406 results about "Execution time" patented technology

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 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 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 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 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 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.

The invention discloses an integrated design management system with an automatic registration communication agent engine and a method thereof, and relates to the technical field of software integrated design simulation. The method has a heterogeneous integration breakthrough, the five communication modes are matched with the software without the API / command line / file, different heterogeneous software can support one communication mode, and the integration cost is remarkably reduced; dynamic expansibility is achieved, a newly added software module only needs to deploy a proxy engine and configure registration, and zero-shutdown upgrading of the platform is achieved; high concurrency reliability is achieved, resource conflicts are solved for an instance mechanism, and parallel task efficiency is improved; and full-process automation is achieved, and the cross-software design process execution time is obviously shortened.

The invention relates to the technical field of hardware utilization rate calculation, and discloses a method and device for determining the hardware utilization rate, equipment, a medium and a program product, and the method for determining the hardware utilization rate comprises the following steps: counting first total execution time and second total execution time of tasks executed on hardware, updating the first total execution time based on the first dynamic compensation time, and updating the second total execution time based on the second dynamic compensation time; the first dynamic compensation time and the second dynamic compensation time are used for compensating the execution time of the task executed on the hardware; and determining a hardware utilization rate in a specified time period by utilizing the updated first total execution time, the updated second total execution time and the specified time period. Compared with the prior art, the accuracy of determining the hardware utilization rate in the specified time period can be improved.

This prevents the system from inferring that the setting has been changed. [Solution] The performance control means executes a first performance related to a specific benefit when a specific benefit is granted, does not execute the first performance when a specific benefit is not granted as a result of the operation in the second operation mode, does not execute the first performance when the stop switch is operated in the second operation mode during a non-advantageous section and a specific benefit is granted in the next game, performs performances through multiple layers including a first layer and a second layer which is a higher layer than the first layer, executes the first performance in the first layer and executes a second performance which is different from the first performance in the second layer, and makes the execution timing of the first performance and the execution timing of the second performance different.

In an industrial processes, a properly instrumented line facilitates capture of data including detected steps, application input, and execution graph transitions, that permit the creation of empirical models of process timing. In this context, a process controlled by individual applications, e.g., at manufacturing workstations, provides a proxy for overall process timing by dividing a workflow into a number of discrete steps completed at each workstation, and further into any number of sub-steps, each controlled by a user and explicitly completed, e.g., by user interactions with widgets or other controls of the application. These applications provide a useful framework for modeling execution timing by providing an initial, implicit model for workflow (based on application control logic) that also facilitates automated detection of process sub-steps based on execution flow, as well as detection and measurement of the contributions of individual widgets and / or combinations of widgets to the process timing. By gathering data in this manner, mixed statistical distributions can be applied based on individual timing data for each possible sub-step, widget, process step, and the like performed with each application.

The invention discloses a GPU cluster resource allocation method and device, equipment and a storage medium, and relates to the technical field of GPU cluster resource allocation. According to the method, a hardware performance description vector is generated by fusing static hardware parameters and dynamic micro-benchmark test data, the limitation that the GPU performance is represented only by depending on the static parameters is broken through, and the actual operation capacity of different architecture GPUs in a heterogeneous cluster is accurately matched; task feature vectors are generated by extracting task calculation features and resource demands, and quantitative description of reasoning task resource consumption features is achieved; information of hardware, tasks and load dimensions is integrated through a machine learning model, performance degradation characteristics during multi-task parallel are effectively captured, and the accuracy of execution time prediction is improved; scheduling decision operation including candidate node screening, comprehensive cost evaluation and resource reservation backfilling is executed in combination with the predicted execution time, and the task execution efficiency and cluster load balancing are both considered.

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 relates to the technical field of heterogeneous computing, in particular to a prediction model dynamic scheduling system and method based on a heterogeneous computing platform. Running the prediction model in the edge host, and abstracting a calculation-intensive operator in the model into a candidate unloading task; and constructing a time cost model containing host execution time, FPGA execution time and transmission overhead, and dynamically updating in combination with execution statistics. And the scheduling module compares the predicted completion time of the local and unloading paths according to a time cost model, and selects to execute a prediction task on the host or the FPGA. A page locking DMA buffer area is arranged in a system memory, and the FPGA directly reads and writes historical data; and the FPGA side schedules tasks according to the priority and the deadline, and returns to a traditional closed-loop correction time cost model. Compared with the prior art, the method can dynamically adapt to illumination conditions and system load changes, reduces the average reasoning time delay and high quantile tail time delay of tasks, and improves the performance and stability of edge side photovoltaic power prediction.

