analysis processing on the hardware resource information to obtain target resource analysis information; Performing parsing processing on the simulation task information to be processed to obtain target task analysis information; Among them, the performing resource availability analysis processing on the hardware resource information to obtain target resource analysis information includes: For any one of the node resource information in the hardware resource information, using a first resource calculation model to perform calculation processing on the node resource information to obtain a node resource analysis result value corresponding to the node resource information; Among them, the first resource calculation model is: In the formula, JDFX represents the value of the node resource analysis result; A j1 and a i1 respectively represent the first GPU resource information corresponding to the j1-th first resource information and the second GPU resource information corresponding to the i1-th first resource information in the node resource information; B j2 and b i2 respectively represent the second storage resource information corresponding to the j2-th first resource information and the second storage resource information corresponding to the i2-th second resource information in the node resource information; x1 and x2 respectively represent the first calculation coefficient and the second calculation coefficient; Sorting all the node resource analysis result values from largest to smallest to obtain node resource analysis result information; For any one of the node resource analysis result values in the node resource analysis result information, based on the position serial number of the node resource analysis result value in the node resource analysis result information and the node resource information corresponding to the node resource analysis result value, determining the first target resource information corresponding to the node resource analysis result value.

2. The parallel simulation method based on GPU resources according to claim 1, wherein The determining the first target resource information corresponding to the node resource analysis result value based on the position serial number of the node resource analysis result value in the node resource analysis result information and the node resource information corresponding to the node resource analysis result value includes: Determining the position serial number of the node resource analysis result value in the node resource analysis result information as the first resource serial number of the first target resource information corresponding to the node resource analysis result value; For any one of the first resource information in the node resource information corresponding to the node resource analysis result value, using a second resource calculation model to perform calculation processing on the first resource information and the second resource information corresponding to the first resource information to obtain the target resource available information corresponding to the first resource information; Among them, the second resource calculation model is: Wherein, ZYKY represents the available information of the target resource; AA and aa respectively represent the first GPU resource information and the second GPU resource information in the first resource information; bb and BB respectively represent the first storage resource information and the second storage resource information in the second resource information; x3 and x4 respectively represent the third calculation coefficient and the fourth calculation coefficient.

3. The parallel simulation method based on GPU resources according to claim 1, wherein Performing matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information, including: Classifying the simulation task resource requirement information in the target task analysis information to obtain target resource requirement information; the target resource requirement information includes first target requirement information and second target requirement information; the first target requirement information includes N sequentially arranged first sub-target requirement information; the second target requirement information includes L sequentially arranged second sub-target requirement information; Judging whether both N and L are greater than 0 to obtain a zero value judgment result; When the zero value judgment result is yes, based on the order of the first resource serial numbers from small to large, sequentially select one piece of target resource available information in the first target resource information as the resource available information to be matched; Based on the resource available information to be matched and the target resource requirement information, determining target matching result information; When the zero value judgment result is no, ending the processing flow corresponding to the zero value judgment result.

4. The parallel simulation method based on GPU resources according to claim 3, characterized in that The determining the target matching result information based on the resource available information to be matched and the target resource requirement information includes: Based on the magnitude relationship between the target resource available value corresponding to the target resource available information in the resource available information to be matched and 1, performing classification and sorting processing on all the target resource available information in the resource available information to be matched to obtain first resource available information to be matched and second resource available information to be matched; the first resource available information to be matched includes P sequentially distributed first sub-resource available information; the second resource available information to be matched includes Q sequentially distributed second sub-resource available information; Based on the first resource available information to be matched, the second resource available information to be matched, and the target resource requirement information, determining target matching result information.

