Heterogeneous data fusion method of GPU software and hardware full stack ecology

Abstract

The application provides a heterogeneous data fusion method of GPU software and hardware full-stack ecology, belongs to the field of knowledge graph and intelligent reasoning, and comprises the following steps: S1, based on a GPU product knowledge graph, key entity and feature extraction, category representation allocation, multi-element relationship modeling and feature optimization, and dynamic knowledge flow modeling; S2, entity state maintenance and knowledge evolution mechanism, relationship flow coding based on knowledge link, knowledge community discovery and evolution; S3, construction of ecological product graph portrait, knowledge link time sequence reasoning mechanism, and prediction of knowledge update frequency.The method can be widely applied to GPU ecological migration, AI development tool chain intelligent analysis, dynamic knowledge graph updating and the like, and has good expansibility and practical value.

Description

Technical Field

[0001] This invention belongs to the field of knowledge graphs and intelligent reasoning, and specifically relates to a method for heterogeneous data fusion in a full-stack GPU hardware and software ecosystem. Background Technology

[0002] Addressing the challenges of a broad, data-rich, and representation-difficult full-stack technology ecosystem encompassing both hardware and software integration, this research focuses on dynamic knowledge graph construction and reasoning techniques for the ecosystem and its products. This provides support for acquiring and understanding billions of complex relationships within the ecosystem. The research explores methods for fusing heterogeneous data, extracting key entities from different data sources, supporting entity alignment and disambiguation, and constructing a unified knowledge graph framework to achieve dynamic data updates and representation learning. Building upon this foundation, the research investigates reasoning mechanisms based on dynamic graph neural networks to support the modeling of entities and their relationships within the ecosystem and the inference of temporal evolution patterns. This generates graph portraits of the ecosystem and its products, enhancing the understanding and analysis of complex knowledge.

[0003] The heterogeneous data fusion focuses on breakthroughs in semantic alignment and conflict resolution technologies for multi-source data, establishing methods for cross-modal data cleaning, entity extraction, and relation recognition to provide a high-quality data foundation for knowledge graph construction. The unified knowledge graph framework construction is dedicated to designing a dynamically scalable graph architecture, developing a knowledge storage mechanism that supports incremental updates and representation learning, and realizing multi-dimensional dynamic modeling of ecological elements. The reasoning mechanism based on dynamic graph neural networks innovates temporal graph representation learning methods, develops graph neural network models that integrate spatiotemporal features, and supports the mining of ecological evolution laws and the reasoning analysis of the entire life cycle of products.

[0004] In the integrated hardware and software full-stack technology ecosystem, given the abundance of development toolchains and performance test reports, the heterogeneity of API documentation, algorithm implementations, debugging logs, and data such as architecture design diagrams and power consumption datasets, researching heterogeneous data fusion methods is particularly important. This includes deep integration of domestic GPU technical documentation, related algorithm libraries, and AI inference application cases. It involves accurately extracting key entities such as architecture parameters in design specifications, function interfaces in code repositories, and configuration items in user manuals, and supports entity alignment and semantic disambiguation for core scenarios such as instruction set extensions, driver version compatibility, and resource scheduling strategies.

[0005] In the research field of the domestic GPU ecosystem, there are issues such as diverse artifacts like hardware specifications and performance test reports, and heterogeneous data sources like API documents and architecture design diagrams. In the area of ​​complex knowledge associations among artifacts, there are problems with handling complex knowledge relationship edge features between software and hardware artifacts. There are also issues of incompatibility and fragmentation in the expression of heterogeneous data. Furthermore, in the software and hardware integrated full-stack technology ecosystem, the complexity of scenarios such as dynamic adaptation of cross-artifact interfaces and dynamic evolution of knowledge communities, as well as the heterogeneity challenges of tasks such as knowledge link redundancy control and time-series state tracking, make the construction of a unified knowledge graph and its dynamic optimization crucial.

[0006] In the complex software and hardware ecosystem, knowledge graph reasoning and updating face the challenges of dynamic changes and real-time requirements. Traditional static graph methods struggle to capture the temporal changes in node behavior and the dynamic reorganization of community structures, resulting in limited accuracy in knowledge reasoning and completion. Furthermore, ecosystem products suffer from difficulties in representing multi-source heterogeneous data and missing entity attributes. To address the need for dynamic attribute completion and adapt to constantly changing interfaces and data patterns, constructing a time-aware reasoning network becomes particularly important.

[0007] The rapid development of the domestic GPU ecosystem has led to significant dynamic evolution of hardware and software products during technological iterations. The coexistence of domestic GPU architectures such as Hygon, Suiyuan, and Tianshu has resulted in ecosystem fragmentation. Overcoming the challenges of tracking the entity state and predicting the evolution of heterogeneous products, by combining dynamic graph neural networks and multi-level temporal modeling techniques, has become a key hurdle. Therefore, it is urgent to address the problems of complex multi-source data structures, strong temporal dynamism, and difficulties in semantic alignment within the existing full-stack hardware and software integration technology ecosystem. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for heterogeneous data fusion within a full-stack GPU hardware and software ecosystem, comprising the following steps:

[0009] Step S1: Based on the GPU artifact knowledge graph, extract key entities and features, assign category representations, model multivariate relationships and optimize features, and model dynamic knowledge flow.

[0010] Step S2: Establish an entity state maintenance and knowledge evolution mechanism based on relational flow encoding of knowledge links, knowledge community discovery and evolution;

[0011] Step S3: Construct an ecological product map and a knowledge link temporal reasoning mechanism to predict the frequency of knowledge updates.