The invention provides a method for executing a dynamic quantum circuit on a superconducting quantum computing device, a superconducting quantum computing system and a computer readable storage medium, and the method comprises the following steps: dividing the dynamic quantum circuit into at least one main circuit and at least one control sub-circuit, the main circuit is a circuit which is not influenced by a measurement result in the dynamic quantum circuit during execution, and the control sub-circuit is a circuit which is controlled by depending on the measurement result in the dynamic quantum circuit during execution; and respectively carrying out routing compiling on the at least one main line and the at least one control sub-line so as to sequentially execute the at least one main line after routing compiling and the control sub-line which is determined to be required to be executed after routing compiling on the superconducting quantum computing equipment according to the execution time sequence of the dynamic quantum line.

The invention provides a surface code-based quantum circuit fault-tolerant compiling method and a surface code-based quantum circuit fault-tolerant compiling device. The method comprises the following steps: acquiring an input set and a quantum circuit; aiming at each initial parameter combination, according to the corresponding bit block number parameter and the corresponding factory number parameter, estimating execution time required for executing the quantum circuit; for each initial parameter combination, generating line space-time cost information of the quantum line according to the corresponding bit block number parameter, the corresponding factory number parameter and the corresponding execution time; determining a target parameter combination from the plurality of initial parameter combinations based on the respective line space-time cost information of the plurality of initial parameter combinations; and according to the target parameter combination, mapping the logic bit of the quantum circuit to the physical space of the chip, and scheduling the quantum operation in the mapped physical space to obtain a physical execution sequence.

The invention provides a task flow dynamic optimization method and system based on data feedback, and the method comprises the steps: obtaining process feedback data, determining the process execution time consumption and a processing result through a production monitoring system log, recognizing the current processing capability of a process, and obtaining the to-be-processed workpiece amount of a downstream assembly node and the frequency fluctuation amplitude of an upstream process output workpiece; acquiring the queuing workpiece quantity and the real-time load quantity of each process corresponding to the rearranged process processing execution sequence, analyzing to obtain a process processing capacity matching degree index, and determining a global workpiece circulation stability evaluation result; and obtaining total number change data of workpieces completed in each process from the dynamic optimization implementation scheme, judging the system processing throughput improvement degree, and determining the process load balancing state of workpiece circulation.

The invention provides a service deployment method and a service deployment system based on Kubernetes. The method comprises the following steps: receiving a deployment instruction carrying an overall planning script package, wherein the overall planning script package comprises an overall planning file, a global configuration file and a plurality of sub-script packages; analyzing the overall arrangement file to determine the execution sequential logic of each sub-script package, and extracting a target configuration parameter from the global configuration file; the sub-script packages are scheduled in sequence, sub-script package description files in the sub-script packages are read, and instantiation deployment parameters are generated in combination with local parameters; analyzing an instruction definition file in the sub-script package to identify an extension instruction; and executing the instruction according to the instantiated parameters, and completing container deployment, database initialization and configuration issuing of business services by issuing an atomic operation instruction to a Kubernetes cluster. According to the method, a complex deployment process is abstracted into a unified arrangement and scheduling process, and the efficiency and reliability of service deployment are remarkably improved.

The invention relates to a computing power task execution method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps of obtaining task description data and task demand data of a target computing power task, and obtaining available computing power resources of an initial computing power cluster; inputting the task description data into a long and short term memory-Transform model, and outputting computing power resource demand data of the target computing power task, a prediction execution duration range and predicted consumption of power resources; determining at least one intermediate computing power cluster from the plurality of initial computing power clusters based on the task description data, the task demand data, the computing power resource demand data, the predicted execution duration range, the predicted consumption of the power resource and the available computing power resource; and screening out a target computing power cluster from the intermediate computing power cluster. According to the method provided by the invention, the waste of computing power resources and electric power resources can be avoided to the greatest extent, the cost can be saved as much as possible, and carbon emission is reduced.

