145 results about "Parallel scheduling" patented technology

The invention discloses a deployment optimization method and system for multi-type collaborative construction, and relates to the technical field of intelligent scheduling, and the method comprises the steps: collecting the pre-construction data of each type of work, and carrying out the hierarchical splitting of a construction task according to the type of work and a working stage, and obtaining construction sub-tasks; constructing a dependency relationship of construction sub-tasks by utilizing topological sorting and a critical path method, and defining a sub-task execution constraint and a work type scheduling constraint by using a constraint planning method; generating a preliminary scheduling scheme by using a heuristic algorithm, and optimizing sub-task execution sorting according to the sub-task execution constraint and the work type scheduling constraint; and dynamically adjusting the execution sorting of the subtasks according to the dependency relationship of the subtasks by using a parallel scheduling algorithm to obtain a scheduling scheme of collaborative construction. According to the method, resource conflicts and delay risks are effectively avoided, high parallelism and load balancing of multiple work types of tasks are achieved, and the construction efficiency and reliability can be remarkably improved in a complex construction environment.

The invention discloses a multi-modal load prediction computing power center dynamic energy-saving scheduling method and system, and relates to the technical field of energy-saving scheduling, and the method comprises the following steps: executing long-tail fractal filtering based on service load data in a steady state for a long time in a computing power center, extracting a steady-state energy spectrum, and constructing a zero-drift contrast base plane; applying micro-amplitude random phase disturbance on the zero-drift contrast base plane, and enhancing weak disturbance characteristics in the steady-state energy spectrum in combination with an entropy gradient amplification strategy; and converting the enhanced precursor micro-fluctuation characteristics into a time sequence difference energy cloud picture. According to the method, zero-drift contrast base surface enhancement perturbation identification is constructed, and multi-scale energy analysis and Bayesian confidence correction are combined, so that accurate prediction and quantification of the burst load are realized; during surge triggering, a fractal slicing and low-power-consumption parallel scheduling strategy is adopted, high response is ensured, meanwhile, energy consumption is reduced, and the method is suitable for a computing power center scene with high performance and energy efficiency requirements.

The invention relates to a quantum measurement and control task compiling method and device, computer equipment and a storage medium. The method comprises the following steps: converting a quantum measurement and control task into an intermediate representation instruction matched with a quantum low-temperature measurement and control chip through a pre-constructed hardware behavior model of the quantum low-temperature measurement and control chip; the hardware behavior model is constructed according to hardware characteristics of the quantum low-temperature measurement and control chip; performing parallel scheduling on the intermediate representation instruction to obtain a scheduling result; the intermediate representation instruction is converted into hardware executable codes according to the scheduling result, compiling of the quantum measurement and control task is completed, and the hardware executable codes are used for indicating the quantum low-temperature measurement and control chip to conduct quantum calculation. By adopting the method, the high efficiency and reliability of the measurement and control chip in the low-temperature environment can be improved.

The invention provides a simulation deduction method and system based on distributed parallel scheduling and a storage medium, and is applied to the technical field of data processing.The method comprises the steps that proxy services are deployed at a plurality of preset computing nodes, resource state information of all the nodes is dynamically collected, and a computing resource pool is obtained; arranging a pre-registered plug-in based on the data dependency relationship to obtain a simulation task process; in response to simulation scene configuration submitted by a user, generating a distributed scheduling task containing a fragmentation strategy; determining a plurality of sub-tasks corresponding to the distributed scheduling task based on the simulation task process; and based on the real-time load of the computing resource pool and the task priorities of the plurality of sub-tasks, respectively distributing the plurality of sub-tasks to a plurality of target computing nodes for task execution, and obtaining task execution result information. According to the method and the device, the time delay performance of distributed scheduling and the high availability performance of scheduling simulation tasks can be improved.