[0012] Beneficial effects:

[0013] 1. This invention utilizes a heterogeneous data feature decoupling and collaborative representation system to overcome the challenge of unified semantic mapping of multi-source heterogeneous data. By designing a spatiotemporally coupled relational reasoning framework, it achieves dynamic modeling and conflict resolution of software and hardware knowledge topology. At the same time, it develops an incremental knowledge evolution mechanism to adapt to the continuous technological ecosystem iteration needs such as computing unit expansion, instruction set upgrades, and toolchain plugin additions. Compared with traditional methods, it achieves deep integration of domestic GPU technical documentation, related algorithm libraries, and AI reasoning application cases.

[0014] 2. This invention utilizes a dynamic tracking system based on memory-enhanced graph neural networks to overcome the challenges of community evolution modeling driven by temporal decay and ensuring the long-term stability of knowledge links. By designing a bidirectional attention-driven compatibility transfer framework, it achieves cross-product interface adaptation and semantic aggregation of heterogeneous knowledge links. At the same time, it develops a knowledge evolution mechanism driven by causal reasoning chains. Compared with traditional methods, it adapts to the ecological needs of continuous evolution such as dynamic completion of knowledge communities, redundancy control, and optimization of uniform category systems, and provides interpretable and highly stable knowledge support for software and hardware collaboration.

[0015] 3. This invention utilizes cross-modal alignment and dual-channel feature aggregation mechanisms to achieve semantic fusion between API text descriptions and actual code, fundamentally solving the semantic gap problem. By employing gated graph attention and community prototype vector modeling, it enhances the expressive power of the knowledge graph structure. Combined with time-aware GRU and multi-hop message propagation mechanisms, it updates entity attributes and evolutionary states in real time, improving the timeliness and completeness of the knowledge graph. Compared with traditional methods, it can effectively capture the dynamic changes and complex dependencies of ecosystem products in the time dimension, enhancing the timeliness and inference accuracy of the knowledge graph. It supports ecosystem-level technology migration and architecture compatibility analysis, providing high-precision and interpretable decision support for the evolution of the domestic GPU ecosystem. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a heterogeneous data fusion method for a full-stack GPU hardware and software ecosystem according to the present invention.

[0017] Figure 2 A schematic diagram of dynamic knowledge flow modeling for the fusion method module of heterogeneous and heterogeneous data;

[0018] Figure 3 This is a schematic diagram of the architecture of a heterogeneous data fusion method;

[0019] Figure 4 A schematic diagram illustrating the unified knowledge graph framework structure;

[0020] Figure 5 This is a schematic diagram of the architecture of an inference mechanism based on a dynamic graph neural network.

[0021] Figure 6 This is a structural block diagram of a heterogeneous data fusion system for a full-stack GPU hardware and software ecosystem according to the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0023] Example 1

[0024] like Figure 1 As shown in the figure, an embodiment of the present invention provides a heterogeneous data fusion method for a full-stack GPU hardware and software ecosystem, comprising the following steps:

[0025] Step S1: Based on the GPU artifact knowledge graph, extract key entities and features, assign category representations, model multivariate relationships and optimize features, and model dynamic knowledge flow.

[0026] Step S2: Establish an entity state maintenance and knowledge evolution mechanism based on relational flow encoding of knowledge links, knowledge community discovery and evolution;

[0027] Step S3: Construct an ecological product map and a knowledge link temporal reasoning mechanism to predict the frequency of knowledge updates.

[0028] In one embodiment, step S1 above—based on the GPU artifact knowledge graph—specifically includes the extraction and category representation allocation of key entities and features, multivariate relationship modeling and feature optimization, and dynamic knowledge flow modeling, and includes:

[0029] Step S11: Extraction and category representation assignment of key entities and features: Traverse entity nodes in the GPU artifact knowledge graph, assign learnable semantic basis vectors according to their categories, form initial localization in the type topology, and map category labels to semantic vectors through a category encoding layer, including the following steps:

[0030] Step S111: Traverse the entity nodes in the GPU artifact knowledge graph, assign learnable semantic basis vectors according to their categories, form the initial localization in the type topology, and map the category labels to semantic vectors through the category encoding layer; for each entity node Its category representation is constructed as follows:

[0031] ;

[0032] in, Represents entity nodes A collection of category labels, Let be the mapping function of the category encoding layer, and d be the dimension of the category semantic basis vectors. For a learnable parameter matrix, For bias terms;

[0033] This step combines parameters to achieve multi-category semantic fusion, ensuring that the features of each category receive appropriate attention, capturing the type information of entities in the knowledge graph, and providing semantic localization for them.

[0034] Step S112: Encode the textual and numerical attributes of the entity nodes, specifically including: obtaining sentence vectors for textual attributes using the BERT-base model; performing dimensionality reduction on numerical data using Principal Component Analysis (PCA); concatenating numerical attributes after min-max normalization to finally generate a feature fingerprint with individual discriminative power; feature encoding of textual attributes. Feature encoding of numerical attributes They are shown below:

[0035] ;

[0036] ;

[0037] in, It is a whole sentence vector representation of text attributes, used to describe things such as "architecture description in hardware specifications" and "functional description in API documentation";

[0038] As a feature extractor, it uses readily available and powerful NLP models to solve the problem of text semantic understanding, ensuring that the semantics extracted from technical documents are accurate;

[0039] For special classification tokens in the BERT model, as a "summary" or "pooling" operation of the entire text input, a fixed-size sentence vector is generated;

[0040] To be with the entity The relevant sequence of original text attributes;

[0041] For the first One original numerical attribute;

[0042] The th after min-max normalization Numerical attributes;

[0043] For the first The minimum value of a numerical attribute in the training dataset or domain is used to determine the lower bound of scaling;

[0044] For the first The maximum value of a numerical attribute on the training dataset or domain is used to determine the upper limit of scaling;

[0045] Step S113: Feature stitching is achieved using a Multiple Perceptron (MLP).

