168 results about "Thread count" patented technology

The invention relates to a lightweight asynchronous message processing method and system for a high-concurrency scene, and the method comprises the steps: receiving a message request of a client, carrying out the preprocessing of the message request, and generating a to-be-processed message; distributing the to-be-processed message to a multi-level priority queue based on a priority scheduling mechanism; dynamically adjusting the number of core threads and the maximum number of threads of the thread pool according to the resource state data, and forming a thread pool resource configuration scheme of the current scheduling period; dynamically calculating the message processing quantity of the current scheduling batch based on the message accumulation quantity of each priority queue, the thread pool resource configuration scheme, the CPU utilization rate in the resource state data and the JVM heap memory utilization rate; and based on the out-of-heap memory buffer area reference, assigning the to-be-processed message object to a corresponding processing thread to execute a service processing logic until the processing operation of the messages in all the scheduling units is completed. The method has the effect of improving the resource adaptability of the asynchronous message processing architecture.

The invention provides a high-speed NTT method and system based on GPU matrix-thread collaborative optimization. The method comprises the steps that an input twiddle factor is converted into a matrix form; transmitting the updated twiddle factor matrix and the input array to a graphics processor; according to the input array and the size of the twiddle factor matrix, the number of blocks in the grid and the number of threads included in each block are set, so that each block is responsible for the operation of multiplying one row by one column, and each thread in the block completes the operation of multiplying one pair of data in the corresponding row-by-column process to obtain a group of intermediate results; and obtaining output data corresponding to the block according to an intermediate result in the block, and sending the output data to a corresponding position of an output matrix to serve as a multiplication result. According to the method, NTT calculation is reconstructed into parallel matrix-vector operation, the maximum utilization of GPU calculation resources is realized through the combination of an efficient thread allocation strategy and an optimized memory access mode, and the calculation efficiency is remarkably improved.

The invention discloses a cloud hard disk data backup and recovery method and device, electronic equipment and a storage medium, and relates to the technical field of cloud computing. A cloud hard disk is segmented into a plurality of segmentation blocks and a multi-thread parallel processing mode is adopted, a backup and recovery task of a large-capacity cloud hard disk is decomposed into a plurality of sub-tasks to be processed at the same time, and the data backup and recovery efficiency is improved. The operation time is greatly shortened, the number of threads in the thread pool is determined according to the number of the segmentation blocks and the current available computing resources, the utilization rate of the computing resources can be improved, cloud hard disk segmentation strategy parameters can be flexibly configured according to service requirements and cloud hard disk characteristics, data protection requirements in different scenes are met, and the user experience is improved. Consistency verification is carried out on the transmitted data, so that the integrity and consistency of the data in the backup and recovery process are ensured, the data loss risk is reduced, and the purposes of improving the backup and recovery efficiency of the cloud hard disk and enhancing the system reliability and the computing resource utilization rate are achieved.

The invention relates to the related technical field of equipment life cycle management, in particular to an aviation equipment full life cycle management method and system based on a digital thread. Determining main data of a first digital thread and a first-layer associated test sequence and main data of a second digital thread and a second-layer associated test sequence, mapping the deviation between a physical equipment state and a virtual simulation state, and identifying a performance difference point under a cross-stage data association link; and setting a management strategy for collaborative optimization. The technical problem that the reliability of aeronautical equipment is limited due to the fact that a cross-stage performance difference point equipment performance fluctuation root positioning fuzzy cannot be recognized in real time is solved, first and second digital thread main data and an associated test sequence are constructed, the deviation between physical equipment and a virtual simulation state is accurately mapped, and the reliability of the aeronautical equipment is improved. And a management strategy is set in combination with dual-thread data and performance difference points, and full-life-cycle collaborative optimization is performed, so that the technical effect of equipment reliability is effectively improved.