The invention discloses a network scanning task intelligent segmentation and load balancing method and system. The method comprises the following steps: identifying abnormal fragments by executing integrity verification on a scanning task, analyzing a task dependency relationship to determine a divisible position, and generating a segmentation scheme in combination with granularity constraint; node resource states are collected to calculate load indexes, and scheduling weights are hierarchically configured according to load levels to establish parallel scheduling channels; constructing a node cooperation group based on load difference, formulating an inter-group circulation rule to form a cooperation scheduling space, fragmenting tasks according to node capacity, and generating a parallel execution plan; monitoring the execution progress to identify overstocked nodes, diagnosing bottleneck types and planning a dredging path to execute task migration; the resource time sequence data is collected to identify the low-load time period, and the task is distributed to the optimal time slot to be executed, so that the task segmentation reasonability and the node load balancing degree are improved, and the distributed scanning execution efficiency is improved.

The invention discloses a channel coding and decoding read-write parallel scheduling method, which comprises the following steps of: inputting a base graph matrix, and sequentially arranging to obtain a read variable node sequence and a write variable node sequence of each row according to a column sequence number of a non-zero element in each row of the base graph matrix; constructing a conflict graph according to the base graph matrix; selecting the vertex with the highest saturation from the uncolored vertexes, selecting the minimum available color, and coloring the vertex; coloring each vertex, grouping the vertexes, dividing the vertexes with the same coloring color sequence number into the same group to obtain a vertex group set, and distributing a processing unit for each vertex group; constructing an operation sequence of each processing unit; conflict variable nodes of operation are eliminated; storing a reading variable node sequence and a writing variable node sequence of each operation of each processing unit; the invention further discloses a channel coding and decoding read-write parallel scheduling device. According to the technical scheme, the conflict rate can be effectively reduced, and the method can be suitable for various base graphs.

The invention provides a simulation scene multi-target calculation method and device based on parallel scheduling and a server, and relates to the technical field of industrial simulation application, and the method comprises the steps: obtaining a parameter set of a plurality of simulation tasks, and carrying out the task clustering processing of the parameter set according to the parameter similarity, and obtaining a plurality of task clusters; all the task clusters are distributed to different computing nodes, parallel solving processing of simulation tasks is carried out on the task clusters in all the computing nodes on the basis of a self-adaptive tuning model of a message passing interface, and local simulation results corresponding to all the computing nodes are obtained; and carrying out convergence and recombination processing on the local simulation results to obtain a complete simulation result data set, carrying out compression and indexed storage processing on the simulation result data set to generate a lightweight simulation result, and carrying out streaming visualization processing on the lightweight simulation result to obtain a target interactive simulation visualization result. According to the method, the simulation efficiency of simulation scene multi-target calculation can be remarkably improved.

A single-instruction multi-thread processor micro-architecture device oriented to neural network parallel scheduling comprises a front-end instruction fetching module, an instruction cache module, a decoding module, an arithmetic logic operation unit, a multiplication and division module, a memory access unit and a data cache module, and the micro-architecture device allocates a unique thread number for each thread. Threads are organized into thread groups, and in each period, the fair alternate arbiter selects an instruction from an instruction buffer of the thread group and sends the instruction to a subsequent decoding stage. The micro-architecture not only solves challenges faced by end-side equipment when processing high-performance calculation requirements such as neural network reasoning, but also provides an effective solution capable of reducing energy consumption and improving calculation efficiency through an innovative architecture design. The method is of great significance in promoting development of end-side AI application.

The embodiment of the invention provides a multi-modal model distributed reasoning optimization method for a dynamic intelligent internet of things. The method comprises the following steps: constructing a multi-modal model distributed reasoning optimization system for the dynamic intelligent internet of things; the system comprises a plurality of Internet of Things devices for generating and uploading DNN reasoning tasks and a plurality of servers for receiving and processing the DNN reasoning tasks, determining a DNN reasoning task data parallel scheduling strategy for the system; determining system reasoning time and system energy consumption by using a DNN reasoning task data parallel scheduling strategy; according to the system reasoning time and the system energy consumption, establishing a minimum long-term time delay under the constraint of long-term energy consumption, and taking the minimum long-term time delay as an optimization target to establish a long-term distributed reasoning stability optimization model; and solving the model by adopting a heuristic algorithm to realize distributed reasoning optimization of the multi-modal model. In this way, distributed reasoning tasks of the multi-modal model can be effectively managed, and reasoning time and energy consumption can be optimized.

