698 results about "Symmetric multiprocessor system" patented technology

The invention discloses a distributed training scheduling and communication optimization method and system of a multi-modal large model on a domestic computing power platform. The method comprises the following steps: virtualizing a heterogeneous computing unit of a preset platform into a virtual device pool, and fusing first-order gradient of a multi-modal sample and Hessian matrix information based on quantitative perception training to generate a sample sensitivity grading atlas; virtual device pool attributes and the sensitivity grading atlas are used as input, an optimal hybrid parallel configuration scheme is automatically generated through a configuration search algorithm, and a parallel combination mode, resource mapping and a high-sensitivity sample scheduling strategy are defined; a distributed training code of an integrated communication optimization strategy is automatically generated according to a configuration scheme, pipeline parallel communication and data parallel gradient synchronization constraint are executed in a topology adjacent equipment subset, and a hierarchical aggregation mechanism is adopted; and dynamically screening a core training set and scheduling a calculation task to complete distributed training. According to the method, efficient cooperative training of the multi-modal large model on the domestic computing power platform is realized.

The invention discloses a task scheduling method based on predictable resource state atlas modeling, a platform is oriented to a heterogeneous computing environment, a unified resource state atlas is constructed by collecting multi-dimensional resource state parameters of computing nodes, and performance characteristics and communication topological relations among the nodes are comprehensively described. On the basis, a bidirectional time sequence model and an attention mechanism are fused, and the load trend of each node in a future short time is predicted. The platform constructs a multi-factor scheduling scoring function based on a task feature vector and resource state prediction map, integrates parameters such as resource matching degree, prediction load, communication delay and energy consumption cost, dynamically evaluates the adaptability of tasks and resources, and realizes adaptive scheduling and optimal resource allocation of the tasks. Compared with the prior art, the method has the advantages of being high in resource state predictability, high in task allocation intelligence degree, outstanding in platform evolution capability and the like, and is suitable for intelligent task scheduling application in a large-scale heterogeneous resource environment.

The invention relates to the technical field of cloud edge collaborative computing power scheduling, and discloses a heterogeneous computing power scheduling optimization method based on a cloud edge collaborative architecture. The method comprises the following steps: acquiring real-time computing power state data of all available computing nodes in the cloud edge collaborative architecture; performing heterogeneous type division on the computing nodes according to the real-time computing power state data to generate a three-layer computing power resource pool containing cloud computing nodes, edge computing nodes and terminal computing nodes; extracting task calculation features for the current to-be-scheduled task set, wherein the features comprise calculation intensity, data dependence and real-time requirements; constructing an initial task allocation scheme based on the matching relationship between the task calculation features and the three-layer computing power resource pool; iteratively optimizing the initial scheme by adopting a dynamic load balancing strategy to generate a final task scheduling instruction; the instructions are distributed to the corresponding computing nodes to be executed, and computing power state changes in the task execution process are continuously monitored.

The invention discloses a heterogeneous computing power cooperative scheduling system and method for mixed precision training, and belongs to the technical field of artificial intelligence computing. The system comprises a computational graph analysis and operator portrait module which is used for analyzing and dividing a model computational graph and extracting operator features; the heterogeneous hardware capability sensing and matching module is used for managing performance files and real-time states of heterogeneous hardware in the cluster and matching optimal execution hardware for each calculation partition; and the data flow coordination and pipeline parallel controller is used for generating a global execution plan, managing cross-device data dependence and communication and calculating overlapping optimization execution efficiency through communication. According to the method, the problem of low scheduling efficiency of mixed precision training in a heterogeneous environment is solved, automatic and accurate mapping from a calculation task to heterogeneous hardware is realized, the training speed is remarkably improved, the training cost is reduced, and the overall resource utilization rate of a cluster is improved.

The invention provides a heterogeneous computing power resource pooling scheduling platform which comprises a task and resource tensor construction module, a scheduling decision module, a breakpoint state generation module and a rescheduling and recovery module. According to the platform, task requirements and node resources are modeled into a tensor structure in a unified mode, and accurate initial scheduling is achieved through a structured scoring function containing conditional penalty terms. In the task running process, interruption judgment is carried out based on the node dynamic state and the scheduling score, and a breakpoint state tensor containing an execution state is generated. When interruption occurs, a proper node is selected through compatibility screening, and task seamless migration and execution recovery are realized by utilizing the stored state. According to the method, the technical problems of non-uniform resource expression, lack of scene adaptability and incapability of realizing uninterrupted migration in a heterogeneous computing power environment are solved, and the resource utilization rate and the service continuity are remarkably improved.

