681 results about "Execution time" patented technology

The invention discloses an intelligent resource scheduling method and system based on a dynamic data consanguinity atlas, and relates to the technical field of resource scheduling, the method comprises the following steps: collecting execution logs and flow metadata of tasks in a computing platform in real time, and constructing a dynamic directed weighted consanguinity atlas; calculating a blood relationship influence coefficient of each node in the dynamic directed weighted blood relationship map; obtaining a to-be-scheduled task, and calculating a comprehensive priority score based on the dependency weight of the dynamic blood relationship map of the to-be-scheduled task, the real-time load state of the target node and the scheduling execution time delay; performing priority ranking on the to-be-scheduled tasks based on the comprehensive priority score, and allocating cluster resources; and predicting the load trend of the target node, and triggering a migration decision when detecting that the predicted load of the target node exceeds a threshold value and the weight ratio of the key consanguinity tasks borne by the target node exceeds a preset threshold value. Through a dynamic consanguinity map and an intelligent scheduling algorithm, high efficiency and fairness of resource allocation are realized, the cluster utilization rate is improved, and task delay is reduced.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention provides a GPU heterogeneous computing resource allocation method and system based on task scheduling, and the method comprises the steps: carrying out the feature analysis of a to-be-scheduled task, so as to extract the calculation intensity, video memory demands, inter-task data dependence and historical execution data of the to-be-scheduled task, and obtaining a task feature vector; constructing a performance model of heterogeneous GPU resources to predict execution efficiency and resource consumption of the GPU under the target task based on hardware performance parameters and running states of the GPU; and matching the to-be-scheduled task with the GPU resources according to the task feature vector and the performance model of the heterogeneous GPU resources. Through task feature analysis and GPU resource performance modeling, the adaptive relation between tasks and GPU resources can be deeply mined, to-be-scheduled tasks are accurately allocated to the most appropriate GPU, the resource utilization rate is greatly increased, the task execution sequence and resource allocation are dynamically planned, and the task execution time is effectively shortened.

The invention discloses a calculation task allocation method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-executed calculation task, and extracting the features of the to-be-executed task to obtain a feature label; decomposing the to-be-executed calculation task based on the feature labels to obtain sub-tasks; constructing a directed acyclic graph based on the dependency matrix of each sub-task, and performing topological sorting on each sub-task to obtain a priority and an execution sequence; according to the method, a to-be-executed calculation task is decomposed into a plurality of sub-tasks through feature labeling, a directed acyclic graph is constructed according to the dependency relationship among the sub-tasks, and the sub-tasks are distributed according to the reference execution time, the priority and the execution sequence. The priority and the execution sequence of each sub-task are determined by utilizing topological sorting, and the sub-tasks are allocated to each GPU for execution in combination with the reference execution time of each sub-task on different GPUs, so that the calculation efficiency of the calculation task is effectively improved.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

The invention provides a timed task execution optimization method and system, and aims to solve the problems of inflexible static priority scheduling, unintelligent resource allocation and the like in the prior art. According to the method, a distributed timed task intelligent scheduling framework is constructed and comprises five core components including a task management console, a scheduling decision engine, a resource monitoring agent, a task execution cluster and metadata storage. Task basic information is obtained through task feature extraction, a dynamic priority score is calculated based on decision factors such as SLA urgency, resource matching degree and service weight, and task priority ranking is achieved. And performing intelligent task allocation by adopting a BestFit algorithm, and allocating the task to the optimal execution node. The system monitors the CPU utilization rate, the memory occupation and the IO waiting time of the nodes in real time, and when the CPU utilization rate exceeds 85%, an elastic resource allocation strategy is triggered. And when the task execution time exceeds the pre-estimated duration, performing task splitting and rescheduling based on a dynamic fragmentation algorithm.

The invention discloses a multi-queue scheduling method based on total and incremental reasoning overhead merging, which comprises the following steps of: firstly, calculating the total resource demand and memory overhead of a task according to the input length and the size of a predictor model in the process of performing total reasoning on a reasoning task; distributing tasks to queues with different priorities according to the total resource demand in combination with response time requirements and task importance; dynamically scheduling tasks according to a priority queue sequence, adjusting an execution sequence in combination with a system load, and preferentially executing tasks in a high-priority queue; distributing a time slice for each task, monitoring task execution time, reducing the priority of overtime tasks, moving the overtime tasks into a secondary priority queue, and triggering new task scheduling; and after preemptive scheduling, the key value cache of the latest scheduling task in the waiting queue is swapped from the accelerator memory to the CPU memory, and is swapped back to the accelerator memory before the task is re-executed.