[0046] ;

[0047] in, For entities The final characteristic fingerprint, The total number of all entities; Indicates a connection operation;

[0048] Step S114: Based on the complementary properties of the dual representations, perform representation fusion using one of the following two representation fusion schemes:

[0049] Option 1 uses static weight allocation, based on predefined rules and fixed weight settings, and presets the fusion ratio according to the entity type:

[0050] Option 2 introduces a multilayer perceptron as a gating network based on a dynamic attention mechanism. The weight parameters are optimized through backpropagation, and the fusion weights are adaptively calculated according to different contexts, as shown in the following formula:

[0051] ;

[0052] ;

[0053] in, This is a dynamic weight coefficient vector used to control the proportion of global and local features in the fusion process;

[0054] It is the sigmoid function;

[0055] It is a learnable parameter matrix; This is a gated bias term;

[0056] Represents global contextual features (or semantic association features), reflecting the overall structure or commonalities of categories;

[0057] It represents the local representation features (or individual features) of an entity, reflecting the unique information of a specific sample or node;

[0058] Indicates to The complementary weights ensure that the sum of the global and local feature weights is 1 during the fusion process, thereby achieving balanced fusion;

[0059] The final fused feature representation vector incorporates global semantic features. With local entity features Information;

[0060] This invention employs a dynamic attention mechanism, which adaptively adjusts the fusion weights based on the specific features of the entity, thereby better balancing the commonalities of categories and the individual characteristics.

[0061] Step S12: Multivariate Relation Modeling and Feature Optimization: Classify the edge relations in the GPU artifact knowledge graph and establish a relation type dictionary; train relation embeddings based on the TransR algorithm. For a relation triple, construct an energy function to measure the degree of matching between the head entity and the tail entity through the relation. Specifically, this includes the following steps:

[0062] Step S121: Multivariate Relationship Modeling and Feature Optimization: Classify the edge relationships in the GPU artifact knowledge graph and establish a relation type dictionary;

[0063] Step S122: In the feature mapping stage, the relation embedding is trained based on the TransR algorithm. For a relation triple... Its energy function Used to measure head entities With tail entity Through relationships The degree of matching is defined as follows:

[0064] ;

[0065] in, and Corresponding to the head entity respectively Tail-end entity Vector representation in entity embedding space aims to capture deep relationships between entities; The relation projection matrix is ​​used to project entities from the entity embedding space to the relation embedding space, so that the relations between different types of entities can be accurately modeled and distinguished. The square of the L2 norm;

[0066] Step S123: To enhance dynamic characteristics by introducing a time decay factor to address the temporal variation characteristics of certain relationships within the GPU ecosystem:

[0067] ;

[0068] in, The interval between the current time and the time the event occurred. The relationship half-life, This is the decay rate parameter;

[0069] Step S13: Dynamic Knowledge Flow Modeling: Standardize the information in the knowledge flow into a standard spatiotemporal representation of four tuples: {head entity, relation, tail entity, timestamp}, and introduce a time decay factor to characterize the temporal change characteristics of relations in the GPU ecosystem, thereby constructing a full-dimensional dynamic knowledge graph covering hardware design specifications, driver interface changes, and application adaptation solutions. Specifically, this includes the following steps:

[0070] Step S131: Based on the multi-granularity spatiotemporal coding system, design an event segmentation coding strategy to standardize the information in the knowledge flow into a standard spatiotemporal representation of a four-tuple {head entity, relation, tail entity, timestamp}. Construct a full-dimensional dynamic knowledge graph covering hardware design specifications, driver interface changes, and application adaptation schemes to capture the static structure of knowledge and incorporate temporal information.

[0071] ;

[0072] in, The head entity represents the subject or initiator in an event or relationship; The term "relation" is used to describe the specific semantic relationship between the head entity and the tail entity. Tail entities represent objects or recipients in events or relationships; A timestamp is used to record the point in time when the knowledge relationship was established or the event occurred. The dimension of the entity embedding vector, i.e. and The length of the vector; The dimension of the relation embedding vector, i.e. The length of the vector;

[0073] The above representation methods can capture the static structure of knowledge and incorporate temporal information, such as Figure 2 As shown.

[0074] Step S132: Introducing a heterogeneous time parser and the Time2Vec algorithm, the original timestamps are transformed into standardized time series representations with frequency and phase characteristics, accurately representing the uniqueness of each time point and identifying potential correlations between different time intervals. This further captures the periodic changes and nonlinear features in the time series. For a quaternion of timestamps... The standardized time-series information processing procedure is shown in the following formula:

[0075] ;

[0076] in, and Corresponding to the first Frequency and phase parameters of each frequency component The number of frequency components; The spatial dimension is a temporal feature;

[0077] The reconstructed quadruple is updated as follows:

[0078] ;

[0079] in, and These are the entity projection matrix and the relation projection matrix, used to unify the dimensions of each element in the quadruple to the latent space dimension. ;

[0080] Step S133: In the knowledge flow evolution modeling, a dual-channel spatiotemporal attention mechanism and an adaptive time-slicing strategy are introduced. By directly participating in weight calculation and propagating the temporal encoding, and combining GraphSAGE and Transformer decoders, the local and global dependencies of nodes are processed simultaneously, realizing spatial neighborhood feature capture and cross-time-slice dependency pattern mining, as shown in the following equation:

[0081] ;

[0082] ;

[0083] in, This indicates that from a fusion perspective, time... Neighbor nodes For the central node Attention weights; The function is used to normalize the attention scores of all neighbors into a probability distribution, ensuring that the sum of all weights is 1; For nodes The timestamp of the corresponding event The encoded vector; To query the projection matrix; The key projection matrix; This is the scaling factor; For nodes Intermediate features that aggregate information about its spatiotemporal neighbors; Represents the spatiotemporal neighborhood; For neighboring nodes eigenvectors; The projection matrix is ​​a value.