The invention relates to a heterogeneous computing-based task scheduling method and device, a storage medium and equipment. The method comprises the following steps of: obtaining an inference task of a large language model; in the execution process of the reasoning task, at least based on the memory access intensity and the current reasoning stage, memory access intensive operators and calculation intensive operators in the reasoning task are recognized; allocating the memory access intensive operator to a first computing unit for executing a memory intensive task, and allocating a computing intensive operator to a second computing unit for executing a computing intensive task; obtaining estimated execution time of the two calculation units for executing the corresponding tasks and data transmission time between the two calculation units; according to the pre-estimated execution time and the data transmission time, the task starting moments of the first calculation unit and the second calculation unit are determined with the purpose of minimizing the overall execution delay of the reasoning task; and controlling the first calculation unit and the second calculation unit to asynchronously execute the corresponding tasks in parallel according to the task starting time.

The invention relates to the technical field of power system automation, and particularly discloses a cross-domain heterogeneous computing power real-time calling and unified scheduling system for the power industry, which comprises a computing power resource global sensing unit, a business demand dynamic modeling unit, a cross-domain unified scheduling decision unit and a task execution and feedback control unit, according to the method, heterogeneous computing power states are collected in real time, task requirements are dynamically modeled, a scheduling scheme is generated based on multi-objective optimization, and task execution monitoring and load balancing are realized by means of closed-loop feedback, so that the utilization efficiency of global computing power resources is improved, and the real-time performance and reliability of power business are guaranteed.

The invention discloses a heterogeneous computing power resource dynamic scheduling system and method based on multi-objective optimization, and the system is deployed in a digital ecological platform complex based on multivariate consensus and embedded intelligent management. Comprising a platform access module, a data acquisition and perception module, a multi-target modeling and optimization module, a dynamic scheduling and execution module and a fault-tolerant mechanism module. The system registers as a computing power scheduling service node through an intelligent contract interface, obtains and verifies the compliance of a computing task, collects heterogeneous computing power resource node state data, and generates a scheduling decision by dynamically adjusting the weight of a target function; evaluating task suitability by using an AMCU suitability scoring model, and executing a scheduling AMCU strategy; according to the method, the technical problems of low resource scheduling efficiency and poor fault-tolerant capability in a heterogeneous computing power environment are solved, and the system throughput and the resource utilization rate are improved.

The invention provides a large-model-oriented heterogeneous computing power adaptive compiling method, which comprises the following steps that: a user inputs a trained large model through a system interface, and a system front-end conversion module analyzes a computational graph of the model and converts the computational graph into an intermediate representation based on a unified operator description language (UDL); the system hardware sensing module automatically detects and extracts hardware feature fingerprints of at least one piece of target hardware; based on the unified operator description language UDL intermediate representation and the hardware feature fingerprint, automatically generating an optimization adaptation rule oriented to at least one piece of target hardware; wherein the basis of parameterized filling comprises specific parameters of hardware feature fingerprints and optimized attribute tags carried in an intermediate representation of a unified operator description language (UDL); and generating and deploying multiple back-end codes. The method has the beneficial effects that intelligent compiling based on hardware features can be realized, and the deployment efficiency and the operation performance of a large model in a complex heterogeneous computing power cluster are remarkably improved.

The invention relates to the technical field of computers, discloses a heterogeneous computing low-delay communication method and system, and aims to solve the problem of high delay caused by high communication protocol overhead, lack of dynamic scheduling collaboration, memory migration redundancy and non-uniform cross-node communication abstraction in existing heterogeneous computing. The method comprises the following steps: receiving a task scheduling request and analyzing a task dependency graph; tasks are dynamically allocated based on node loads and link states; rDMA, NVLink or PCIe straight-through protocols are adaptively selected according to node types to establish communication channels; zero-copy data exchange is realized through a shared memory mapping buffer area; hardware timestamps are utilized to synchronize feedback delays with PTP to optimize scheduling. The system comprises a heterogeneous computing node cluster, a unified communication scheduling controller, a low-delay communication protocol stack, a shared memory mapping buffer area and a communication delay sensing task distributor. According to the scheme, the communication delay is remarkably reduced, and the throughput and the task execution efficiency are improved.