The invention provides a home intelligent service mining method, a related device and a computer program product. The home intelligent service mining method comprises the steps that a home equipment action list is acquired, and the home equipment action list comprises equipment actions of multiple pieces of equipment in a home and execution time corresponding to the equipment actions; the household equipment action list is input into an intelligent service mining model, an equipment control sequence group is obtained, and the equipment control sequence group comprises preorder actions with the first execution time and postorder actions with the later execution time; and based on the device control sequence group, generating an intelligent service, the trigger condition of the intelligent service being a preorder action, and the trigger result of the intelligent service being automatically executing a subsequent action. The home intelligent service mining method can construct an application scene in a self-adaptive manner and accurately match actual use requirements.

The application discloses a blockchain task allocation method and device based on DQN, equipment and medium, relates to the field of task allocation, comprising: determining the state vector of the DQN model, selecting the target action according to the state vector, and determining the predicted execution time and the actual execution time of the target action; the predicted execution time and the actual execution time are used as the input of the preset time length reward formula to obtain the time length reward; a plurality of experiences are randomly extracted, the target Q value of the DQN model is determined based on the plurality of experiences, and the DQN model is updated through the observation Q value and the target Q value of the DQN model; multiple rounds of updating are performed until the DQN model meets the preset requirements to obtain the target DQN model; the target blockchain task allocation model is constructed through the target DQN model, and after receiving the blockchain task allocation request, the target blockchain task allocation model is used for intelligent task allocation. Therefore, the task allocation efficiency can be improved, the dynamic environment change can be responded to, and the manual intervention can be reduced.

The invention relates to the technical field of electronic data processing, and discloses a micro-service concurrent scheduling method and system based on DAG (Directed Acyclic Graphics) drive, which comprises the following steps: acquiring a directed acyclic graph, identifying a strong dependency side and a weak dependency side to carry out topological layering on the directed acyclic graph, and calculating a global parallelism factor; determining a prediction range, analyzing nodes in a subsequent main service layer in the prediction range, and generating a predictive resource demand vector; determining a target number of various service instances in the preheating pool; when the health degree attenuation value is lower than an activity threshold value, marking the idle instance as a to-be-refreshed state; calculating the scheduling affinity of each matching pair, and selecting the matching pair with the highest affinity to execute the task; if the execution time consumption exceeds the statistical standard, recording the type of the service node and the timeout amplitude as a path blocking event; the historical path blocking degree is updated and used for feeding back and adjusting the target number of the follow-up preheating pools. According to the method, the task can be matched to the optimal execution instance, and the scheduling decision quality is improved.

The invention discloses a data processing method and device in an assembly line, a medium, equipment and a product, and the method comprises the steps: obtaining a barrier identifier before the execution of a to-be-processed data stage in the current assembly line according to an assembly line identifier and a stage identifier of the current assembly line; comparing the completion signal count associated with the barrier identifier of the current assembly line with the expected signal number, and triggering the current assembly line to execute a to-be-processed data stage under the condition that the completion signal count is equal to the expected signal number; after the current assembly line finishes executing the to-be-processed data stage, obtaining a barrier identifier before executing the to-be-processed data stage in the next assembly line; and sending a completion signal to the synchronization barrier indicated by the barrier identifier of the next assembly line through the current assembly line, and updating the stage identifier of the current assembly line so as to enter a next data processing stage. According to the method, the execution time sequence of the assembly line can be accurately controlled, and more efficient and more accurate scheduling and utilization of hardware resources are realized.

The invention provides a resource scheduling method which can be applied to the fields of cloud computing and artificial intelligence. The resource scheduling method is applied to a service processing system containing heterogeneous computing resources and comprises the steps that service request load prediction information in a preset time period is obtained, the batch processing scale of each priority is determined by combining service priorities and a response time limit threshold value, and the batch processing scale of the high priority is smaller than the batch processing scale of the low priority; based on the batch processing scale and the memory consumption characteristic parameters, calculating the memory capacity required by the task, and screening candidate computing resources with sufficient available memories; according to the batch processing scale, the calculation feature parameters and the candidate resource performance parameters, calculation execution time delay and combination of queuing time delay, determining predicted completion time; and in the candidate resources meeting the response time limit threshold, selecting the resource with the smallest number of calculation units to execute the task. The invention further provides a resource scheduling device and equipment, a medium and a program product.