The invention provides a system operation lagging construction method and device, equipment, a medium and a program product. Relates to the technical field of system performance testing. The method comprises the steps that the CPU thread number and a watchdog threshold value of a current system are obtained; on the basis of the CPU thread number and the watchdog threshold value, the duration of the target thread executing busy waiting operation is calculated through a preset formula; initializing a spin lock of a kernel mode, and creating target threads of which the number is the same as that of the CPU threads, so that the target threads execute preset processing logic to construct system operation jamming; the preset processing logic comprises the following steps: periodically obtaining a spin lock; responding to the situation that the spin lock is not obtained, and continuing to execute the spin lock obtaining operation; and in response to obtaining the spin lock, executing a busy waiting operation. According to the method, CPU resources are continuously occupied through kernel spin lock competition, and a real system jamming scene is simulated. The busy waiting operation duration is dynamically calculated, it is ensured that the single-thread lock holding time is slightly lower than a watchdog threshold value, and crash of a system triggering unrecoverable is avoided.

The invention provides a concurrent scheduling and fault-tolerant optimization method and a concurrent scheduling and fault-tolerant optimization system for distributed jobs of a Spark application layer. The method comprises the following steps: acquiring a scheduling parameter, and analyzing the parameter to generate a configuration object; dividing data in a starting and ending date range according to business logic based on the configuration object, and generating a discrete data fragment list; based on the discrete data fragment list and the concurrent thread count, creating a thread pool, packaging each discrete data fragment into an independent task, and storing the independent task into a to-be-processed fragment queue; obtaining discrete data fragments through a thread pool working thread, calling SparkSession to execute service processing logic, and submitting a job to a cluster to obtain an execution result; performing judgment processing on the execution result, and recording a task processing state; based on all processing states, an execution report is generated. According to the method, the execution efficiency and the resource utilization rate of the Spark application are remarkably improved.

The embodiment of the invention discloses a data synchronization method and device, electronic equipment and a storage medium. The method comprises the steps that connection with a data source is established, metadata information of the data source is acquired and analyzed, and data source characteristics are obtained; determining a target index according to the real-time operation index of the data source and a local real-time allowable performance index, determining an adjustment strategy of the collection thread count according to the target index, and adjusting the current collection thread count according to the adjustment strategy; and determining the synchronization content of the to-be-synchronized data according to the data source characteristics and the database log, determining a parallel pipeline according to the synchronization content, and issuing the synchronization content to the parallel pipeline to synchronize the to-be-synchronized data. According to the method, the acquisition thread count is accurately adjusted, and the load balance of the two ends in the data transmission process is ensured while the data transmission load pressure of the data source and the local is relieved.

The invention discloses a streaming task operation method and device based on a distributed batch scene, and relates to the technical field of distribution.The method comprises the steps that after a task scheduling instruction distributed by a scheduler is received, the global streaming read thread count in a distributed cache is inquired, and when the task scheduling instruction does not exceed a preset thread count threshold value, the task scheduling instruction is executed; adding the job task into a streaming read task execution queue, scheduling a to-be-executed task from the streaming read task execution queue under the condition of monitoring that the target cache triggers a resource release event, confirming a target thread corresponding to the to-be-executed task, and reading task data from a database through a streaming read mode according to the to-be-executed task; after task processing is completed, thread resources corresponding to the target thread are released, and the thread occupancy state in the target cache is updated. According to the method and the device, the technical problems of long task processing time, excessive consumption of thread resources and influence on database use due to adoption of a streaming reading strategy in a distributed batch scene in related technologies are solved.

A system for scaling parallelism in sweeping stale data associated with multi-version data management is disclosed. The system is programmed to manage a targeted sweep of stale data stored on distributed storage associated with a set of computing nodes, where the stale data includes metadata for database transactions. The system is programmed to dynamically determine, on a computing node, how many threads to simultaneously perform deletion for the targeted sweep based on historical performance of the computing nodes, historical performance of the distributed storage associated with the computing nodes, or other factors. The system is programmed to then cause a certain number of threads to run on the computing node, where each thread identifies and deletes specific metadata for data transactions within the scope of the targeted sweep.