The invention provides an SQL statement optimization suggestion generation method and device, a medium and electronic equipment, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring SQL log monitoring data, including an execution timestamp and a time consumption value of an SQL statement, dividing the data according to a preset time window, and constructing a to-be-processed data set; a median approximation algorithm is used for calculating the median of SQL execution time consumption, and then abnormal SQL statements in the running log are screened out. And for each abnormal SQL, reconstructing an execution context thereof, obtaining an abstract syntax tree and an execution plan, and substituting the abstract syntax tree and the execution plan into a preset cue word template to generate cue words. And finally, calling a large model to analyze the cue word so as to obtain an optimization suggestion for the abnormal SQL. By collecting and analyzing the SQL logs and combining the median algorithm and the large model technology, efficient SQL statement optimization is achieved.

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 discloses a multi-interface interactive training simulation method and system, a medium and a program product, and relates to the technical field of simulation training, and the method comprises the steps: determining a target training subject according to the selection of a student, determining a standard training procedure, displaying a virtual operation panel corresponding to the target training subject on a first display screen, a working principle animation corresponding to the target training subject is displayed on a second display screen, and a real-time simulation state corresponding to the target training subject is displayed on a third display screen; acquiring operation data of a student on the virtual operation panel in real time, matching the operation data with an operation step sequence in a standard training procedure, generating an operation instruction sequence, driving a working principle animation of the second display screen and a real-time simulation state of the third display screen, and recording actual execution time and operation deviation data; and inputting to a preset scoring model to obtain a final operation score, and generating a training report. According to the invention, pertinence of the trainee in simulated driving training can be improved.

Techniques for facilitating efficient polling using machine learning are disclosed. A system uses historical data associated with execution of tasks to train a machine learning model to predict execution times. After receiving a request for execution of a task, the system provides a polling configuration to the requesting device that includes a polling frequency based on a prediction for when the task execution will be completed. This prediction is generated by the machine learning model.

The invention discloses a task scheduling method and system based on an electric power big data model. The method comprises the following steps: S1, collecting and preprocessing electric power system data by utilizing the electric power big data model, calculating a data integrity index, and integrating high-integrity data; s2, on the basis of the integrated data, analyzing features of a task to be scheduled, calculating average execution time of the task and an expected value of resource consumption, and determining an execution difficulty coefficient of the task; s3, evaluating a system resource condition, calculating a resource utilization rate and a residual amount, and allocating resources according to a task execution difficulty coefficient; s4, optimizing the execution sequence and time arrangement of the tasks in combination with the task characteristics and the resource allocation condition, and formulating a task scheduling scheme; s5, executing the task, monitoring the task progress and the resource use condition, calculating the task execution progress deviation and the resource consumption change rate, and adjusting the scheduling scheme according to the monitoring result; according to the method, the reasonability, accuracy and overall operation efficiency of task scheduling in the power system can be effectively improved.

The invention discloses a use case testing method and device, electronic equipment and a medium, and relates to the technical field of computers. The method comprises the steps that multiple to-be-tested use cases are acquired; determining a comprehensive weight of each to-be-tested case according to the priority information, the function modification information, the historical test fault information and the execution duration corresponding to each to-be-tested case; according to the comprehensive weight corresponding to each to-be-tested case, determining a sorting sequence of the to-be-tested cases; and grouping the to-be-tested cases according to the sorting sequence, and distributing each group of to-be-tested cases to test environments with different test efficiencies for testing. According to the application, the comprehensive weight of each to-be-tested case during testing is determined according to the priority information, the function modification information, the historical test fault information and the execution duration of different to-be-tested cases, and the to-be-tested cases are distributed to test environments with different test efficiencies for testing based on the comprehensive weight, so that the test period is shortened, and the test efficiency is improved. The test efficiency is improved.

The invention discloses a cross-domain task scheduling strategy determination method and device, equipment and a storage medium. The method comprises the steps of obtaining a first calculation task and a first cluster parameter; and based on the initial scheduling strategy, simulating task allocation processing between clusters according to the first cluster parameter and the first calculation task, and determining a fitness function required by adopting the initial scheduling strategy. And for each task stage, evolutionary operation is performed on the task scheduling strategy corresponding to the minimum fitness function based on a genetic algorithm, the evolutionary task scheduling strategy of each task stage is obtained, the fitness function is determined, and the evolutionary operation of the task scheduling strategy is circularly executed. And when the condition is met, determining that the circulation is ended, and determining a target scheduling strategy according to the task scheduling strategy of the minimum fitness function of each task stage. According to the technical scheme, the optimal task load can be accurately allocated to each cluster, the task execution time can be effectively shortened, and the task execution efficiency is further improved.

