18 results about "Parallel running" patented technology

This application provides a method, apparatus, and system for downloading control strategies for coal-fired power plants. The method includes collecting multi-source production data from the power plant, performing heterogeneous adaptation and standardization processing to generate standard data frames; loading the control strategy to be implemented into a shadow running unit, running it in parallel based on the standard data frames, and obtaining verification results; performing gray-scale downloading of the control strategy to be implemented under preset gating conditions; and real-time monitoring and execution of tiered rollback operations in case of abnormal situations. This application solves the problem of differences between offline simulation and the real operating environment by setting up a shadow running unit to run the control strategy to be implemented in parallel; by performing gray-scale downloading, it avoids the sudden operational risks caused by full write; furthermore, by real-time monitoring of the operating status and execution of tiered rollback operations, it can automatically and quickly handle abnormal situations, improving the safety and reliability of downloading control strategies for coal-fired power plants and reducing operational risks.

The invention relates to a method (400) for parallel running of at least one first quantum circuit and a second quantum circuit on a quantum computer, wherein the quantum computer comprises a qubit arrangement which is functionally divided into at least a first qubit group and a second qubit group, wherein the first qubit group and the second qubit group are spatially separated from one another, wherein the method (400) comprises the following steps: providing (406) a list including temporal delay values; Providing (401) a mapping of the first quantum circuit to the first qubit group and providing a mapping of the second quantum circuit to the second qubit group, wherein the first quantum circuit and / or the second quantum circuit are time-delayed by means of the delay values; initializing (402) the quantum computer, comprising: initialising the first qubit group and initialising the second qubit group; parallel running (403) of the first quantum circuit with the first qubit group to generate (404) first measurement results of the first qubit group and parallel operation of the second quantum circuit with the second qubit group to generate (404) second measurement results of the second qubit group; providing (405) the first measurement results and the second measurement results.

The application belongs to the technical field of processors, and provides a verification method and system of a RISC-V out-of-order superscalar processor, a test program is generated based on a random instruction generator of a RISC-V instruction set, and the test program is run in parallel by an out-of-order superscalar processor and an instruction set simulator; in the running process of the out-of-order superscalar processor, retired instruction information is sequentially extracted from a reorder buffer thereof, and corresponding instruction execution results are read from a physical register file according to the information of the retired instruction, a check information queue table item of sequential execution is generated, and is stored into a check information queue; through a preset interface, the instruction set simulator is controlled to run in single step, and sequential submission results corresponding to the check information queue table item are extracted and stored into a submission result queue; the table items in the check information queue and the submission result queue are compared one by one, if the comparison is consistent, the verification is continued, and if the comparison is inconsistent, an error instruction is located. The efficiency of verification is improved.

This invention relates to the field of new energy grid-connected control technology, and particularly to a method and apparatus for identifying control modes and parameters of a doubly-fed induction generator (DFIG) converter. The method involves applying small-amplitude step or pseudo-random voltage disturbances at the turbine terminal or grid connection point, collecting three-phase voltage and three-phase current data at the grid connection point, and obtaining the input vector and measurement output vector in a synchronous rotating coordinate system through Park transformation. An augmented state-space model library containing multiple potential control modes is established, and the PI parameters to be identified are incorporated into the augmented state. Unscented Kalman filters are run in parallel for each model to estimate the state / parameters, calculating the innovation residuals and their covariance. Based on Bayesian inference, the posterior probabilities of each model are recursively updated and normalized. The model with the highest probability is selected to determine the control mode, and the corresponding parameter estimates are output. This method does not require preset modes, improves identification accuracy and parameter physical consistency during mode switching or critical operating conditions, and facilitates stability analysis and operating condition migration. It has wide applicability.

Embodiments provide a fast multi-agent reinforcement learning (RL) pipeline that runs the full RL workflow end-to-end on a single GPU, using a single store of data for simulation roll-outs, inference, and training. Specifically, simulations and agents in each simulation are run in tandem, taking advantage of the parallel capabilities of the GPU. This way, the costly GPU-CPU communication and copying is significantly reduced, and simulation sampling and learning rates are in turn improved. In this way, a large number of simulations may be concurrently run on the GPU, thus largely improving efficiency of the RL training.

The application discloses a particle accelerator operation control method, device, system, equipment and medium, relates to the particle accelerator control technical field, and through the global configuration parameter package as a unified driving source, the multidimensional control subsystem is real-time collaborative scheduling, so that the multidimensional control subsystem independently runs in parallel under the premise of not interfering with each other. Wherein the method comprises: according to the operation plan of the particle accelerator, the global configuration parameter package is generated in advance;Under the driving of the global configuration parameter package, the multidimensional control subsystem is collaboratively scheduled, so that the multidimensional control subsystem independently and in parallel executes corresponding control logic, and obtains parallel output information;Combining the current beam state, the parallel output information of the multidimensional control subsystem is fused to obtain a beam control decision;The beam control decision is issued to the execution equipment, so that the execution equipment triggers corresponding beam control actions according to the beam control decision.

The application discloses a kind of multi-core architecture software and hardware cooperation quick start method, CPU is according to the corresponding loading descriptor generated by compressed firmware, and auxiliary core is according to loading descriptor control loading hardware module and hardware decompression module to the compressed firmware startup loading procedure and decompression procedure of binding, finally CPU to the firmware startup running procedure after decompression, during the operation of running procedure, the compressed firmware corresponding to next loading descriptor is run loading procedure and decompression procedure in parallel, i.e. running procedure is in parallel with loading procedure and decompression procedure, and the loading and decompression of compressed firmware are directly completed by hardware module, while releasing the resources of CPU and auxiliary core, greatly speed up the start-up time.