The invention relates to the technical field of graphic rendering, and provides a task processing method and device, a storage medium, electronic equipment and a program product. The task processing method comprises the steps that a plurality of first calculation tasks are converted into at least one second calculation task, and the number of threads in the first calculation tasks is smaller than the number of threads in the second calculation tasks; and executing the at least one second calculation task in parallel through the plurality of processing units to obtain a task processing result. According to the method, a plurality of small-order first computing tasks with serial execution sequences are converted into at least one large-order second computing task, and the plurality of small-order first computing tasks are compactly arranged, so that the large-order second computing tasks can be simultaneously configured to a plurality of existing processing units to be processed; therefore, parallel processing of the multiple first computing tasks is achieved, multiple existing processing units are fully utilized, GPU computing resource waste is avoided, and task processing efficiency is improved.

The invention discloses a self-adaptive memory allocation optimization method and device, terminal equipment and a storage medium, and belongs to the field of memory management. The method comprises the steps of monitoring thread data and system state data in real time, extracting thread memory behavior characteristics and resource state characteristics when one variable quantity exceeds a safety threshold value, and performing weighting calculation according to a preset weight coefficient to obtain a target strategy score; when the strategy score difference value of the target strategy score and the current strategy score is smaller than a preset difference value threshold value, the current memory allocation strategy is kept; when the strategy score difference value is not smaller than a difference value threshold value, a target memory allocation strategy is matched in a preset strategy library according to the thread memory behavior and the resource state characteristics, a current memory allocation strategy is updated, memory allocation parameters are adjusted, and a current strategy score is updated; when the current memory allocation strategy and the current strategy score are compared for the first time, the current memory allocation strategy and the current strategy score are the initial memory allocation strategy and the corresponding initial score, and the problem that efficiency and compatibility cannot be considered at the same time in the prior art is solved.

The thread allocation method, device, storage medium and computer device provided by the application, when the target task to be executed and the total number of threads to be allocated are determined, since the target task includes a plurality of concurrently executed subtasks, and the number of subtasks is not greater than the total number of threads, therefore, at the initial running of the system, the application can perform at least one thread allocation operation on each subtask according to a preset first allocation strategy until the number of allocated threads reaches the total number of threads; further, in order to avoid allocation errors caused by uneven data size, the application can set a round interval in advance, and after the thread allocation is completed, the current execution round of the target task is monitored, and when the current execution round reaches the preset round interval, at least one thread allocation operation is performed on each subtask according to a preset second allocation strategy until the number of allocated threads reaches the total number of threads, which can improve the resource utilization.

The embodiment of the application provides a kind of method and device for determining target thread, storage medium, electronic equipment, which comprises: performing classification operation to N test class files in test case set, obtain M test class file set;Determine the first thread interval required to run the test case included in each test class file set based on the number of test class files included in each test class file set;Based on the running time of test case and the first thread interval included in each test class file set, the predicted running time of each test class file set is calculated, and M predicted running times are obtained;According to target predicted running time and the running time of test case included in target test class file set, the target thread required to run the test case included in target test class file set is calculated. Through the present application, the problem that the number of threads cannot be reasonably determined in the related art can be solved, and the effects of reasonably starting threads and reducing resource consumption are achieved.

Code running on a processing unit can retain a hold on an active thread thereby preventing another thread running on the processing unit from executing. This can occur when the code requests more threads to be executed than the processing unit can give forward progress to, e.g., more threads than can be run simultaneously. Inserting the conventional yield instruction can lead to processing inefficiency such that the yield instruction can be executed more times than necessary thereby leading to an inefficiency in processing cycles of the processing unit performing the thread-switching routines. A yield occasionally instruction can be inserted into the code instead of the yield instruction. The yield occasionally can limit the times a yield instruction is executed thereby improving the efficiency of the processing cycles. The yield occasionally instruction can be used in non-terminating regions of the code, where the forward progress of other threads cannot be guaranteed.