Embodiments of the present application provide a method, apparatus, electronic device, and storage medium for managing an automated process. The method includes: obtaining time constraint information between two adjacent nodes in the automated process, where the time constraint information is used to constrain the execution time when performing a transfer operation on a target between two adjacent nodes; determining the execution order of the minimum sub-operations between two adjacent nodes according to the connection relationship between the two adjacent nodes in the automated process; determining the total time for performing the transfer operation between two adjacent nodes at least according to the execution order and the preset execution time of each minimum sub-operation; and verifying whether the time constraint information is reasonable according to the total time. This solution can ensure the effectiveness of the target in the automated process, thereby ensuring the accurate and smooth progress of the automated process.

The invention provides a timed task execution optimization method and system. The method comprises the following steps: forming a timed task execution optimization system architecture; determining a corresponding calculation factor to obtain a dynamic priority score; predicting the to-be-predicted task data by using the task prediction model to obtain a task execution time prediction value; analyzing the to-be-executed task queue, calculating the average waiting time, determining a multiple factor according to the task priority and the system load state, and identifying the task with the task execution time prediction value larger than the dynamic threshold value in the to-be-executed task queue as a long task; performing automatic fragmentation processing on the long task, generating a fragmentation result, determining a scheduling sequence based on the dynamic priority score and the task execution time prediction value, and generating a scheduling decision; and performing resource availability evaluation on the resource use data to obtain a node resource evaluation result, and allocating execution resources for the selected task. According to the method, the problems of efficiency and reliability of the timed task execution system in a high-load scene are solved.

The invention relates to a memory access adaptive self-attention mechanism for a transformer model. A method may include estimating a first execution time for selecting a number k of dominant data elements from an initial self-attention input matrix of a transformer model to generate a sparse self-attention input matrix; estimating a second execution time for performing a self-attention operation of the transformer model based on the sparse self-attention input matrix; estimating a third execution time for performing a self-attention operation based on the initial self-attention input matrix; and performing a self-attention operation based on the first execution time, the second execution time, and the third execution time.

The invention relates to the technical field of data processing, in particular to a data mining system based on artificial intelligence. According to the method, a finer computing resource regulation and control mechanism is constructed through links of inter-task correlation analysis, load task priority adjustment, computing cost evaluation, abnormal task identification, resource use trend analysis and the like. A topological structure is constructed for the relevance between the tasks through a graph neural network, and fuzzy matching is performed in combination with the computing resource sharing degree, so that automatic reasoning of the task dependency relationship is realized, and the intelligence of scheduling optimization is improved. The task priority adjustment adopts a mode of combining a CPU utilization rate and a calculation time proportion, load tasks are dynamically marked, a scheduling sequence is optimized in combination with a task time overlapping degree, and calculation resource conflicts are effectively reduced. According to calculation resource consumption analysis, task calculation load influence coefficients are calculated from multiple dimensions of task execution time, data reading amount and I / O operation times, and the reasonability of calculation resource allocation is improved.

The invention provides a model training method. The method comprises the steps of collecting information of a first model; and making a re-calculation strategy according to the communication time of the first model in the communication stage and the execution time required by the plurality of operators in the first model. The information of the first model comprises execution time respectively required by a plurality of operators in the first model, the recalculation strategy comprises at least one recalculation operator in the plurality of operators and opportunity for executing a recalculation process by the at least one recalculation operator, the recalculation operator is an operator used for executing the recalculation process in the plurality of operators, and the opportunity for executing the recalculation process by the at least one recalculation operator in the plurality of operators is the opportunity for executing the recalculation process by the at least one recalculation operator. The opportunity at which the at least one recalculation operator performs the recalculation process includes performing the recalculation process in parallel with the communication phase of the first model. The recalculation process is parallel to the communication process of the model, so that the model training time is shortened, and the throughput of model training is improved.

The invention provides a large language model application workload scheduling method, system and device, and relates to the technical field of model load scheduling, and the method comprises the steps: modeling a composite large language model application into a directed acyclic graph comprising a conventional stage, an LLM stage and a dynamic stage; modeling execution correlation among stages in the directed acyclic graph through a Bayesian network, dynamically predicting duration distribution of uncompleted stages, and calibrating a duration estimated value of an LLM stage in combination with a real-time batch processing size of an LLM executor; the uncertainty reduction amount of each ready stage is quantitatively scheduled based on information entropy; an epsilon-greedy strategy is adopted, and a JCT priority queue and an uncertainty reduction priority queue are combined to allocate scheduling resources; and assigning the task to a corresponding executor for execution, and repeating the above process until all operations are completed. The technical problem that the scheduling technology in the prior art is difficult to effectively deal with the execution time uncertainty and the structure uncertainty of the composite LLM application is solved.