5. The parallel simulation method based on GPU resources according to claim 4, wherein The determining the target matching result information based on the first resource available information to be matched, the second resource available information to be matched, and the target resource requirement information includes: Judging whether P is greater than 0 to obtain a first numerical judgment result; When the first numerical judgment result is yes, judging whether N is greater than 0 to obtain a second numerical judgment result; When the second numerical judgment result is yes, sequentially select one piece of the first sub-target requirement information in the first target requirement information as the first requirement information to be processed, and delete the first sub-target requirement information corresponding to the first requirement information to be processed from the first target requirement information; Judging whether the priority corresponding to the first requirement information to be processed is the first priority to obtain a priority judgment result; When the priority judgment result is yes, sequentially select one of the first sub-resource availability information from the first to-be-matched resource availability information as the target resource task matching result information corresponding to the first to-be-processed requirement information; Delete the first sub-resource availability information corresponding to the target resource task matching result information from the first to-be-matched resource availability information, and trigger the execution of judging whether P is greater than 0 to obtain a first numerical judgment result; When the priority judgment result is no, reversely select one of the first sub-resource availability information from the first to-be-matched resource availability information as the target resource task matching result information corresponding to the first to-be-processed requirement information; Delete the first sub-resource availability information corresponding to the target resource task matching result information from the first to-be-matched resource availability information, and trigger the execution of judging whether P is greater than 0 to obtain a first numerical judgment result; When the second numerical judgment result is no, judge whether L is greater than 0 to obtain a third numerical judgment result; When the third numerical judgment result is yes, sequentially select one of the second sub-target requirement information from the second target requirement information as the second to-be-processed requirement information; Judge whether there is the second sub-resource availability information that meets the resource matching condition in the second to-be-matched resource availability information to obtain an existence judgment result; When the existence judgment result is yes, use the second sub-resource availability information with the earliest sorting position that meets the resource matching condition as the target resource task matching result information corresponding to the second to-be-processed requirement information; Delete the second sub-target requirement information corresponding to the second to-be-processed requirement information from the second target requirement information, and trigger the execution of judging whether L is greater than 0 to obtain a third numerical judgment result; When the existence judgment result is no, judge whether the second sub-target requirement information corresponding to the second to-be-processed requirement information is the last second sub-target requirement information in the second target requirement information to obtain a first sequence judgment result; When the first sequence judgment result is no, trigger the execution of sequentially selecting one of the second sub-target requirement information from the second target requirement information as the second to-be-processed requirement information; When the first sequence judgment result is yes, trigger the execution of judging whether both N and L are greater than 0 to obtain a zero value judgment result; When the third numerical judgment result is no, trigger the execution of judging whether both N and L are greater than 0 to obtain a zero value judgment result; When the first numerical judgment result is no, trigger the execution of judging whether L is greater than 0 to obtain a third numerical judgment result.

6. A parallel simulation device based on GPU resources, characterized in that, The device includes: An acquisition module, configured to acquire hardware resource information and to-be-processed simulation task information; the hardware resource information includes a plurality of node resource information; the node resource information includes M pieces of first resource information and second resource information corresponding to the first resource information; the first resource information includes first GPU resource information and second GPU resource information; the second resource information includes first storage resource information and second storage resource information; A first processing module, configured to analyze and process the hardware resource information and the to-be-processed simulation task information to obtain target resource analysis information and target task analysis information; the target resource analysis information includes a plurality of first target resource information arranged in sequence; the first target resource information includes a first resource serial number and M pieces of target resource availability information; the target task analysis information includes a plurality of simulation task resource requirement information. A second processing module, configured to perform matching processing on the target resource analysis information and the target task analysis information to obtain target matching result information; the target matching result information includes a plurality of target resource task matching result information. Wherein, the analyzing and processing the hardware resource information and the to-be-processed simulation task information to obtain target resource analysis information and target task analysis information includes: Performing resource availability analysis processing on the hardware resource information to obtain target resource analysis information. Performing parsing processing on the to-be-processed simulation task information to obtain target task analysis information. Wherein, the performing resource availability analysis processing on the hardware resource information to obtain target resource analysis information includes: For any one of the node resource information in the hardware resource information, using a first resource calculation model to perform calculation processing on the node resource information to obtain a node resource analysis result value corresponding to the node resource information. Wherein, the first resource calculation model is: Wherein, JDFX represents the value of the node resource analysis result; A j1 and a i1 respectively represent the first GPU resource information corresponding to the j1-th first resource information and the second GPU resource information corresponding to the i1-th first resource information in the node resource information; B j2 and b i2 respectively represent the second storage resource information corresponding to the j2-th first resource information and the second storage resource information corresponding to the i2-th second resource information in the node resource information; x1 and x2 respectively represent a first calculation coefficient and a second calculation coefficient; Sorting all the node resource analysis result values from largest to smallest to obtain node resource analysis result information. For any one of the node resource analysis result values in the node resource analysis result information, based on the position serial number of the node resource analysis result value in the node resource analysis result information and the node resource information corresponding to the node resource analysis result value, determining the first target resource information corresponding to the node resource analysis result value.

7. A parallel simulation device based on GPU resources, characterized in that, The device includes: A memory storing executable program code. A processor coupled to the memory. The processor calls the executable program code stored in the memory and executes the parallel simulation method based on GPU resources according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which are used to execute the parallel simulation method based on GPU resources according to any one of claims 1-5 when being called.

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