[0084] Step S134: Design a dynamic update formula with a time decay factor. By introducing the influence of the decay coefficient and time interval, the update process of node representation is optimized to ensure that the information at each time node can be properly evaluated and integrated.

[0085] ;

[0086] in, The attenuation coefficient; Representing entities At the present moment The old state vector; Indicates a time interval; Indicates time interval Vectorized encoding; These are learnable parameters; For layer normalization operation;

[0087] Step S135: Construct the loss function based on the elastic weight consolidation EWC algorithm This addresses the concept drift problem in incremental learning and, by introducing the diagonal elements of the Fisher information matrix and historical optimal parameters, reflects the sensitivity of the loss function to changes in model parameters and identifies the importance of parameters to existing knowledge.

[0088] ;

[0089] in, For EWC loss terms; This is a regularization strength hyperparameter that controls the importance of the EWC constraint term in the total loss function. For the Fisher information matrix One diagonal element; For the model in the first The current values ​​of the parameters; These are the optimal values ​​of the parameters after training.

[0090] In step S1, deep integration was performed based on domestic GPU technical documentation, related algorithm libraries, and AI inference application cases, achieving the following:

[0091] (1) Construct a feature decoupling and collaborative representation system for heterogeneous data to overcome the problem of unified semantic mapping of multi-source heterogeneous data;

[0092] (2) Design a spatiotemporally coupled relational reasoning framework to realize dynamic modeling and conflict resolution of software and hardware knowledge topology;

[0093] (3) Develop an incremental knowledge evolution mechanism to adapt to the continuous technological ecosystem iteration needs such as computing unit expansion, instruction set upgrades and toolchain plugin additions;

[0094] (4) Extraction of key entities and features and category representation allocation. In response to the problems of diverse products such as hardware specifications and performance test reports and heterogeneous data sources in the domestic GPU ecosystem, a dual representation mechanism based on category information and its own features is proposed. The structural rules and instance features are collaboratively expressed through the construction of product knowledge graph and multimodal feature encoding, thereby achieving dynamic updates and efficient representation learning.

[0095] (5) Multi-relationship modeling and feature optimization effectively capture diverse relationship types in the knowledge graph, such as "use" and "development", and establish a relationship type dictionary. Regularly review and update the definition of relationship types to ensure their accuracy and timeliness. On the other hand, it provides adaptive modeling for the dynamic changes of complex relationships in the GPU ecosystem and can respond to these changes in real time to maintain the accuracy and timeliness of the model, thereby providing support for the intelligent management and optimization of the ecosystem.

[0096] (6) Dynamic Knowledge Flow Modeling: To address the dual challenges of temporal information modeling and knowledge flow evolution, a dynamic knowledge fusion framework is proposed. This framework combines the temporal correlation of architectural evolution with the spatial dependency of interface changes, employs spatiotemporal coupled representation and online incremental learning methods, and supports continuous learning mechanisms by suppressing catastrophic forgetting. It utilizes cross-version spatiotemporal encoding and event-triggered segmented modeling to mine the evolutionary patterns of knowledge flow topology, and completes dynamic knowledge representation and updating through incremental parameter optimization and concept drift detection processing, ultimately enabling large-scale migration and updating of the GPU ecosystem in dynamic knowledge environments.

[0097] Figure 3 A schematic diagram of the architecture of the heterogeneous data fusion method is shown.

[0098] In one embodiment, step S2 above is an entity state maintenance and knowledge evolution mechanism, based on relational flow encoding of knowledge links, knowledge community discovery and evolution, specifically including:

[0099] Step S21: Entity State Maintenance and Knowledge Evolution Mechanism: Construct a memory-enhanced graph neural network framework, combining adversarial generative networks and a bidirectional attention-driven mechanism to achieve dynamic tracking and evolutionary modeling of the entity state; fuse old and new states through gated recurrent units to capture the correlation between them, and adjust the strength of relationships between entities through an attention mechanism, thereby effectively realizing knowledge transfer and updating; construct a causal reasoning chain, dynamically adjust the strength of relationships between entities through a bidirectional attention mechanism, and use an attention-based link aggregation method to weightedly aggregate knowledge quadruples to generate link representations, specifically including the following steps:

[0100] Step S211: Assume at time... ,entity status Based on previous moments The state and the current input feature vector The result of the update is:

[0101] ;

[0102] in, It is a gated loop unit function. It is a physical entity At any moment state, It is a physical entity At any moment The input feature vector;

[0103] Step S212: Construct a causal reasoning chain, enabling the bidirectional attention mechanism to dynamically adjust the strength of relationships between entities, further enhancing reasoning ability and the strength of relationships between entities. Dynamically adjust using the following formula:

[0104] ;

[0105] in, It is the attention weight matrix. and It is a physical entity and entity At any moment The state vector; A learnable attention vector;

[0106] Step S22: Relational Flow Encoding Based on Knowledge Links: Entity encoding adopts a context-based embedding method, and relation encoding combines semantic role labeling to capture the directionality and semantics of relations. Through bidirectional encoding, the semantic information in the knowledge quadruples is represented more comprehensively. An attention-based link aggregation method is designed, which aggregates the encoding results of each quadruple by weighting. By constructing a multi-layer Perception-LSTM network, the dynamic transfer and updating of entity knowledge in the link is realized. The knowledge link is updated by optimizing the objective function. Specifically, the steps are as follows:

[0107] Step S221: Entity encoding employs a context-based embedding method, while relation encoding combines semantic role labeling to capture the directionality and semantics of relations. Through bidirectional encoding, the semantic information in the knowledge quadruple is represented more comprehensively.

