286 results about "Execution time" patented technology

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 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.

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 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 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 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 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.

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).

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.

The invention provides a task scheduling method, a scheduling system, a client and a computing device cluster, and the method comprises the steps that the scheduling system obtains information of a first computing task, the information of the first computing task comprises first execution time of the first computing task, and the first execution time is time under a first time system; and the scheduling system determines a first time period corresponding to the first calculation task from the plurality of time periods according to the first execution time, and schedules the first calculation task when the current time is in the first time period. Wherein the multiple time periods are different time periods under the second time system, the second time system and the first time system are different timing systems, the first execution time corresponds to second execution time under the second time system, and the second execution time is located in the first time period. According to the scheme, the management complexity of the calculation task can be reduced.

The invention relates to a task scheduling strategy generation method and device, a storage medium and a program product. The method can comprise the steps of obtaining task description information of a to-be-executed task of an agent with a body and scene information of a target scene; wherein the task description information comprises expected execution durations of at least two sub-tasks, and the target scene is an operation scene in which the agent with the body executes the task; on the basis of the task description information and the scene information, a task scheduling strategy of an agent executing tasks in the target scene is generated, the task scheduling strategy comprises the execution sequence of the at least two sub-tasks, and the execution time interval of at least part of the sub-tasks is overlapped with the execution time interval of other sub-tasks. According to the technical scheme, the task scheduling flexibility and the task execution efficiency can be improved, and the total task execution duration is shortened.

The application provides a method and device for recording retries in business processing, an electronic device and a storage medium, wherein the method comprises: obtaining an associated record related to a target task to be executed, wherein the associated record contains a current retry number of a method function corresponding to the target task, a current time, and an interval duration between the current time and a next execution time; storing the associated record to a first preset table and running the target task; and moving the associated record to a second preset table or a third preset table according to a target number obtained after scanning the associated record according to a running result and a target thread, wherein the target number is a value obtained by increasing the preset number to the current retry number after the method function is called the preset number of times based on the interval duration. Through the application, the problem of leaving no trace of retries and being unable to query the retry record in time in the subsequent process in the related art is solved.

The invention provides an application program execution method and system and a storage medium. The method comprises the steps of splitting an application program into a plurality of step functions by calling a plurality of preset functions, and obtaining corresponding message data based on a preset message queue; constructing a flow table and an application execution table based on the plurality of step functions and the message data; traversing the application execution table through the message data to obtain a corresponding flow table; and acquiring a corresponding step function based on the flow table so as to execute the step function in a time-sharing manner. According to the method provided by the invention, the technical problems that when the multi-task management is carried out in the real-time operating system, the execution time of the task is relatively long or the calculation complexity is relatively high, so that the thread is blocked, the blocked task occupies system resources, other tasks are delayed or stopped, and even the system function is abnormal are effectively solved; the complexity of the application program is effectively simplified, and the execution efficiency of the application program is improved.

The embodiment of the application provides a compilation optimization method and an electronic device. The method is executed by the electronic device and includes: in the case that the compilation optimization of an application is triggered to start, obtaining a plurality of decision parameters, the plurality of decision parameters being related to the execution time of the compilation optimization; based on the plurality of decision parameters, determining a target compilation optimization scheme adopted for the compilation optimization, the target compilation optimization scheme including a full-amount compilation optimization scheme or an incremental compilation optimization scheme; and based on the target compilation optimization scheme, performing the compilation optimization. Thus, when the electronic device starts the compilation optimization of the application, the target compilation optimization scheme adopted for the compilation optimization is comprehensively evaluated by obtaining the plurality of decision parameters, the compilation optimization scheme with shorter execution time is selected to execute the compilation optimization task, so that the compilation time is optimized and the running performance of the electronic device is improved.

This specification discloses a task scheduling method, apparatus, medium, and device. The method involves: acquiring task description information of a computational task; inputting the task description information into a pre-trained first prediction model to obtain a first execution time for the computational task output by the first prediction model; determining a resource scheduling strategy for the computational task based on the task description information and the first execution time; executing the computational task based on the resource scheduling strategy and the task description information; determining resource utilization information during the execution of the computational task and determining a second execution time for the computational task based on the resource utilization information; adjusting the resource scheduling strategy for the computational task based on the second execution time, and continuing to execute the computational task according to the adjusted resource scheduling strategy. By dynamically adjusting the resource scheduling strategy for the computational task, the utilization efficiency of computing resources and the execution efficiency of the computational task are improved.

The invention provides a multi-mode data processing method and device, electronic equipment and a storage medium, and relates to the technical field of computer software, and the method comprises the following steps: creating a plurality of data processing modes; creating and defining a plurality of basic data models, and configuring at least one data processing mode for each basic data model; configuring a plurality of data processing rules for each data processing mode under each basic data model; configuring a dependency relationship for the data processing rule; configuring a scheduling rule of the data processing mode in combination with the dependency relationship; setting a scheduling execution time plan; triggering execution scheduling according to the time plan, and generating a job execution chain according to the basic data model and the scheduling rule after triggering; and executing the data processing job according to the job execution chain. According to the method, the data processing modes can be dynamically switched according to service requirements in a configuration mode, the resource utilization rate and the processing efficiency are improved, the data reliability and consistency are guaranteed, and the practicability is high.