The invention discloses a method for automatically generating a rejection defense document based on multi-agent collaboration, and belongs to the technical field of artificial intelligence. The method comprises the steps of collecting and preprocessing original multi-modal interaction data related to a payment refusing case, processing the original multi-modal interaction data into a text form to obtain an original interaction text, and storing the original interaction text in a database; performing cleaning processing on the original interaction text in the database by adopting Non-Agent to obtain semantic intermediate representation with consistent format; and based on the structured input, generating an anti-distinguishing reason by a multi-agent collaborative anti-distinguishing framework to obtain an anti-distinguishing document of the current payment refusing case. According to the invention, based on structured layering, responsibility constraint and parallel scheduling, the key defects of'hallusion caused by input noise ', 'single LLM responsibility overload' and'end-to-end time delay 'in the prior art are overcome together, so that the automatic defense signal generation system realized based on the method is remarkably improved in the aspects of compliance and fact accuracy; and practical advantages are embodied in business generalizability and engineering efficiency.

The embodiment discloses a scheduling method, apparatus, device and storage medium. The method includes: obtaining an algorithm directed graph corresponding to a target task, the algorithm directed graph comprising a plurality of algorithm nodes; grouping the plurality of algorithm nodes in the algorithm directed graph to obtain a plurality of node groups; scheduling the plurality of node groups in series, and scheduling a processing algorithm corresponding to at least one algorithm node in the node group in parallel.

The invention provides a water conservancy model parallel scheduling calculation method, system and equipment, which are applied to a water conservancy model parallel computing scheduling system, the water conservancy model parallel computing scheduling system comprises a computing engine, a visual interaction module, a GIS map and a user interaction interface, and the water conservancy model parallel computing scheduling method comprises the following steps: obtaining a control instruction, the control instruction is sent to the calculation engine; controlling the calculation engine to interact with the visual interaction module based on the control instruction, and obtaining an interaction result; and rendering the interaction result into the GIS map. According to the method, the whole process of the computing power resources is visually displayed, the integration of an algorithm is concerned in a water conservancy model platform, construction in the computing power resources is concerned at the same time, and the computing nodes, the computing unit states, the computing process grouping information and the like are visually displayed in the computing process, so that the computing power resource construction application is assisted to be optimized and improved.

The invention provides an efficient operation and maintenance software architecture setting method for multi-task parallel scheduling, and relates to the technical field of operation and maintenance automation. The method comprises the steps that resource state data of operation and maintenance nodes in an available area are collected in real time; dynamically calculating a comprehensive load index of each available area operation and maintenance node based on the resource state data to obtain a calculation result; according to the calculation result, identifying a high-load available area with the comprehensive load index exceeding a preset threshold value and a low-load available area with the comprehensive load index lower than the preset threshold value; and based on the high-load available area, performing priority grading on operation and maintenance tasks to be processed in the high-load available area according to task types to form a high-priority core task queue and a low-priority conventional task queue. According to the invention, accurate matching and efficient utilization of cross-regional resources are realized.

The present invention discloses a method and system for efficient configuration of a time-sensitive network based on hardware acceleration, pertaining to the field of wired communication networking technology. The method includes an acquisition step, a preprocessing step, a scheduling step, a generation step and a deployment step. The scheduling step, according to an operating state of a scheduling engine, based on attribute information of to-be-configured data streams, selects a time slot length, calculates a hyperperiod, depending on a size of the hyperperiod, carries out any of hyperperiod based parallel scheduling or conflict-group based parallel scheduling, obtaining a scheduling result, and thereby generates and deploys a configuration scheme to network devices. The present invention is able to, based on on-site network and terminal device conditions, derive global deterministic scheduling and configuration schemes and enables rapid distribution and deployment.

The invention provides an instruction-level parallel scheduling method and device in a deep learning compiler, and the method comprises the steps: decomposing a calculation graph of a deep learning task, decomposing a task represented by each calculation graph node on the calculation graph into a hardware calculation instruction according to a current task decomposition scheme, and obtaining an instruction set of the calculation graph; according to the data flow dependency relationship between the nodes on the calculation graph, obtaining a dependency relationship graph between the instructions in the instruction set; dividing the hardware calculation instructions in the instruction set into each sub-core in the processor; according to the dependency graph, determining a directed acyclic graph containing the dependency among the hardware calculation instructions on the sub-cores in the processor, and according to an emission model of the processor and the reciprocal throughput rate of the hardware calculation instructions, determining the execution sequence of the hardware calculation instructions divided to the sub-cores on the sub-cores, and the processor executes the instruction scheduling to obtain an execution result of the deep learning task.