The invention relates to the technical field of Internet of Things gateways, and discloses an Internet of Things gateway device and system based on edge computing, and the device comprises a hardware heterogeneous computing module and a multi-interface protocol adaptation module. The edge calculation local processing module is used for performing preprocessing, feature extraction and local decision on the acquired terminal data; the network redundancy and self-healing module is used for realizing accurate network disconnection detection, seamless switching of a main network and a standby network and equipment fault self-recovery through a multi-dimensional network health degree evaluation algorithm; the local autonomous storage module is used for realizing hierarchical storage of high-frequency data and network disconnection emergency data and seamless synchronization after network recovery through network disconnection data hierarchical caching and an incremental synchronization algorithm; and the cloud collaborative management module is used for realizing remote configuration, OTA upgrading and state monitoring. The method is high in integration, low in delay and fast in response, adapts to real-time scenes, saves bandwidth cost, improves cloud efficiency, is high in reliability, and realizes network disconnection and continuous operation.

The invention relates to the technical field of smart home distributed heterogeneous computing power cooperative reasoning methods, and particularly discloses a smart home distributed heterogeneous computing power cooperative reasoning method. The objective of the invention is to solve the problems that heterogeneous equipment in an existing smart home system is uneven in computing power utilization, depends on a central scheduling node, is difficult to ensure privacy security and is insufficient in dynamic adaptability. The method comprises the following steps: constructing a dynamic computing power portrait of household equipment and updating the dynamic computing power portrait in real time; performing semantic analysis and computational graph decomposition on the intelligent reasoning task to generate a fine-grained reasoning unit with computing power and delay constraints; based on a decentralized broadcast protocol and a weighted Hungary algorithm, the reasoning unit is matched to the optimal local device; executing cross-device collaborative reasoning through an encrypted publishing-subscribing mechanism; and finally aggregating a result and returning a log for optimization. According to the technical scheme, efficient cooperation of family heterogeneous computing power can be achieved, end-side reasoning delay is lower than 200 milliseconds, the comprehensive utilization rate of resources exceeds 85%, and meanwhile data privacy and system robustness are guaranteed.

The invention provides an edge gateway computing power sharing method based on an MQTT protocol. An edge terminal sends a computing power request to an edge gateway through the MQTT protocol, wherein the computing power request comprises a computing task type identifier and a priority identifier; after receiving the computing power request, the edge gateway dynamically schedules heterogeneous computing resources of a built-in neural network processing unit (NPU) and a built-in central processing unit (CPU) according to a computing task type identifier and a priority identifier; and the edge gateway executes a corresponding calculation task by using the scheduled calculation resource, feeds back a processing result to the edge terminal initiating the request through an MQTT protocol, and synchronizes own calculation power state information.

The invention discloses a heterogeneous computing network resource collaborative scheduling optimization method based on self-adaptive multi-agent, and aims to solve the scheduling problem caused by resource heterogeneity, load dynamics and task high concurrency in a heterogeneous computing network system. According to the method, cross-domain resource collaboration is realized by constructing three sub-domain adaptive agents of a computing resource domain, a network resource domain and a storage resource domain and a global collaboration layer. According to the method, a deep reinforcement learning algorithm and an 'LSTM + GNN' fusion model are integrated, and multi-target adaptive optimization of resource utilization rate, task time delay, service quality and energy consumption is achieved through closed-loop optimization of state perception, strategy generation, value evaluation and strategy updating. The heterogeneous computing network resource fine-grained sensing, cross-domain cooperative scheduling and multi-target dynamic optimization are realized, the resource utilization rate and the task completion rate are high, the service quality and the energy consumption performance are good, and the dynamic response capability and the overall performance of the heterogeneous computing network system are improved.

The invention discloses an intelligent collaborative scheduling system and method for a general computing and intelligent computing fusion scene, and relates to the technical field of computing power resource management and scheduling. In order to solve the problem that isomerous computing power resource islands are difficult to collaborate, the system comprises a computing power access layer used for executing specified operation on isomerous computing power resources through a standardized access template, abstracting the isomerous computing power resources into a unified logic computing power unit and registering the unified logic computing power unit into a computing power pool; the resource management layer is used for continuously monitoring and collecting static attributes and dynamic states of computing power resources to form a global resource real-time view; the business processing layer is used for receiving the business submitted by the user, analyzing and identifying the business demand, converting the business demand into a demand vector, and generating a dynamic arrangement scheme based on the real-time view and the multi-target strategy library; and the collaborative scheduling execution layer is used for converting the dynamic arrangement scheme into an instruction adaptive to various APIs and completing computing power resource allocation and service starting. According to the invention, unified management and intelligent cooperative scheduling of computing power resources can be realized.

