726 results about "Scheduling (computing)" patented technology

The invention discloses an energy-efficient task scheduling method for an edge computing system, which comprises the following steps of: acquiring a task state, a computing node resource state, a link state and an energy consumption state according to a unified time slot under a computing power network control domain, and constructing a system state vector; performing priority evaluation on the to-be-scheduled task based on the residual delay budget, the candidate node energy efficiency coefficient and the queue position to obtain a target task set; inputting a system state vector and a target task set into an energy efficiency perception deep reinforcement learning scheduling model, outputting a task-node allocation decision under the constraint of computing node resources and task time delay, and introducing a system-level energy consumption ratio, self-adaptive energy consumption penalty and exploration bias facing high-energy-efficiency nodes into rewards; and scheduling tasks according to the allocation decision, recording state transition and instant rewards, updating a double-commentator and actor network, and performing iterative execution in continuous time slots. According to the method, task success rate, time delay, load balancing and energy-saving performance are considered, and system energy consumption is reduced.

The invention discloses a distributed training scheduling and communication optimization method and system of a multi-modal large model on a domestic computing power platform. The method comprises the following steps: virtualizing a heterogeneous computing unit of a preset platform into a virtual device pool, and fusing first-order gradient of a multi-modal sample and Hessian matrix information based on quantitative perception training to generate a sample sensitivity grading atlas; virtual device pool attributes and the sensitivity grading atlas are used as input, an optimal hybrid parallel configuration scheme is automatically generated through a configuration search algorithm, and a parallel combination mode, resource mapping and a high-sensitivity sample scheduling strategy are defined; a distributed training code of an integrated communication optimization strategy is automatically generated according to a configuration scheme, pipeline parallel communication and data parallel gradient synchronization constraint are executed in a topology adjacent equipment subset, and a hierarchical aggregation mechanism is adopted; and dynamically screening a core training set and scheduling a calculation task to complete distributed training. According to the method, efficient cooperative training of the multi-modal large model on the domestic computing power platform is realized.

The invention discloses a task scheduling method based on predictable resource state atlas modeling, a platform is oriented to a heterogeneous computing environment, a unified resource state atlas is constructed by collecting multi-dimensional resource state parameters of computing nodes, and performance characteristics and communication topological relations among the nodes are comprehensively described. On the basis, a bidirectional time sequence model and an attention mechanism are fused, and the load trend of each node in a future short time is predicted. The platform constructs a multi-factor scheduling scoring function based on a task feature vector and resource state prediction map, integrates parameters such as resource matching degree, prediction load, communication delay and energy consumption cost, dynamically evaluates the adaptability of tasks and resources, and realizes adaptive scheduling and optimal resource allocation of the tasks. Compared with the prior art, the method has the advantages of being high in resource state predictability, high in task allocation intelligence degree, outstanding in platform evolution capability and the like, and is suitable for intelligent task scheduling application in a large-scale heterogeneous resource environment.

The invention discloses a power consumption and performance balanced scheduling method and system for a heterogeneous multi-core processor, and belongs to the technical field of computers, and the method comprises the steps: obtaining historical load data and current system state parameters, generating a load sequence and carrying out load prediction analysis, generating a predicted load value for feature fusion and quantification, and generating a task feature vector; constructing a core capability portrait, calculating a matching degree between the task characteristic vector and the core capability portrait, and generating a scheduling strategy; and dynamically combining the physical cores into a logic calculation unit according to a scheduling strategy, configuring a power consumption state, and executing task allocation and migration. According to the method, the technical means of load prediction analysis, quantitative matching of tasks and cores and dynamic combination and configuration of physical cores are adopted, and pre-judgment and prospective distribution of system resources can be achieved, so that performance requirements are met, meanwhile, the system energy efficiency is effectively optimized, and deep balance of power consumption and performance of a processor is achieved.

The invention provides a heterogeneous computing power resource pooling scheduling platform which comprises a task and resource tensor construction module, a scheduling decision module, a breakpoint state generation module and a rescheduling and recovery module. According to the platform, task requirements and node resources are modeled into a tensor structure in a unified mode, and accurate initial scheduling is achieved through a structured scoring function containing conditional penalty terms. In the task running process, interruption judgment is carried out based on the node dynamic state and the scheduling score, and a breakpoint state tensor containing an execution state is generated. When interruption occurs, a proper node is selected through compatibility screening, and task seamless migration and execution recovery are realized by utilizing the stored state. According to the method, the technical problems of non-uniform resource expression, lack of scene adaptability and incapability of realizing uninterrupted migration in a heterogeneous computing power environment are solved, and the resource utilization rate and the service continuity are remarkably improved.