The invention discloses a coroutine scheduling method, device, system and equipment, a storage medium and a product, and relates to the technical field of task scheduling, a multi-priority queue group is used for storing a first coroutine task to be executed in a corresponding scheduling domain, and a storage module is used for storing a second coroutine task periodically executed in a corresponding scheduling domain; the execution cycle of the first coroutine task is smaller than that of the second coroutine task. And when the expired second coroutine task exists in the storage module, mounting the expired second coroutine task to the multi-priority queue group. The situation that the first coroutine task with the low priority cannot be executed due to the fact that the second coroutine task does not reach the execution time is effectively avoided. And the coroutine tasks are obtained and executed from the multi-priority queue group according to the priority sequence, so that the tasks with high priorities are ensured to preferentially obtain execution resources. According to the running state and the load state of each scheduling domain, task migration of each scheduling domain is carried out, and the resource utilization rate is optimized.

The embodiment of the invention provides a compilation optimization method and electronic equipment, the method is executed by the electronic equipment, and the method comprises the steps that under the condition that starting of compilation optimization of an application is triggered, a plurality of decision parameters are obtained, and the decision parameters are related to execution time of compilation optimization; based on the multiple decision parameters, a target compiling optimization scheme adopted for compiling optimization is determined, and the target compiling optimization scheme comprises a full-amount compiling optimization scheme or an incremental compiling optimization scheme; and performing compilation optimization based on the target compilation optimization scheme. Therefore, when the electronic equipment starts compiling optimization of the application, the multiple decision parameters are obtained to comprehensively evaluate the target compiling optimization scheme adopted for compiling optimization, and the compiling optimization scheme with short execution time is selected to execute the compiling optimization task, so that the compiling time is optimized, and the running performance of the electronic equipment is improved.

The invention discloses a method and a system for asynchronous execution of JavaScript at a server side, and relates to the technical field of computer software, the method comprises the following steps: constructing a multi-language runtime context pool on the server side based on GraalVM, and pre-compiling a script and a core class; when the script is loaded, asynchronous execution strategy parameters are injected into the corresponding isolation context; calculating the fragmentation number of the overall task submitted by the script, and carrying out overall task fragmentation; mapping the fragments to the corresponding isolation assembly lines according to the fragment execution categories, constructing feature vectors according to the fragments in the isolation assembly lines, calculating fragment priorities, and executing in sequence; after fragment execution is completed, execution time delay is collected, historical time delay prediction is updated, and when conditions are met, isolation retry is carried out, and compensation fragments are generated. Through pre-compilation, asynchronous execution strategy, adaptive fragmentation and priority scheduling, and compensation fragmentation generation, fault self-recovery and service continuity guarantee with low time delay response, high scheduling flexibility and controllability and automation are realized.

The invention discloses a heterogeneous processor-oriented deep neural network reasoning task dynamic scheduling method and system, and belongs to the technical field of computer systems. The method comprises the steps that a DNN model is converted into a computational graph in a user space, and the computational graph is divided into a plurality of fine-grained micro-tasks through key path analysis; monitoring resource states of the CPU and the GPU in a kernel space in real time; dynamically selecting an optimal processor for each microtask in a kernel layer based on a cost model comprising execution time, queue length and utilization rate; and executing the micro-task on the corresponding processor according to a scheduling result, and triggering cross-processor task dynamic migration when detecting that the load is unbalanced, including context storage, data transmission and execution recovery. The system adopts a user space and kernel space collaborative architecture to realize the method. Through fine-grained division, kernel-level real-time scheduling and a dynamic migration mechanism, the reasoning efficiency, the resource utilization rate and the system stability of the heterogeneous system under a complex load are effectively improved.

The invention belongs to the technical field of computers, and particularly relates to a resource allocation method and device, equipment and a storage medium. The method comprises the following steps: S1, acquiring a target resource occupation task and real-time state data of a plurality of clusters; s2, calculating a cluster resource utilization rate based on the total resource configuration amount and the resource usage amount; calculating a residual resource proportion of the cluster based on the resource residual amount and the resource demand parameter; calculating a load sensitivity coefficient of the target resource occupation task based on the task priority, the resource demand parameter, an execution duration threshold and a task dependency identifier; and S3, screening the clusters according to preset screening conditions corresponding to different task guarantee levels, determining the clusters meeting the preset screening conditions as candidate clusters, monitoring in real time, triggering resource migration based on quantitative migration conditions, and forming a closed-loop process of data acquisition-index calculation-screening distribution-preemption coordination-monitoring migration.