[0108] ;

[0109] ;

[0110] in, and These are the encoding functions for entities and relations, respectively. and These represent the encoded entity and relation representations, respectively, which together constitute an indivisible semantic unit;

[0111] Step S222: Design an attention-based link aggregation method. The aggregation process can be represented as follows: Given a set containing n quadruplet codes: Calculate attention score:

[0112] ;

[0113] Normalization yields the attention weights:

[0114] ;

[0115] Perform weighted aggregation:

[0116] ;

[0117] in, For learnable attention vectors, For the first Attention score of each quadruple; Leaking linear rectifier function;

[0118] For the first Attention weights normalized to 4 tuples;

[0119] This represents the aggregated link;

[0120] Step S223: By constructing a multi-layer Perception-LSTM network, dynamic transfer and updating of entity knowledge in the knowledge link are achieved. The knowledge link update is realized by optimizing the following objective function:

[0121] Knowledge graph completion:

[0122] ;

[0123] in, This represents the number of training samples; For the model of quadruplets The score is determined by the scoring function. Positive samples; The sigmoid function maps scores to probabilities.

[0124] Temporal link prediction loss:

[0125] ;

[0126] in, In order to be in The set of new links that actually appear at time 1; (·) represents the probability of this link occurring as predicted by the model. Historical data;

[0127] Entity state consistency loss:

[0128] ;

[0129] in, For entities At any moment The state vector; The square of the L2 norm;

[0130] Construction update loss It is used to measure the difference between the updated knowledge representation and the true value;

[0131] ;

[0132] Then we have:

[0133] ;

[0134] in, This is a regularization term to prevent the model from overfitting. For hyperparameters; The objective function is...

[0135] Step S23: Knowledge Community Discovery and Evolution: Based on the semantic features and interaction behavior similarity of nodes, a modularity optimization algorithm is used to divide the initial community, and a sliding time window mechanism is introduced to dynamically adjust the community members and their feature representations through time-series interaction data to reflect the evolution of group preferences over time; and the semantic association between members is enhanced through the message propagation mechanism within the community, specifically including the following steps:

[0136] Step S231: Based on the semantic features and interaction behavior similarity of nodes, the initial community is divided using a modularity optimization algorithm.

[0137] ;

[0138] in, For adjacency matrix elements, For nodes The degree, The total number of sides, Represents a node and Do they belong to the same community?

[0139] By maximizing the modularity Q, community structures with tight internal connections and sparse external connections can be identified;

[0140] Step S232: Introduce a sliding time window mechanism. By analyzing the temporal interaction data of nodes, dynamically adjust the members and their feature representations of these communities to reflect changes in group preferences over time. Node similarity decays over time, i.e.:

[0141] ;

[0142] in, Represents a node and In time similarity, For nodes In time Embedded vector, [0, 1] represents the historical weight decay factor; if the similarity between nodes is below the threshold... If so, it will be migrated to a more suitable community, thus achieving dynamic adjustment of the community structure;

[0143] Step S233: Based on the community's message propagation mechanism, information is disseminated from one node to the entire community, enhancing connections among community members and promoting knowledge diffusion and sharing, namely:

[0144] ;

[0145] Among them, attention coefficient By node right The importance was calculated. for The neighboring nodes; For the first Layer-learnable linear transformation weight matrix; Represents a node In the The input feature vector of the layer; This refers to the index of the graph neural network layer;

[0146] Step S234: Design a joint loss function by combining node features and community structure information to further optimize the dynamic reasoning process of the knowledge graph. The joint loss function Loss is shown below. By minimizing... Simultaneously, optimize the graph completion accuracy and the representation similarity of nodes within the community:

[0147] ;

[0148] in, For triples gather, For the community to gather, As a balance factor; For entities The embedding vector.

[0149] In step S2, dynamic optimization based on the unified knowledge graph was performed, achieving the following:

[0150] (1) Construct a dynamic tracking system based on memory-enhanced graph neural networks to overcome the challenges of community evolution modeling driven by temporal decay and ensuring the long-term stability of knowledge links;

[0151] (2) Design a bidirectional attention-driven compatibility transfer framework to achieve cross-product interface adaptation and semantic aggregation of heterogeneous knowledge links;

[0152] (3) Develop a knowledge evolution mechanism driven by causal reasoning chain to adapt to the ecological needs of continuous evolution such as dynamic completion of knowledge community, redundancy control and optimization of uniform category system, and provide interpretable and highly stable knowledge support for software and hardware collaboration;

[0153] (4) Entity state maintenance and knowledge evolution mechanism: Construct a memory-enhanced graph neural network framework, combine adversarial generative network and bidirectional attention-driven mechanism, capture the relationship between entities, accurately track the state changes of each entity, and realize the dynamic tracking and evolution modeling of the product entity state;

[0154] (5) Relationship flow encoding: This invention proposes a relationship flow encoding method based on knowledge links, which decomposes complex relationship flows into multiple knowledge links and captures dynamic relationships between entities through the interaction between links. Entity encoding adopts a context-based embedding method, and relationship encoding is combined with semantic role labeling to capture the directionality and semantics of relationships.

[0155] (6) Knowledge community discovery and evolution: Based on the dynamic graph learning method of community detection, the knowledge community members and their feature representations are dynamically adjusted. The community detection module automatically discovers groups of nodes with similar interests. Through the combination of temporal interaction and community structure, the discovery and evolution modeling of knowledge communities is completed. A sliding time window mechanism is introduced, and a community-based message propagation mechanism is adopted. Through the evolution of community representation, a joint loss function is designed by combining node features and community structure information to further optimize the dynamic reasoning process of knowledge graph.