The invention relates to the field of computing power scheduling, and discloses an artificial intelligence-based computing power scheduling method, which comprises the following steps of S1, full-dimensional data acquisition: acquiring hardware resource data, task operation data, user demand data and heterogeneous computing power characteristic data through distributed sensing nodes; s2, data processing and feature construction: preprocessing the data collected in the S1, extracting multi-dimensional features through feature engineering, and constructing a feature matrix adapted to an AI model; s3, computing power demand intelligent prediction: learning the feature data preprocessed in the S2 by using a federal deep learning model, and outputting time-phased and type-divided computing power demand prediction results; and S4, scheduling strategy optimization generation. Hardware resources, task operation, user requirements and heterogeneous computing power characteristic data are comprehensively collected by relying on edge-core two-level distributed sensing nodes, the collection frequency can be dynamically adjusted between 50ms and 1mi n through a self-adaptive algorithm, and real-time performance and resource economy are both considered.

The invention discloses a deterministic heterogeneous computing task chain system and method for high-speed printing. The system is formed by an acquisition preprocessing unit, an intelligent analysis unit and a real-time decision-making unit through a deterministic task chain interconnection structure. The interconnection structure directly maps the task completion state of the upstream unit into a hardware signal (a state signal and an interrupt signal) which can be sensed by the downstream unit. The intelligent analysis unit monitors a state signal through hardware logic and automatically triggers calculation, and the real-time decision-making unit directly starts a control task through hardware interruption. A central scheduler is abandoned, a self-driven deterministic processing assembly line is formed through hardware-level direct state sensing and triggering, high-precision task switching precision and total system delay are completely determined, and the requirement of high-speed printing for hard real-time performance is met while heterogeneous computing power is provided.

The invention discloses a cache maintenance system of a heterogeneous computing system and electronic equipment, and relates to the technical field of data processing, a first directory controller manages the consistency state of a host end, and a second directory controller manages the consistency state of an equipment end. The consistency maintenance operation is firstly efficiently processed by the corresponding directory controller in the storage domain, cross-device frequent coordination communication is reduced, and when cross-domain data access occurs, the two directory controllers cooperate through an established communication coupling mechanism and execute global consistency maintenance as required. The method not only can adapt to a high-burst and high-parallel access mode of a device end, but also can avoid direct conflicts with a cache management mechanism of a host end, and realizes global consistency through distributed collaboration. The technical problem of performance bottleneck of cache consistency in the heterogeneous system is solved, and the technical effect that the overall data access efficiency of the heterogeneous system is improved while the correctness is maintained is achieved.

The invention discloses a multi-machine multi-card distributed training optimization system and method oriented to a credential heterogeneous environment, and relates to the technical field of distributed training. In order to solve the defects existing in distributed training in a credential and credential heterogeneous environment, the system comprises an application layer for receiving a training task; the core system layer comprises a self-adaptive task scheduling module, a hybrid communication topological optimization module and a dynamic check point and elastic recovery control module, and the scheduling module generates a performance portrait and an optimal scheme based on a training task and adjusts the scheme according to a node fault; the optimization module executes the scheme, firstly detects node topology, constructs a hierarchical path, loads and initializes an optimal communication rear end for target equipment, and synchronizes the path to the target equipment; the control module periodically stores a complete training context and realizes reliable recovery of a training process; the credential hardware abstraction layer is used for providing a uniform interface for realizing underlying resource access and data acquisition; and the hardware resource layer comprises heterogeneous computing nodes and an RoCE network switch.

The invention relates to a deterministic service-oriented resource scheduling method, apparatus and device, a medium and a program product. The method comprises the following steps: modeling a deterministic service into a directed acyclic graph; therefore, the processing flow of the deterministic service can be represented by the directed acyclic graph, so that the intelligent agent can identify the execution overhead of each task on the computing node according to the directed acyclic graph, and a basis is provided for subsequent scheduling decisions. Determining a real-time state of each computing node in the heterogeneous computing environment; therefore, the current data processing capacity of each computing node in the complex heterogeneous computing environment can be mastered in real time, and the task load of each computing node can be balanced conveniently. And based on the directed acyclic graph corresponding to the deterministic service and the real-time state of each computing node, outputting a scheduling decision of the deterministic service through the trained agent. Therefore, computing and communication resources can be dispatched cooperatively under a unified converged network architecture, so that the computing and communication resources can adapt to a complex and changeable network environment, and an optimal dispatching decision is output.