The embodiment of the invention discloses a self-adaptive frequency modulation method and device which are used for improving the frequency modulation accuracy of a processor. The method comprises the steps that the computing device collects real-time monitoring data of the processor, the real-time monitoring data comprises one or more events monitored by a performance monitoring unit (PMU) of the processor, and the events comprise one or more of the following items: the number of instruction execution times, the cache hit rate and the branch prediction error rate. A predicted performance change rate of the processor is calculated based on the real-time monitoring data, the predicted performance change rate is determined based on a current performance change rate of the processor, the current performance change rate is determined based on the real-time monitoring data, and the predicted performance change rate is used for indicating the proportion of a reference performance value to an operation performance value of the processor. And determining a target frequency of the processor based on the target performance loss rate and the predicted performance change rate, wherein the target performance loss rate is used for indicating the predicted performance change rate tolerable by the processor.

The invention discloses a production line unit task allocation method based on reinforcement learning, which comprises the following steps: extracting a specific task instruction of each production line unit from a final task allocation matrix, verifying the feasibility of the instruction under the constraint of completion time through a simulation execution module, determining an instruction set passing verification, and performing task allocation on the instruction set; wherein the instruction set is obtained by fusing the solution of the multi-target conflict; according to the verified instruction set, production line feedback data such as actual execution time and energy consumption records are obtained, deviation is analyzed from the feedback data, if it is judged that the deviation is larger than a preset threshold value, a self-adaptive adjustment mechanism is triggered, and a corrected distribution strategy is obtained; and the optimized resource allocation indexes are extracted from the corrected allocation strategy, the indexes are pushed to the production line equipment through the real-time distribution system, the execution monitoring cycle after pushing is determined, and the monitoring cycle is obtained by continuously tracking the low-efficiency risk.

The invention relates to a real-time hybrid test control and test system and method based on a Linux-RT system and application, and belongs to the technical field of real-time hybrid tests of structural engineering. Comprising the following steps of: realizing deterministic conversion of states by adopting a real-time system manager of a singleton mode and an atomic state variable; according to the real-time executor based on the SCHEDFIFO scheduling strategy, affinity binding of a high-priority thread and a CPU is set, and periodic task scheduling of microsecond-level precision is achieved; a lambda functional programming data processing pipeline is adopted, and efficient data transmission of a filter chain is achieved through a zero copy mechanism; multi-protocol communication interfaces of UDP (User Datagram Protocol), EtherCAT (Ethernet Control Automation Technology) and WebSocket are supported, and a uniform abstraction layer is provided to realize low-delay data exchange between a numerical value and a physical substructure; and a real-time performance monitoring module adopting an exponential moving average algorithm provides accurate statistics of execution time and jitter.

The invention relates to an analog quantity selection method and system based on a Boolean switch, in particular to the field of industrial automation, and aims to directly realize signal mapping by adopting a pure hardware combinational logic circuit and thoroughly eliminate instruction execution time, so that the state change of a Boolean switch signal can be instantaneously reflected on analog quantity output; according to the method, real zero-delay switching is achieved, meanwhile, program adjusting algorithms in any form are avoided, disturbance phenomena such as overshoot or undershoot are completely eradicated from the principle, and high stability and accuracy of output signals are ensured. Finally, according to the technology, the control instruction can be accurately and synchronously issued without delay, and the response speed and the synchronization precision of the whole control system in a high dynamic response industrial scene are remarkably improved.

The invention discloses a task scheduling method and device for attention calculation, a medium, equipment and a product. The method comprises the steps that a loading task of a query block is decomposed into N1 loading subtasks, and a calculation task of a jth attention score block is decomposed into N2 calculation subtasks; decomposing an updating task of a middle accumulation block corresponding to the attention output block in the current iteration round into N3 parallel updating sub-tasks; asynchronously submitting all the subtasks to different task slots of N4 synchronization channels; and performing polling operation on the task slot in each synchronization channel to execute the sub-tasks stored in the task slot, and controlling an execution time sequence among the sub-tasks with the dependency relationship in each synchronization channel by configuring a number threshold value of the uncompleted sub-tasks in the waiting instruction. According to the method, the covering capability and the parallel computing capability of hardware can be improved, and the attention computing efficiency is further improved.