The invention provides an ecological system model parameter optimization system and method based on parallel scheduling, and belongs to the technical field of resources and environments. By constructing a species feature library, adopting a clustering method for species identification, and combining various sensitivity analysis algorithms and an automatic parameter optimization strategy, full-process automatic configuration of remote sensing data preprocessing, feature extraction, species identification and PFT mapping is realized, and the workload of manual intervention and manual parameter adjustment is remarkably reduced; an unsupervised / semi-supervised learning method is introduced, so that high-precision automatic identification of main vegetation species in the region is ensured; according to the method, a multi-method fusion sensitivity analysis of Sobol ', Morris, EFAST and the like is adopted, a multi-algorithm collaborative optimization framework is combined, accurate and automatic adjustment of key physiological parameters is achieved, so that the precision and adaptability of model prediction are greatly improved, a Dask parallel computing framework and an SLURM scheduler are utilized, efficient fragmentation and dynamic resource scheduling of tasks are achieved, and the computing efficiency is remarkably improved.

The embodiment of the invention discloses a production change control method and device for mixed line production and a medium, and relates to the technical field of production control, and the method comprises the steps: collecting real-time production index data corresponding to a current production product in a current production line in real time under the triggering of a production change instruction issued by a production management system, obtaining a target process parameter set corresponding to the target product change product; according to the real-time production index data and the production change instruction, a production change state vector containing a production change stage label is determined, and the production change stage label comprises a production change preparation stage, a production change pre-adjustment stage and an execution stage; and based on the production change state vector and the target process parameter set, through a preset reinforcement learning agent, determining a time sequence production change action sequence, converting the time sequence production change action sequence into an equipment control instruction for instruction distribution, and realizing production change control. The sequential production change action sequence comprises a pre-adjustment action instruction, a parallel scheduling instruction and a parameter loading instruction.

The invention discloses a test sequence optimization method and device, equipment and a storage medium. Comprising the steps of obtaining function point related data, and generating a function point test sequence based on an upper confidence bound algorithm; defining a global resource occupancy matrix, and according to the function point test sequence, sequentially importing the function points, traversing and querying the global resource occupancy matrix, and generating a parallel scheduling task; and executing the parallel scheduling task, and monitoring resource conflict conditions and test failure information in real time. By comprehensively considering related data of the function points, intelligent sorting of the function points is achieved, and the problems that in a traditional sorting method, exploration and utilization are difficult to balance and global dynamic optimization is lacked are solved. By reasonably distributing parallel tasks, resource conflicts are avoided, the utilization rate of limited test resources is improved, and efficiency reduction caused by unreasonable resource planning is reduced. Resource conflicts are found and processed in time through real-time monitoring, the conflicts are prevented from influencing the test process, test failures can be quickly responded, and the test efficiency is improved.

The embodiment of the invention provides a multi-account concurrent processing management system and a multi-account concurrent processing management method, which are applied to the technical field of multi-account high-concurrency business processing, and are used for analyzing a plurality of received account operation requests into a micro-operation set, packaging micro-operations into an operation object identifier, an operation type and a dependency relationship, and storing the operation object identifier, the operation type and the dependency relationship. Comprising resource dependence, granularity dependence and business logic dependence; determining a lock execution path based on the operation object identifier, the operation type and the dependency relationship; constructing a dependency tensor model, performing conflict density mapping to form conflict density distribution, and performing tensor conflict operation on the microoperation in combination with a lock execution path to obtain a conflict intensity value; a high-conflict area is identified based on conflict density distribution, a potential deadlock graph is constructed, a micro-operation directed acyclic graph is generated, nodes with zero in-degree and out-degree are identified to form a first execution sequence, a second execution sequence is constructed according to residual micro-operations, a micro-operation execution sequence is formed through splicing, and efficient parallel scheduling and throughput capacity improvement are achieved.