The invention discloses an optical remote sensing ground feature relationship semantic understanding system and method in a localized environment, and the system constructs a remote sensing scene relationship analysis module, a multi-modal scene knowledge base module, a remote sensing scene graph construction module, a remote sensing scene representation module and a multi-modal sample pair construction module. Carrying out programmed modeling on the ground feature relationship by utilizing a code big language model adaptive to a domestic platform, and generating a scene graph triple; through a double-branch double-time phase comparison learning network, time sequence vision and structure knowledge are fused, and cross-modal joint representation learning is realized. According to the method, the problems of incomplete surface feature relationship expression, inaccurate modeling and lack of real-time semantics in the prior art are solved, deep optimization is carried out in the aspects of data annotation, heterogeneous computing power management and the like aiming at the localized environment, and the accuracy and integrity of semantic understanding of the surface feature relationship and the operation efficiency of the semantic understanding on a domestic software and hardware platform are remarkably improved.

The invention discloses a cloud data server mixed training method and system, which are applied to a cloud data server cluster comprising a plurality of computing resources. According to the method, a model structure, a data set, a time delay constraint and an isolation constraint of a training task are analyzed to generate a task portrait, resource configuration, an operation state and an isolation capability of a calculation node are collected to generate a resource portrait, and a co-located interference degree index table of a task and resource combination is constructed based on historical monitoring data. During scheduling, a heterogeneous computing power utilization rate, an estimated training time delay deviation and a co-located interference degree are taken as indexes, a candidate resource allocation scheme is subjected to weighted evaluation to generate a mixed training scheduling strategy, and resource isolation is implemented through container and accelerator multi-instance division. In the operation process, the training time delay and the actual co-located interference degree are continuously monitored, the scheduling weight and the co-located interference degree index are dynamically adjusted according to the deviation, closed-loop optimization of multi-task mixed training is achieved, the heterogeneous resource utilization rate is increased, and time delay default and co-located interference are reduced.

The invention discloses a vehicle emission identification system and method based on artificial intelligence, and relates to the technical field of vehicle emission detection. The snapshot recognition module is provided with a multispectral camera array and a synchronous controller, dynamically adjusts imaging parameters in combination with a laser radar and a millimeter wave radar, and supports multi-type vehicle recognition; the AI algorithm processing module adopts a heterogeneous computing power architecture to allocate resources; the vehicle anomaly judgment module constructs a multi-dimensional anomaly feature chain, calls OBD data to study and judge anomaly and identifies blacklist vehicles; the data storage and calling module adopts block chain evidence storage and hierarchical storage; the mobile law enforcement adaptation module supports end-side cloud collaboration; the multi-source data fusion module realizes data space-time alignment; the dynamic model self-updating module optimizes the model based on federal learning. The method improves the comprehensiveness of vehicle identification and abnormity determination, guarantees the law enforcement data to be legal and credible, relieves the computing force pressure of a mobile terminal, supports model continuous adaptation technology iteration, assists efficient mobile law enforcement, and provides powerful support for environmental protection law enforcement.

The invention relates to the technical field of computer server resource management and network virtualization, and particularly discloses a server hybrid deployment and management system based on a virtualization technology, which comprises a uniform resource abstraction layer, a global resource sensing and modeling unit, a strategy-driven intelligent arrangement engine and a heterogeneous resource execution adapter. The uniform resource abstraction layer performs standardized abstraction on a physical server, a virtual machine and a container; the global resource sensing and modeling unit integrates the resource data and constructs a global resource model; a strategy-driven intelligent arrangement engine performs multi-objective optimization solution based on strategies and constraints to generate a scheduling scheme; the heterogeneous resource execution adapter translates the scheme into bottom executable atomic operations. According to the invention, unified management and intelligent cooperative scheduling of heterogeneous computing resources are realized, and the resource utilization efficiency and the system automation level are improved.

The invention relates to the technical field of cloud fusion application operation support platforms, and discloses a session flow intelligent scheduling method under a fusion communication architecture, which comprises the following steps: monitoring a session flow logic sequence number of a current node; constructing an asynchronous mirror instance at the target node, and obtaining a computing power comparison parameter; adjusting an asynchronous mirror instance instruction execution rate to execute serial number chasing; after the sequence number difference value enters a synchronous threshold value, processor assembly line branch prediction data is injected, and instruction prefetching sequence filling is driven; according to the instruction level feature injection and execution phase alignment method, hardware execution momentum imbalance in a heterogeneous computing power environment is eliminated through instruction level feature injection and execution phase alignment, and progress connection and logic consistency of session streams at a migration interface are guaranteed.