The invention discloses an access address configuration method, a processor, a multiprocessor system, a medium and a product. The method comprises the following steps: configuring an independent register block for each thread bundle in the processor; wherein the register group is used for storing coordinate information of tensor data used in the execution process of a corresponding thread bundle program; reading the coordinate information of the tensor data to be processed from the register group, and sending the read coordinate information to a coprocessor to carry out access address calculation of the corresponding tensor data; according to the method, each thread bundle is provided with a group of entity registers for storing the coordinate information of the tensor data, multiple multiplexing of the coordinate information can be supported, and a large number of instructions for specifying coordinates are saved, so that the number of assembly instructions is reduced, the instruction overhead is effectively reduced, the execution time of a processor is saved, and the processing efficiency of the processor is improved.

The invention relates to a method for filling a filling installation, comprising a step of receiving tasks to be performed and a set of resources to be used, a step of selecting available resources corresponding to the resources to be used, and of selecting the tasks to be performed and at least one production order such that the set of tasks to be performed of said at least one production order is selected, a step of defining a complete execution sequence of the selected tasks to be performed such that the time to perform each selected task to be performed is less than or equal to the available time of the selected available resources, and a step of filling at least a portion of the plurality of available containers according to the complete execution sequence. Abstract figure: Fig. 1

The invention provides an instruction processing method and device, electronic equipment, a storage medium and a chip, and relates to the technical field of computers. The method comprises the following steps: acquiring execution time information of a plurality of to-be-processed instruction sequences and position information of the plurality of to-be-processed instruction sequences; according to the execution time information and the position information, determining an execution sequence of the plurality of to-be-processed instruction sequences; and executing the plurality of to-be-processed instruction sequences according to the execution sequence. The execution time information reflects the time characteristic, the position reflects the space characteristic, the time characteristic and the space characteristic are considered at the same time, instruction execution better meets the actual requirement, and the instruction execution efficiency is improved.

The invention discloses an operator execution method and device, equipment, a storage medium and a product, and the method comprises the steps: applying for a preset number of buffer areas for a thread for executing an operator; wherein the preset number is greater than or equal to 3; for any buffer area, the target data elements in the local register array of the thread are sequentially sent into the buffer area through the thread to be accumulated, and an accumulation result in the buffer area is obtained; wherein the target data element is a data element corresponding to the thread local register index at the interval of the preset number in the thread local register array; and accumulating the accumulation results of the corresponding buffer area through the thread to obtain a row accumulation result of each row in the local register array of the thread or a column accumulation result of each column. By adopting the embodiment of the invention, the execution time of an operator on a chip can be reduced, and the operator performance is improved.

The execution-time reporting of telemetry of execution of a software program includes identifying a software program and accessing an event stream with multiple event entries associated with the software's components. An object model is generated that comprises a hierarchy of objects and properties associated with the events by creating objects for the software's components, including identifiers, properties, and timestamps. When an AI entity is instantiated for use during the software's execution, an AI object is created with an identifier and properties. The AI object is dynamically modified to reflect events associated with the AI entity during operation of the software program. The telemetry associated with the AI entity is reported according to subscriptions for the telemetry sessions.

The invention discloses a GPU (Graphics Processing Unit) parallel thread block adjustment and kernel function scheduling optimization method and system. The method comprises the following steps: acquiring GPU equipment attributes and kernel function execution attributes; determining a feasible thread block size solution space of each kernel function based on GPU equipment attributes and kernel function execution attributes, and selecting a thread block size which enables the load balance degree evaluation model value to be maximum as optimal execution configuration of the kernel function; on the basis of the determined optimal thread block size of each kernel function, dividing an execution grid of each kernel function into a plurality of kernel function slices, and establishing a concurrent execution relationship between the kernel functions; a CUDA graph is constructed in a mixed mode according to the established concurrent execution relation between the kernel functions and data dependence; and by taking an actual model of particle simulation software as a test object, respectively comparing GPU occupancy rates before and after thread block adaptive adjustment, multi-stream concurrent scheduling and hybrid CUDA graph scheduling optimization, kernel function execution time and overall simulation efficiency, and outputting an optimized particle simulation result.

Various example embodiments relate to methods, devices, and / or systems for executing a plurality of tasks in a computer operating environment. The method comprises monitoring a remaining execution time of a current task executing on the processing circuitry, and performing, by the processing circuitry, pre-emption of the current task based on the remaining execution time of the current task and a desired execution threshold time, the desired execution threshold time being one of a desired value based on one or more configuration parameter of the current task or a dynamic value determined based on information related to ongoing activities in the computer operating environment.