The invention discloses a large model heterogeneous reasoning method and system. The method comprises the steps that target components to be unloaded and reserved components to be unloaded are determined according to calculation and storage characteristics for a large model, the target components are components which are densely stored and have the calculation strength lower than a set standard, and the reserved components are calculation-intensive components; and unloading the target component to a heterogeneous processing unit different from the GPU to execute related tasks, allocating the related tasks of the reserved component to the GPU to execute, and performing acceleration processing on the tasks executed by the heterogeneous processing unit in cooperation with a parallel scheduling strategy and a quantitative storage strategy so as to obtain a reasoning result. According to the method, the dependence on the GPU video memory and the reasoning cost are reduced, and the throughput is improved.

The invention belongs to the technical field of computers, particularly relates to a data migration method, system and equipment based on task arrangement, and aims to solve the problems of difficulty in dynamic adjustment and lack of decoupling in the prior art. Data migration logic is packaged into a pluggable module, and a user arranges a migration process through a graphical interface: a module is selected, an execution sequence and dependency are set, and a structured task description file is generated. And the system automatically analyzes the dependency relationship in the file to generate an execution sequence. The parallel scheduling is independent of tasks; and after any task is completed, the postposition dependence is relieved, and the task meeting the condition is executed immediately. Abnormal response is carried out according to a strategy in the whole execution process, transaction rollback based on a persistent framework is automatically triggered when data operation is abnormal, and reliability is ensured. The method has the advantages of strong decoupling line and high expansibility, and the adaptability and maintainability of the migration process are remarkably improved.

The invention discloses a large language model reasoning acceleration method based on GQA feature optimization, and the method comprises the steps: 1, determining the segmentation granularity of a query tensor Q, and segmenting the query tensor Q into one or more subtasks; step 2, adaptively determining segmentation numbers of a key tensor K and a value tensor V; and step 3, calculating a query tensor Q, and after calculation is completed, scheduling the key tensor K and the value tensor V to different calculation unit groups to perform calculation in parallel. The invention further discloses a reasoning acceleration system for implementing the reasoning acceleration method. The reasoning acceleration system comprises a calculation task dynamic division module, a parallel scheduling module, a dynamic addressing multiplexing module and a zero-copy access module. The reasoning acceleration method and system have wide application value.

The invention discloses a multi-operator coarse-grained parallel scheduling method based on a heterogeneous data stream architecture, which belongs to the field of runtime systems, and comprises the following steps of: dividing a computing load of an operator with in-degree of 0 in a computing graph to be executed into a plurality of sub-tasks Task and packaging the sub-tasks into independent Task objects; constructing four priority queues which are respectively used for storing Task objects which have different preferences on four functional parts in the processing unit, putting index Task objects of operators into the corresponding priority queues according to the preferences of the operators on the functional parts and the number of instructions of the operators, and sorting the index Task objects, k (K < = 4) Tasks with complementary resource requirements are selected from the four priority queues to form a new composite operator, and the new composite operator is executed by the processing unit array; according to the method, the new operator is generated by identifying the preference of the operator function part and dynamically combining the Task, so that the utilization rate of the internal function part of the PE can be fully improved, and the operation efficiency of the whole data flow program is accelerated.

This invention discloses a large-scale two-dimensional convolution operator acceleration method for DSP platforms, belonging to the field of digital signal processing. This method selects the im2col or col2im algorithm to rearrange data based on the convolution type; adopts a three-segment matrix partitioning strategy to adapt to L3 cache capacity; utilizes an EDMA buffer ping-pong architecture to achieve pipeline parallelism for data transmission and computation; implements SIMD instruction-level optimization, using DMPYSP and DADDSP instructions in conjunction with pipeline optimization to achieve four FP32 multiplication and addition operations per cycle; and implements multi-core parallel scheduling, using OpenMP to achieve task-level and data-level parallelism. This method has been tested on a TI TMS320C6678 platform to achieve efficient inference of SAR target detection networks, providing a feasible solution for real-time inference of CNN networks on DSP platforms.