The invention discloses an SSD (Solid State Disk) back-end anomaly detection method and system. The method comprises the following steps: after a master control core releases reset of a rear-end core, starting a timer and monitoring a state flag, and synchronously monitoring a progress flag sequence written by the rear-end core; based on the sequence analysis execution time consumption, calculating the time consumption increase proportion of continuous steps to judge the health degree; triggering a predictive boot flow when a plurality of consecutive proportions exceed a threshold; the master core then executes an exception handling program to degrade the identified exceptional channels and chip enable units, including switching to a low speed mode, limiting write operations, and updating system resource mapping. According to the method, conversion from passive timeout to active prediction is realized, hardware exceptions can be found and isolated in an early stage, and the storage capacity is reserved to the maximum extent while the availability of the system is guaranteed.

The invention relates to the technical field of computing power resource allocation, and discloses an AI computing power resource dynamic allocation method and system, and the method comprises the steps: analyzing a resource state according to pre-obtained computing power resource state data, recognizing discretely distributed fragment resources, calculating a corresponding resource value, and constructing a fragment resource map; in response to a preset calculation task, configuring a fragment combination mechanism to obtain a fragment resource block; configuring a resource screening mechanism, dynamically screening out computing power resources which can meet the computing task after being combined with the fragment resource blocks, and obtaining a resource allocation scheme; scheduling the computing power resources based on the resource allocation scheme; by effectively utilizing the fragment resources, the overall resource utilization rate of the computing power resources can be increased, the task response speed can be increased, the fragment resources are actively combined to complete the corresponding computing tasks, and the task queuing and execution time is shortened, so that the dynamic scheduling efficiency and the task execution efficiency of the computing power resources are improved.

A simulation system, a method for simulating a simulation system, and a computer-readable data storage medium provide a simulation technique for more efficient program verification. The simulation system includes: a simulation unit that performs a simulation of the actions of one or more devices; a data input unit that acquires one or more execution times in the simulation, position information (252) of one or more objects in the simulation, and values ​​(254) of one or more variables referenced by a program (253) for causing one or more devices to operate; a data recording unit that associates and saves the position information (252) of one or more objects with the values ​​(254) of one or more variables as a log (250) for each of the one or more execution times; and a display unit that displays the position information (252) of one or more objects and the values ​​(254) of one or more variables for each of the one or more execution times contained in the first log (250).

The application discloses an intelligent resource scheduling method and system based on a dynamic data blood relationship graph, and relates to the technical field of resource scheduling.The method comprises the following steps: collecting the execution log and flow metadata of tasks in a computing platform in real time, and constructing a dynamic directed weighted blood relationship graph; calculating the blood relationship influence coefficient of each node in the dynamic directed weighted blood relationship graph; obtaining a to-be-scheduled task, and calculating a comprehensive priority score based on the dependency weight of the dynamic blood relationship graph of the to-be-scheduled task, the real-time load state of a target node and the scheduling execution time delay; performing priority sorting on the to-be-scheduled task 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 it is detected that the predicted load of the target node exceeds a threshold value and the key blood relationship task weight proportion borne by the target node exceeds a preset threshold value. Through the dynamic blood relationship graph and the intelligent scheduling algorithm, the efficiency and fairness of resource allocation are realized, the cluster utilization rate is improved, and the task delay is reduced.

This application discloses an accelerator card simulation method, apparatus, device, storage medium, and program product, relating to the field of accelerator card technology. The method includes obtaining multiple execution instructions corresponding to a target task; determining a first mapping relationship between the multiple execution instructions and multiple thread bundles of the target task; determining a second mapping relationship between the multiple thread bundles and multiple processors, the second mapping relationship including the identifiers of the multiple processors and the identifier of at least one thread bundle corresponding to each processor; and determining the predicted execution time of the target task by the accelerator card based on the first mapping relationship, the second mapping relationship, and the operating parameters of the multiple processors. The above method can improve simulation accuracy.