The invention discloses a heterogeneous computing power resource allocation method and system, and belongs to the technical field of computing power scheduling. The method comprises the following steps: analyzing an edge task through a content value analysis model, and generating a task label containing real-time, accuracy and exploratory demand scores; based on the task labels, a distributed computing power distribution protocol is utilized to match and distribute computing power resources for the tasks, and primary distribution and execution are completed; in the task execution process, the data value is evaluated through the lightweight evaluation model; if the evaluation value exceeds a dynamic threshold value, a computing power scheduling model agent is triggered to perform centralized secondary distribution, and the agent decides an optimal uploading path based on deep reinforcement learning and adjusts the threshold value so as to efficiently upload high-value data to a headquarter cloud and drive a large model to autonomously evolve. Through the double-track parallel architecture, the resource conflict between the real-time guarantee of the edge task and the evolution of the cloud model is solved, and the efficient and self-adaptive allocation of the computing power resource is realized.

The invention is suitable for the technical field of computers, and provides a CPU and GPU cooperative work heterogeneous computing method and platform, and the method comprises the following steps: S1, carrying out the meta-task analysis of an input computing task, extracting the computing feature metadata of the computing task, and predicting the performance of the computing task based on a pre-trained performance prediction model; dynamically deciding execution path planning of the task between the CPU and the GPU; s2, according to the execution path planning, carrying out adaptive resource collaborative configuration; and S3, on the basis of the calculation feature metadata and the current hardware state, through a parameterized kernel template or a just-in-time compilation technology, heterogeneous perception optimized kernel codes are generated. The method effectively solves the problems that a task scheduling strategy is rigid, the bottleneck of memory and data transmission is prominent, calculation kernel optimization is insufficient and is lack of adaptability, and a system is lack of self-evolution and learning ability.

The invention relates to the technical field of new energy management and edge computing, and particularly discloses a heterogeneous computing power aware new energy cluster collaborative scheduling method and system. According to the method, information such as static attributes and dynamic loads of various energy devices and control units in a cluster is collected through a lightweight probe, and a dynamically updated heterogeneous computing power resource portrait is constructed after processing; analyzing the structured scheduling request, and combining a cluster physical layout to construct a space-time-capability-dependency-computing power-energy consumption five-dimensional coupling adaptation degree model; searching an optimal scheduling plan through a hybrid optimization algorithm, wherein the optimal scheduling plan covers energy distribution, energy storage scheduling and transmission path planning; after the stability is verified by the digital twin sandbox, an instruction is issued; real-time monitoring is carried out during operation, and computing power-physical joint rescheduling is triggered. Intelligent perception, accurate prediction, simulation verification and dynamic collaborative optimization of the heterogeneous computing power resources of the new energy cluster are realized, and the overall efficiency, stability and adaptive capacity of an energy system are remarkably improved.

The invention relates to the technical field of distributed computing, in particular to a multivariate computing power intelligent scheduling management method and system. The multivariate computing power intelligent scheduling management method comprises the following steps: collecting original performance indexes of hardware, and uniformly abstracting the original performance indexes into a computing unit CU; maintaining a real-time directory of global resources, and realizing second-level weak consistent synchronization of resource views among the nodes; dynamically calculating a node health degree score; matching the task service labels with the static labels and the dynamic labels of the nodes to generate a candidate resource node set, and scheduling priority weights; calculating a comprehensive score of each candidate node, and performing redundant task deployment; and predicting a resource demand, and performing judgment by applying a scaling decision tree to realize resource allocation and scaling. According to the multivariate computing power intelligent scheduling management method and system, unified abstraction and management of heterogeneous computing power are realized, the intelligence and precision of resource scheduling decision are improved, the elastic expansion and fault-tolerant capability of the system is enhanced, and the maximization of the global resource utilization rate and energy efficiency is realized.

Systems and methods for routing manageability data in heterogeneous computing platforms. In some embodiments, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and an Out-of-Band (OOB) Microcontroller Unit (MCU), an Embedded Controller (EC), or a network device integrated into or coupled to the heterogeneous computing platform and distinct from any host processor of the heterogeneous computing platform, where the OOB MCU, EC, or network device is configured to receive a packet or command from a cloud service, and send the packet or command to a selected one of the plurality of devices.