The application provides an electrical control system and parallel scheduling method of a large gear automatic welding workstation, belongs to the field of automatic welding equipment and intelligent manufacturing technology, the method controls two riveting and welding platforms to alternately perform welding, turning over and feeding and discharging through a central controller, and realizes no-waiting alternate operation by using area interlocking and pre-switching logic; combined with demonstration-free visual guidance welding and mobile base control, it ensures continuous operation of the welding robot and guarantees that multiple mobile work equipment do not interfere and collide; the central controller opens an industrial Ethernet protocol interface, collects welding parameters in real time and uploads them to an MES system, realizes data tracing and remote monitoring. The application solves the problems of low utilization rate, weak multi-variety adaptability, weak collaborative control ability and insufficient data collection and networking ability of the existing large gear welding workstation equipment.

This invention discloses a parallel optimization system for massive video processing. The system adopts a four-layer integrated parallel and collaborative processing architecture, comprising, from top to bottom: a parallel task layer for video stream access, grouping, splitting, encapsulation, and queue management; a resource abstraction layer for unified hardware modeling, status acquisition, topology construction, and capability assessment; a parallel scheduling layer for task-hardware matching, load balancing, priority scheduling, and dynamic adjustment; and an execution optimization layer for data stream localization, parallel read / write, cache optimization, and zero-copy processing. The parallel task layer, resource abstraction layer, parallel scheduling layer, and execution optimization layer form a complete parallel processing link: task input, resource awareness, accurate scheduling, and optimized execution. This invention implements concurrent rate limiting and smooth access for the input video stream to prevent traffic surges; and sets synchronization points and timing control for parallel tasks to ensure orderly output.

The invention relates to a mechanical hard disk sequential parallel scheduling double random sample high-speed access method and system, and the method comprises the steps: packaging training samples into a fixed number of data packages, and uniformly distributing the data packages to a plurality of mechanical hard disks through a greedy algorithm; a plurality of data readers are created through the data manager, and all the mechanical hard disks are distributed to the data readers in a balanced mode; circularly accessing the distributed mechanical hard disk data packets through an infinite iterator of each data reader, randomly rearranging a data packet path queue during each cycle, continuously reading training samples from the current data packets in sequence, and storing the training samples into a buffer pool of a data manager; sequential reading operation is executed in parallel through a plurality of data readers, and the throughput of each mechanical hard disk is accumulated; a client request is received based on a high-efficiency communication protocol, and a training sample is randomly selected and returned, so that the purpose of random sample high-speed access which is low in cost, high in throughput and capable of meeting deep learning training requirements is achieved.

Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate two or more blocks of threads to be scheduled in parallel.

The application provides a sparse matrix-based parallel scheduling acquisition method, system, device and medium, the method comprises the following steps: acquiring a sparse matrix; constructing an initial dependency graph according to the sparse matrix; processing the initial dependency graph through a bidirectional sub-tree to obtain a target aggregation graph; and acquiring a task dependency graph according to the target aggregation graph; the sparse matrix-based parallel scheduling acquisition method can efficiently realize the parallelization of sparse matrix calculation with a cyclic carrying dependency relationship by combining an affinity perception scheduling strategy to generate a high-performance parallel scheduling scheme through bidirectional sub-tree aggregation; the system has the same beneficial effects.

The application discloses a power center dynamic energy-saving scheduling method and system for multi-modal load prediction, relates to the technical field of energy-saving scheduling, and comprises the following steps: based on the business load data of the power center in a stable state for a long time, performing long-tail fractal filtering, extracting the steady-state energy spectrum, and constructing a zero-drift reference surface; applying a small random phase disturbance to the zero-drift reference surface, and combining an entropy gradient amplification strategy to enhance the weak disturbance characteristics in the steady-state energy spectrum; and converting the enhanced precursor microwave fluctuation characteristics into a time series difference energy cloud chart. The application enhances micro-disturbance identification by constructing a zero-drift reference surface, combines multi-scale energy analysis and Bayesian confidence correction, realizes accurate prediction and quantification of sudden load, adopts fractal slicing and low-power parallel scheduling strategies when the load surges, ensures high response while reducing energy consumption, and is suitable for power center scenes with high performance and energy efficiency requirements.