The application provides a parallel computation method and system for multiplication of a symmetric matrix and a vector, and the method comprises the following steps: obtaining the symmetric matrix and the vector, and determining the number of threads; dividing a sub-region; in each sub-region, marking a first axis and / or a second axis parallel to the symmetric axis; calculating the symmetric axis region and the vector, and writing into a register; performing twice different first multiplication calculation on the first axis, obtaining two groups of different data and writing into the register respectively; performing twice different second multiplication calculation on the second axis, obtaining two groups or four groups of different data and writing into the register respectively, and obtaining the calculation result of the multiplication of the symmetric matrix and the vector. According to the scheme, the multiplication calculation of the symmetric matrix and the vector can be accurately and quickly realized, the order of element processing in the matrix is reasonably planned, resources of the threads are reasonably distributed, the write conflict can be effectively avoided, the multi-thread parallel calculation is realized, the calculation efficiency is high, the load of each thread is balanced, and the thread resource distribution is reasonable.

The application discloses a multi-stage dynamic arrangement method for Modelica task parallel compilation load, the method designs an arrangement optimization strategy according to the granularity of each stage, the front compilation stage improves the translation speed by setting the number of parallel CPUs for single-machine compilation of the task, but if multiple tasks are compiled concurrently on the single machine, the speed will decrease exponentially, so when the execution nodes are distributed, all the tasks currently arrived are arranged globally; the number of parallel compilation sub-threads of the total splitting and the distributed compilation optimization Modelica task parallel compilation load are set in the post-compilation stage, aiming at solving the problem that the Modelica model cannot be distributed compiled, improving the overall compilation efficiency of the system, and reducing the slow simulation speed problem caused by the single-machine computing power problem.

The invention relates to the field of network communication, in particular to a software differential packet processing method, device and equipment. According to the method, firstly, after the differential request is responded, the intranet storage address is included in the exclusive downloading thread pool instead of single-thread one-time downloading, and the thread count can be flexibly allocated according to hardware resources; then file fragmentation downloading is carried out, a large file is split into small fragments for parallel transmission, network bandwidth fluctuation can be adapted, and insufficient bandwidth utilization rate or congestion during single-thread transmission is avoided; and meanwhile, the fragmentation mechanism supports accurate retry failure fragmentation, and a complete file does not need to be replayed. Through combination of thread pool dynamic scheduling and fragmentation parallel transmission, waste caused by resource exclusive occupation of a single thread is avoided, the data transmission amount in unit time is increased through multi-thread parallel, the fragmentation size and the thread number can be flexibly adjusted by adapting to different hardware resources and bandwidth conditions, finally the downloading efficiency is remarkably improved, and the downloading efficiency is improved. The problems of resource waste and low efficiency in the prior art are solved.

The invention provides a thread allocation method and electronic equipment, and belongs to the technical field of resource allocation. According to the method, when the number of nodes in a ready node queue is larger than the number of idle threads, the priority score of each node in the ready node queue is calculated according to the time consumption mean value, the time consumption standard deviation and the executed probability corresponding to each node, and then the threads are distributed to each node according to the priority score corresponding to each node. And distributing threads to the nodes with higher priority scores preferentially. And according to the time consumption mean value, the time consumption standard deviation and the executed probability corresponding to each node, calculating the priority score of each node in the ready node queue, so that the time consumption can be minimized under the constraint that each DAG processing is not overtime, and the priority decision accuracy can be improved. Meanwhile, under limited thread resources, through a priority strategy, the time consumption of micro-service interface processing can be automatically optimized, the timeout rate is reduced, the interface response speed is improved, and machine resources are saved.

The present application relates to the technical field of equipment life cycle management, and specifically includes an aviation equipment full life cycle management method and system based on digital threads, the method comprising: acquiring process parameters of each stage, determining first digital thread master data and a first layer of associated test sequences, second digital thread master data and a second layer of associated test sequences, mapping the deviation of the physical equipment state and the virtual simulation state, and identifying the performance difference point under the cross-stage data correlation link; setting a management strategy for collaborative optimization. The technical problem of being unable to identify the cross-stage performance difference point equipment performance fluctuation root cause positioning in real time, causing the reliability of aviation equipment to be limited is solved, the first and second digital thread master data and the associated test sequences are constructed, the deviation of the physical equipment and the virtual simulation state is accurately mapped, the management strategy is set in combination with the double thread data and the performance difference point, and full life cycle collaborative optimization is performed, and the technical effect of effectively improving the equipment reliability is achieved.