[0156] Figure 4 A schematic diagram of the unified knowledge graph framework structure is shown.

[0157] In one embodiment, step S3 above—constructing an ecological product atlas profile and a knowledge link temporal reasoning mechanism to predict the frequency of knowledge updates—specifically includes:

[0158] Step S31: Construct an ecological product atlas: A unified representation space is built using cross-modal alignment technology, integrating API semantic descriptions and code implementations. A dual-channel feature aggregation mechanism is employed to integrate neighborhood and community features, generating a globally enhanced feature representation of the entity. This includes the following steps:

[0159] Step S311: Construct a unified representation space through cross-modal alignment technology to facilitate comparison and analysis, for text-code heterogeneous data pairs consisting of API semantic descriptions and real-world code. Define the feature alignment loss function ,Right now:

[0160] ;

[0161] in, and It is a dual encoder, used to extract feature representations of the source domain and the target domain respectively, enhancing the consistency and comparability of cross-domain features; For expectation operators;

[0162] Step S312: Improve the model's expressiveness and generalization ability by designing a dual-channel feature aggregation and integrating data features from different sources. First, perform neighborhood feature extraction, and use a gated graph attention network to calculate entities. with neighbors Interaction weights :

[0163] ;

[0164] in, The strength of connections between entities based on supply chain relationships To query the transformation matrix; The key transformation matrix; The dimensions of the query vector and key vector; as the central entity The current feature vector;

[0165] Step S313: Aggregated local features Accurately characterize the behavioral patterns of entities in the technology dependency dimension, reflecting their interaction depth and role function in a specific technology ecosystem (such as the call frequency of CUDA kernel functions, the usage popularity of APIs in the TensorFlow framework):

[0166] ;

[0167] in, For entities Local aggregation features; For entities For entities Attention weights or relationship strength; Value transformation matrix; For neighboring entities The current feature vector, For entities The set of neighboring nodes;

[0168] Step S314: Community Feature Enhancement Stage. Based on the modularity maximization criterion, identify technology communities and construct community prototype vectors to represent community-level technology evolution paths, thereby capturing the trends and changes in technology development within the community.

[0169] ;

[0170] ;

[0171] in, For entities Belonging to the The weight or relationship strength of a technical community; Use the Sigmoid activation function; For entities Local aggregation features; For the first A prototype vector for a technical community; For entities Global enhancement features; These are learnable weight parameters; is the learnable weight vector; m is the total number of technical communities;

[0172] Step S32: Knowledge Link Temporal Reasoning Mechanism: Construct a multi-dimensional temporal feature matrix, extract local temporal features using a temporal convolutional network, update entity states through a dynamic graph attention mechanism, and predict the probability of knowledge link occurrence based on a community-enhanced link representation method; knowledge update frequency prediction includes: calculating the knowledge update intensity of entities using a sliding window mechanism, and modeling the temporal evolution characteristics of update frequency using a time decay factor, specifically including the following steps:

[0173] Step S321: Construct a multi-dimensional temporal feature matrix ,in For time step, To determine the number of entities, integrate the instruction set evolution record. Driver version update sequence and the popularity of community discussions Dynamic signals are extracted using a temporal convolutional network to capture local temporal features of single-item evolution.

[0174] Step S322: Design a temporal-spatial joint reasoning mechanism, updated using a dynamic graph attention mechanism:

[0175] ;

[0176] in, For normalization operations, For entities In the previous moment The state vector; For attention query vectors; The key transformation matrix; For neighboring entities In the previous moment The state vector; Value transformation matrix;

[0177] Step S323: For the knowledge link prediction task, a community-enhanced link representation method is proposed: for candidate links... Extract the dynamic representation of the community in which the entity resides, namely:

[0178] ;

[0179] in, For the first At this moment, the technical community The dynamic prototype vector; For gated loop unit; For the first The technical community at the last moment The prototype vector; For the community At any moment The collective average state;

[0180] By aggregating the current state and the state at the previous moment, this recursive process enables the model to capture the changing trends over time and use the feature information of other entities within the community to update the current state of the community.

[0181] Step S324: By fusing entity states and community characteristics through a gating mechanism, a link representation is generated as follows:

[0182] ;

[0183] in, and For relation-specific parameter matrix, The gating coefficient is used to adaptively adjust the community contribution.

[0184] Step S325: To transfer ecological knowledge (e.g., CUDA), design a domain adaptive regularization term that minimizes the source domain. With the target domain Potential spatial distribution differences:

[0185] ;

[0186] in, For domain-adaptive loss terms; This represents the maximum mean difference. It is a set of feature representations of source domain entities; A set of entity feature representations for the target domain;

[0187] Step S326: Quantize the probability of link occurrence through a time-aware prediction layer. By using the Sigmoid activation function, the model outputs the probability value of link occurrence:

[0188] ;

[0189] in, Given link characteristics The conditional probability that the link will then hold; It is a multilayer perceptron used to map link features (including factors such as time changes) to the final predicted value, thereby determining whether a link has occurred; Let T be the learnable weight parameter vector, and T be the transpose. The time interval between the current link prediction time and the most recent observation or update of the link; the entity state matrix is ​​dynamically updated based on the prediction results to achieve quantitative prediction of the impact of architecture upgrades on industrial software compatibility;

[0190] Step S33: Predict the frequency of knowledge updates, construct a knowledge state representation model, mathematically represent the state of knowledge entities using a dynamic graph neural network, construct a message passing mechanism with time decay characteristics, extract the temporal features of entities, calculate their knowledge update intensity using a sliding window mechanism, introduce a differentiable time decay mechanism and a hierarchical feature fusion strategy, and predict the frequency of knowledge updates. Specifically, this includes the following steps:

[0191] Step S331: Represent the state of knowledge entities mathematically using a dynamic graph neural network, and construct a message passing mechanism with time decay characteristics. Its state update process can be formalized as follows:

[0192] ;

[0193] in, Representing entities timestamp The hidden state, The time interval between adjacent events. The attenuation coefficient is a learnable factor. For the aggregation function of neighbor node states; For timestamp The learnable weight matrix;

[0194] Step S332: For the entity The temporal feature extraction employs a sliding window mechanism to calculate its knowledge update intensity, namely:

[0195] ;

[0196] in, For entities A collection of historical update moments, The length of the time window. For indicator functions, It is an exponential decay factor;

[0197] Step S333: Model the heterogeneous interactions among GPU ecosystem artifacts as a multi-dimensional relationship graph, and achieve entity alignment through a hierarchical attention mechanism:

[0198] ;

[0199] in , These are the feature matrices of the source entity and the target entity, respectively. This is the dimension scaling factor;

[0200] Step S334: The semantic information and temporal evolution features of heterogeneous entities are fused in a unified feature space, ultimately outputting three types of results, thus realizing a complete closed loop from heterogeneous data fusion to knowledge transfer.

[0201] Based on steps S132, S331, S314, and S323, a dynamic atlas profile for ecological products is constructed as follows:

[0202] ;

[0203] The probability prediction of the occurrence of a time-aware knowledge link obtained from step S326 is as follows:

[0204] ;

[0205] The knowledge update frequency prediction based on the time decay mechanism obtained from step S332 is as follows:

[0206] .

[0207] In step S3, the following was achieved based on the agile update mechanism of the dynamic graph neural network:

[0208] (1) Ecological product map profile construction, attribute completion is achieved by integrating neighborhood topological features and community evolution laws, enhancing the correlation and consistency between data, improving the completeness and reasoning ability of knowledge graph, and deeply mining the intrinsic connection of data through multi-level feature fusion and dynamic reasoning mechanism, solving the problem of multi-source heterogeneous data representation of ecological products, optimizing the generalization ability and stability of the model, and further improving the adaptability to complex environmental changes.

[0209] (2) Knowledge link temporal reasoning mechanism: Design a link prediction model based on spatiotemporal graph convolution. Its core is to achieve accurate knowledge link prediction through temporal entity state evolution modeling and community perception mechanism. By constructing a multi-dimensional temporal feature matrix, the current state and the state at the previous moment are aggregated, so that the model can capture the changing trend in the time series and use the feature information of other entities in the community to update the current community state. At the same time, it realizes the organic integration of entity temporal evolution law, community dynamic features and cross-entity interaction mode, effectively modeling the knowledge evolution path across time steps. The domain adaptive regularization term significantly reduces the semantic gap of cross-ecosystem knowledge transfer by minimizing the potential spatial distribution difference between the source domain (such as CUDA ecosystem) and the target domain (domestic GPU ecosystem).

[0210] (3) Knowledge update frequency prediction: Focusing on knowledge update frequency prediction, this paper addresses the problem of ecosystem fragmentation caused by the coexistence of domestic GPU architectures such as Hygon, Suiyuan, and Tianshu. By combining dynamic graph neural networks and multi-level temporal modeling technology, it tackles the key challenges of tracking and predicting the state of heterogeneous products. Through dynamic graph neural networks, the state of knowledge entities is mathematically represented, and a message transmission mechanism with time decay characteristics is constructed. While maintaining the core ideas of temporal evolution modeling and dynamic state update in the original technical route, the paper effectively improves the accuracy of knowledge update frequency prediction in the multi-source heterogeneous data scenario of the GPU ecosystem by introducing a differentiable time decay mechanism and a hierarchical feature fusion strategy. This provides key technical support for building an adaptive evolutionary ecosystem resource library.

[0211] Figure 5 A schematic diagram of the inference mechanism based on dynamic graph neural networks is shown.

[0212] This invention addresses the challenges of complex multi-source data structures, strong temporal dynamics, and semantic alignment difficulties in the full-stack technology ecosystem of hardware and software integration. It proposes a dual representation mechanism and a dynamic graph neural network inference framework. First, a heterogeneous entity representation system supporting the collaborative expression of category identifiers and individual features is constructed, combining static category encoding and dynamic feature fingerprints to generate multi-dimensional feature vectors. Second, an edge feature modeling method based on relational semantics is designed, introducing a time decay mechanism to capture the evolutionary characteristics of knowledge relationships. Then, a dynamic knowledge flow modeling framework is constructed, integrating spatiotemporal encoding, community detection, and causal inference mechanisms to dynamically track the state of artifacts and the evolutionary path of relationships. Finally, a dynamic graph neural network is used to achieve entity attribute completion and knowledge link prediction, improving the system's ability to understand and transfer complex knowledge in a dynamic ecosystem.

[0213] This research satisfies the need for heterogeneous data fusion methods for the full-stack ecosystem of domestic GPU software and hardware. It provides a breakthrough in semantic alignment and conflict resolution techniques for multi-source data, and establishes cross-modal data cleaning, entity extraction, and relationship recognition methods. These methods can be widely applied to scenarios such as GPU ecosystem migration, intelligent analysis of AI development toolchains, and dynamic knowledge graph updates, and have good scalability and practical value.

[0214] Example 2

[0215] like Figure 6 As shown, this embodiment of the invention provides a heterogeneous data fusion system for a full-stack GPU hardware and software ecosystem, comprising the following modules:

[0216] The heterogeneous data fusion module 41 is used for extracting key entities and features and assigning category representations based on GPU artifact knowledge graphs, modeling multivariate relationships and optimizing features, and modeling dynamic knowledge flow.