Vehicle control device (10) which controls a display (20, 30) arranged in a vehicle interior, wherein the vehicle control device (10) comprises: - a physical processor (101) that runs multiple operating systems (103, 103a, 104) in parallel on virtualization software (102, 102a) via virtualization technology; - a trigger detection unit (110) that detects an activation trigger of the vehicle control device (10); and - an allocation unit (1021), wherein - the physical processor (101) has several physical processor cores (1011, 1012, 1013, 1014), abstracted into virtual processor cores by virtualization technology; - the multiple operating systems (103, 103a, 104) comprise: a first operating system (103, 103a) which is the operating system for running a priority application, which is an application for displaying content that is preferentially displayed when the vehicle control device (10) is activated; and a second operating system (104) as another operating system; and - then, when the trigger detection unit (110) detects the activation trigger to activate the first operating system (103, 103a) and the second operating system (104), the allocation unit (1021) performs a temporary allocation that temporarily assigns the first operating system (103, 103a) to the virtual processor cores with an allocation set of virtual processor cores that is greater than a predefined allocation set of virtual processor cores as an initial allocation set after activation of the first operating system (103, 103a) has been completed.

This application provides a method and system for configuring an AI model, used to more efficiently obtain operator sequences computed by computing devices running the AI ​​model in parallel using a target parallel configuration. The method includes: running a first AI model in an AI cluster using M parallel configurations, with each computing device executing M first operator sequences. Typically, N parallel modes can be pre-set, and one or more of the N parallel modes are disabled each time, with different disabled parallel modes each time, resulting in different M configurations; subsequently, obtaining second operator sequences that are identical among the M first operator sequences; further, running the first AI model using each of the N parallel modes to obtain a third operator sequence for each parallel mode; subsequently calculating the difference operator between each third operator sequence and the second operator sequence; and generating an operator sequence based on the number of N parallel modes in the target parallel configuration, the difference operator for each parallel mode, and the second operator sequence.

The invention relates to a method (300) for the functional partitioning of a qubit arrangement of a quantum computer: providing (301) information relating to at least one first quantum circuit and a second quantum circuit, which are provided for parallel running on the quantum computer; providing (302) a number of qubit groups specifying the number of quantum circuits that can be run in parallel; providing (303) an optimization function, which depends on a distance measurement between the qubit groups in the qubit arrangement; optimizing (304) the optimization function with regard to the distance measurement between the qubit groups; providing the positions of the qubit groups in the qubit arrangement. The invention also relates to a method for the parallel running of at least one first quantum circuit and one second quantum circuit on a quantum computer.

Compiler techniques lot inline parallelism and re-targetable parallel runtime execution of logic iterators enables selection thereof from the source code or dynamically during the object code execution.

A computer-implemented method of predicting a timeseries of time delays resulting from contention between tasks running in parallel on a multi-processor system using a trained Machine Learning based Task Contention Model may include executing a plurality of actual execution tasks on the multi-processor system in isolation, and for each task and during execution of the respective task, capturing a timeseries including a plurality of uWindows. The method may also includes inferring, from a time-agnostic ML based regressor, a predicted contention delay for the first uWindow of the timeseries when two or more of the plurality of actual execution tasks are executed on parallel on the multi-processor system. The method further includes inferring, from a Non-linear AutoRegressor with exogenous inputs (NARX), predicted contention delays for each subsequent uWindow of the timeseries based on the respective captured snapshot, and the predicted values for the previous time periods fed back into the NARX.

This invention discloses a method, apparatus, device, and storage medium for orchestrating diverse heterogeneous models in aerospace software. The method uses a data pool configuration XML file to uniformly manage simulation data of various simulation entities in aerospace equipment. The model configuration XML file defines the required set of diverse heterogeneous models for each simulation entity and the preset scheduling strategy for each model. Based on the preset scheduling strategy, each simulation entity runs in parallel within a simulation cluster without interference. A publish / subscribe mechanism is used to encapsulate the aerospace equipment system bus. A daemon thread monitors the operational health status of each simulation entity in real time and executes corresponding response strategies based on the operational health status. This enables efficient collaborative orchestration of diverse heterogeneous models in aerospace software, improves the uniformity and consistency of simulation data management, ensures that multiple simulation entities run in parallel without interference, enhances the monitoring capability and stability of the simulation process, and supports unmodified access of the software platform to the aerospace equipment system bus.

This invention discloses a method and system for coordinated optimization and control of deep coal seam storage and methane flooding based on digital twins, belonging to the field of unconventional natural gas extraction and carbon capture, utilization, and storage technology. The method constructs a high-performance digital twin and establishes a reduced-order model using a deep operator network. In each decision cycle, the twin is dynamically updated using ensemble Kalman filtering based on real-time monitoring data. The updated twin serves as the simulation environment, driving a multi-objective reinforcement learning agent to perform online rolling optimization decisions. Based on a comprehensive reward function, it outputs coordinated control commands for injection parameters and downhole intelligent equipment. Simultaneously, a risk prediction and hierarchical interlocking response mechanism based on the digital twin runs in parallel, achieving proactive safety management from risk warning to emergency response. This method realizes real-time perception, dynamic simulation, multi-objective coordinated optimization, and proactive safety control of the storage and flooding process, effectively improving storage efficiency, methane recovery rate, and engineering safety level.