The invention discloses a Falcon signature loop structure parallel acceleration method based on a GPU (Graphics Processing Unit) shared memory. The method comprises the following steps: S1, distributing the shared memory and sharing data in a thread block; s2, performing parallel circulation; s3, performing thread synchronization; and S4, result combination and output. And S5, a Falcon signature result is obtained. According to the method, a large temporary storage space originally distributed on a function stack is optimized into GPU shared memory distribution, and a serial loop structure related to large-scale polynomial operation is transformed into a parallel execution mode of a fixed thread count (such as 128 threads), so that the execution efficiency of Falcon signature operation is greatly improved on the premise of ensuring the calculation correctness, and the execution efficiency of the Falcon signature operation is improved. The memory occupation is reduced, and the expandability and practicability of the algorithm in a high-dimension and large-scale application scene are enhanced.

The invention discloses a data processing method and a related device, and the method comprises the steps: obtaining a first backlog data volume of a message queue at a first time, and obtaining a second backlog data volume of the message queue in a first time period, the first time period being a time period before the first time; determining whether the message queue satisfies a thread pool adjustment condition based on the first backlog data volume and the second backlog data volume; under the condition that the message queue meets the thread pool adjustment condition, adjusting the thread number of a current thread pool of the message queue to obtain a first thread pool of the message queue; and processing the data in the message queue based on the first thread pool. According to the method, the running state of the message queue can be tracked in real time, so that the adjustment opportunity is accurately determined, the thread pool of the message queue is accurately and dynamically adjusted, and abnormity of the message queue is avoided.

The application discloses a kind of real-time database monitoring methods based on ordered queue, including according to system design throughput and machine configuration, set initial parameter respectively set working thread and distribution thread number and memory capacity alarm threshold, according to the number of working thread adjustment shunt storage queue number ensure that thread queue number match, management node starts, start receiving heartbeat request, and according to preset hash algorithm is shunted storage, new received heartbeat request is stored in ordered double linked list queue head, then according to host IP information to hash table inquires whether heartbeat record exists, if the new received heartbeat record does not exist, for new online client, client information is stored to hash table then triggers new online event and persists to client database table write-in process ends.The application solves the problem that a large amount of data heartbeat information detection detection complexity rises exponentially with data volume rising by hash and double linked list structure.

The invention provides a multi-thread rapid sleep wake-up control method and system. According to the technical scheme, any multiple threads can directly enter a sleep state according to stored thread data; dormancy and awakening of the threads are controlled in a conditional variable mode, so that the threads can be awakened according to a set sequence conveniently, and the problem of awakening time sequence is solved; a unified dormancy interface is used, direct dormancy is achieved after any thread is called, and use is easy; in the thread wakeup stage, all threads can be pulled up according to a set sequence only by calling a wakeup interface once. And the application program is quickly recovered to normal execution. According to the method and the device, the sleep and wake-up efficiency of the threads can be effectively improved under the condition of many threads; hibernation awakening operation is performed through a unified interface, so that the complexity of software can be greatly relieved; the control drive is used for management, switching between a normal mode and a dormant state can be carried out, software design is simplified, and robustness and stability of the system are improved.

The invention provides a method and a device for adaptively adjusting concurrency of consumer threads. The method comprises the following steps: acquiring consumer offset and log end offset of each partition of a consumer, calculating message accumulation amount of each partition, and summarizing to obtain current message accumulation amount of the consumer; based on the current message accumulation amount, a target thread count is calculated in combination with a preset rule or a dynamic index, and the dynamic index at least comprises a change rate of the accumulation amount within a preset time and a single-thread average message processing throughput; and under the condition of determining that the preset adjustment condition is met, adjusting the core thread count and the maximum thread count of the thread pool of the consumer to the target thread count through the interface of the Kafka consumption task executor. Based on the method, the processing capacity of the consumer can be matched with the real-time message load, the resource utilization rate is improved while the processing delay is reduced, and the stability of the system adjustment process is guaranteed.