[0217] The unified graph recognition framework construction module 42 is used for entity state maintenance and knowledge evolution mechanisms, based on relational flow encoding of knowledge links, knowledge community discovery and evolution;

[0218] The dynamic graph neural network reasoning module 43 is used to construct an ecological product map and a knowledge link temporal reasoning mechanism to predict the frequency of knowledge updates.

[0219] A heterogeneous data fusion device for a full-stack GPU hardware and software ecosystem includes one or more electronic devices, wherein the one or more electronic devices are used to implement a heterogeneous data fusion method for a full-stack GPU hardware and software ecosystem.

[0220] An electronic device includes: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors enable the one or more processors to implement a heterogeneous data fusion method for a full-stack GPU hardware and software ecosystem.

[0221] A computer-readable storage medium storing executable instructions that, when executed by a processor, enable the processor to implement a heterogeneous data fusion method for a full-stack GPU hardware and software ecosystem.

[0222] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a heterogeneous data fusion method for a full-stack GPU hardware and software ecosystem.

[0223] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for heterogeneous data fusion within a GPU hardware and software full-stack ecosystem, characterized in that, include: Step S1: Based on the GPU artifact knowledge graph, extract key entities and features, assign category representations, model multivariate relationships and optimize features, and model dynamic knowledge flow. Step S2: Entity state maintenance and knowledge evolution mechanism, based on relational flow encoding of knowledge links, knowledge community discovery and evolution; Step S3: Construct an ecological product map and a knowledge link temporal reasoning mechanism to predict the frequency of knowledge updates.

2. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, Step S1 involves the extraction of key entities and features, and the assignment of category representations, specifically including: The entity nodes in the GPU artifact knowledge graph are traversed, and learnable semantic basis vectors are assigned according to their categories to form the initial positioning in the type topology. The category labels are then mapped to semantic vectors through the category encoding layer.

3. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, The multivariate relationship modeling and feature optimization in step S1 specifically includes: The edge relationships in the GPU artifact knowledge graph are classified, and a relation type dictionary is established. Relation embeddings are trained based on the TransR algorithm. For a relation triple, an energy function is constructed to measure the degree of matching between the head entity and the tail entity through the relation.

4. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, The dynamic knowledge flow modeling in step S1 specifically includes: Information in the knowledge flow is standardized into a standard spatiotemporal representation of a quadruple: {head entity, relation, tail entity, timestamp}, and a time decay factor is introduced to characterize the temporal change characteristics of relations in the GPU ecosystem, thereby constructing a full-dimensional dynamic knowledge graph covering hardware design specifications, driver interface changes, and application adaptation solutions.

5. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, Step S2: Entity state maintenance and knowledge evolution mechanism, specifically includes: A memory-enhanced graph neural network framework is constructed, combining adversarial generative networks and a bidirectional attention-driven mechanism to achieve dynamic tracking and evolution modeling of the state of product entities. By fusing new and old states through gated recurrent units, the correlation between the new and old states is captured, and the strength of the relationship between entities is adjusted through an attention mechanism, thereby effectively realizing knowledge transfer and updating. A causal reasoning chain is constructed, and the strength of the relationship between entities is dynamically adjusted through a bidirectional attention mechanism. A link aggregation method based on the attention mechanism is used to weighted aggregate knowledge quadruples to generate link representations.

6. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, Step S2: Relationship flow encoding based on knowledge links specifically includes: Entity encoding employs a context-based embedding method, while relation encoding combines semantic role labeling to capture the directionality and semantics of relations. Through bidirectional encoding, it more comprehensively represents the semantic information in knowledge quadruplets. We design an attention-based link aggregation method by weighting and aggregating the encoding results of each quadruple; we construct a multi-layer Perception-LSTM network to realize the dynamic transfer and updating of entity knowledge in the link; and we optimize the objective function to achieve the updating of knowledge links.

7. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, Step S2: Knowledge Community Discovery and Evolution, specifically includes: Based on the semantic features and interaction behavior similarity of nodes, a modularity optimization algorithm is used to divide the initial community, and a sliding time window mechanism is introduced to dynamically adjust the community members and their feature representations through time-series interaction data to reflect the evolution of group preferences over time; and the semantic association between members is enhanced through the message propagation mechanism within the community.

8. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, Step S3: Constructing an ecological product atlas profile, specifically includes: A unified representation space is constructed by cross-modal alignment technology, which integrates API semantic description and code implementation. A dual-channel feature aggregation mechanism is used to integrate neighborhood features and community features to generate a global enhanced feature representation of entities.

9. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, Step S3: Knowledge Link Temporal Reasoning Mechanism, specifically includes: A multidimensional temporal feature matrix is ​​constructed, local temporal features are extracted by combining a temporal convolutional network, entity states are updated by a dynamic graph attention mechanism, and the probability of knowledge links is predicted based on a community-enhanced link representation method. The knowledge update frequency prediction includes: calculating the knowledge update intensity of entities using a sliding window mechanism, and modeling the temporal evolution characteristics of update frequency by combining a time decay factor.

10. The heterogeneous data fusion method for the GPU hardware and software full-stack ecosystem according to claim 1, characterized in that, Step S3: Predicting the frequency of knowledge updates, specifically including: A knowledge state representation model is constructed, and the state of knowledge entities is mathematically represented by a dynamic graph neural network. A message passing mechanism with time decay characteristics is constructed. For the extraction of the temporal features of entities, a sliding window mechanism is used to calculate the knowledge update intensity. A differentiable time decay mechanism and a hierarchical feature fusion strategy are introduced to predict the knowledge update frequency.

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