This invention discloses a multi-threaded dynamic task execution method and system. The method includes: receiving an original task processing request containing formatted task data; generating a task processing request to be executed based on the original task processing request; creating a thread for the formatted task data in the task processing request to be executed and enabling a thread lock; processing the formatted task data with the thread lock enabled to generate formatted task data to be segmented; when the number of formatted task data to be segmented exceeds a set threshold for the amount of task data to be executed by a single thread, segmenting the formatted task data to be segmented according to a pre-set task data segmentation rule to generate multiple task datasets; and executing the formatted task data in the multiple task datasets using multiple threads according to a set number of threads that can be enabled. This method and system reduce the number of interactions and improve response efficiency when there are a large number of concurrent user requests.

Embodiments of the present invention disclose a method, apparatus, device, storage medium, and product for reducing multidimensional tensors. The method includes: determining m reduced dimensions and n-m unreduced dimensions of an n-dimensional tensor; generating a reduction calculation instruction; after the reduction calculation instruction is executed, K thread blocks are started on an artificial intelligence chip, and the K thread blocks perform reduction calculations in parallel on the m reduced dimensions; the number of threads contained in each thread block is associated with the number of elements contained in the m reduced dimensions, and K is associated with the number of elements contained in the n-m unreduced dimensions, and each thread in each thread block reads its own reduced data in parallel; and the reduction calculation instruction is sent to the artificial intelligence chip, which executes the reduction calculation instruction. Reduction calculations can be performed on any dimension of a multidimensional tensor, improving versatility, reducing calculation time, and improving calculation efficiency.

The application relates to the field of big data, in particular to a task scheduling method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring all candidate tasks in a distributed system and the task concurrency quantity of each candidate task; the candidate task represents a task in the distributed system which has been executed by a fault tolerance mechanism; acquiring the minimum idle thread quantity of a key service node corresponding to each candidate task; adjusting the task concurrency quantity of the corresponding candidate task according to the minimum idle thread quantity of each key service node, so as to obtain the target task concurrency quantity of each candidate task; the target task concurrency quantity is the task quantity that each key service node can execute the corresponding candidate task. By using the method, the risk that the candidate task may still be blocked when being executed again can be reduced.

The invention belongs to the technical field of vehicle software development, and particularly relates to a system and method for a non-real-time system to achieve hard real-time in hardware-in-loop simulation, and the system comprises a computer device, a hardware-in-loop simulation device and a hardware-in-loop simulation device. The computer device is configured to execute a task creating module and a task thread setting module; wherein the task creating module is configured to create a real-time model task and form a task program; the task thread setting module is configured to enable a task program to automatically read computer configuration and judge the number Z of processor cores of the current computer device, when the number Z of the cores does not exceed X, the thread number n is set to be equal to 1 so as to execute a real-time model task, otherwise, the thread number n is set to be equal to (Z-X) / Y so as to execute the real-time model task, wherein Y represents the number of threads of one physical core of the processor.

The application discloses a distributed scheduling method and device under a micro-service architecture and a medium. The method comprises the following steps: determining a scheduled task to be distributed, wherein the scheduled task refers to a business logic executed according to a set timer rule; according to different business types, dividing servers into a scheduling execution micro-service unit and a business micro-service unit, and distributing the business micro-service unit; determining a tenant database corresponding to the scheduling execution micro-service unit, wherein the tenant database only allows the corresponding scheduling execution micro-service unit to access; determining the number of threads corresponding to the scheduling execution micro-service unit and the working state of the threads, and determining scheduled task information; and according to the number of threads, the working state of the threads and the scheduled task information, distributing the scheduled task in the tenant database to other scheduling execution micro-service units and business micro-service units through the scheduling execution micro-service unit.