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 computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention provides a BLAS3 structured operator accelerated computing system based on a Hopper architecture GPU, and relates to the technical field of computers. The system comprises: a calculation unit discrimination module for determining a calculation unit used by a current operator during operation, and estimating the maximum row dimension upper bound of the current operator in a tensor core execution path; an instruction sensing block parameter determination module dynamically determines the optimal block size and number of the input matrix in real time; the block matrix loading and aligning module divides an input matrix and a matrix to be updated into sub-matrixes by taking the block size as a basic block and completes loading of the corresponding sub-matrixes; the operator kernel function execution module completes shared memory structured parallel loading and storage of a double-precision floating-point number array of a sub-matrix corresponding to the input matrix, and calls a tensor core to carry out multiply-add accumulation calculation; and the assembly line and concurrent scheduling module adds the block calculation tasks into corresponding task sets and performs multi-stream concurrent scheduling on the task sets.

The invention discloses a heterogeneous resource energy-saving optimization scheduling method and system based on computing power perception, and relates to the technical field of modern computing infrastructure, the system comprises a computing power-energy consumption evaluation module, the computing power-energy consumption evaluation module is used for carrying out computing power and energy consumption evaluation on heterogeneous resources, generating and dynamically updating a computing power-energy consumption mapping table, the heterogeneous resources comprise ARM architecture resources, X86 architecture resources and GPU resources. According to the method, the computing power-energy consumption mapping table is generated and updated in real time, so that the scheduling decision is always based on the latest energy efficiency state of the resources, the accuracy of the basic data is ensured, intelligent task classification and dynamic constraint construction are combined, the system can flexibly balance the task performance and the energy consumption target, and particularly, the strategy can be adaptively adjusted for a high-emergency task; the timeliness of key services is guaranteed, the differentiated scheduling strategy serves as an efficient pre-screening mechanism, task requirements and resource architecture characteristics are accurately matched, and the pertinence and efficiency of resource allocation are remarkably improved.

The invention relates to the field of resource adaptive scheduling, and discloses an intelligent door lock control method and system based on deep learning, and the method comprises the steps: obtaining the system load, execution time, resource occupancy rate and other data, and obtaining the overall performance state description; according to the state description, a dynamic conflict quantized value is calculated, execution time deviation is evaluated, and a reasoning stage list needing to be adjusted is determined; extracting fluctuation trend characteristics based on historical records, and optimizing a resource allocation proportion to obtain an adjusted execution strategy scheme; judging whether the actual accuracy rate reaches the preset accuracy rate or not by applying the strategy scheme; if yes, the cycle interval duration of the self-adaptive control mechanism is determined; and scheduling the next monitoring process through the cycle interval duration, repeating the dynamic conflict evaluation, and dynamically regulating and controlling the execution strategy scheme. According to the method, real-time and self-adaptive allocation of computing resources of the door lock control system can be realized, dynamic conflicts are effectively solved, and millisecond response speed and recognition accuracy under high load are guaranteed.

The embodiment of the invention provides a distributed model training method and device, a server and a storage medium, and relates to the technical field of distributed model training. Determining a target sub-model according to the training dependency relationship among the sub-models, and determining a target computing node according to a resource configuration parameter and a model training parameter of the target sub-model and the resource state of each computing node; creating a plurality of training tasks corresponding to the target sub-model according to the resource configuration parameters and the model training parameters of the target sub-model, allocating each training task to each target computing node, and controlling each target computing node to perform model training; and under the condition that the target sub-model is successfully trained, determining the next to-be-trained sub-model as a new target sub-model, and carrying out model training on the new target sub-model until all the sub-models are successfully trained, so that the flexibility and adaptability of resource allocation in the training process can be improved, and the training efficiency is improved. The problems of long resource occupation period, insufficient utilization and scheduling stiffness are solved.

The invention discloses a heterogeneous computing network resource collaborative scheduling optimization method based on self-adaptive multi-agent, and aims to solve the scheduling problem caused by resource heterogeneity, load dynamics and task high concurrency in a heterogeneous computing network system. According to the method, cross-domain resource collaboration is realized by constructing three sub-domain adaptive agents of a computing resource domain, a network resource domain and a storage resource domain and a global collaboration layer. According to the method, a deep reinforcement learning algorithm and an 'LSTM + GNN' fusion model are integrated, and multi-target adaptive optimization of resource utilization rate, task time delay, service quality and energy consumption is achieved through closed-loop optimization of state perception, strategy generation, value evaluation and strategy updating. The heterogeneous computing network resource fine-grained sensing, cross-domain cooperative scheduling and multi-target dynamic optimization are realized, the resource utilization rate and the task completion rate are high, the service quality and the energy consumption performance are good, and the dynamic response capability and the overall performance of the heterogeneous computing network system are improved.

The embodiment of the invention provides a multi-agent collaborative scheduling method based on federal reinforcement learning and digital twinning, which belongs to the technical field of computing, and specifically comprises the following steps: importing a test task list, environmental parameters and an agent capability matrix; constructing a mathematical model of a scheduling decision; based on a digital twinborn model of a physical entity, rehearsing a scheduling process and outputting a pre-optimization strategy set containing a conflict avoidance strategy; independently executing a deep reinforcement learning algorithm locally, generating a private experience pool, and updating local model parameters according to the private experience pool; the aggregation server calculates an aggregation weight based on the performance index of each agent, and adopts a weighted average strategy to aggregate local model parameters of each agent; dynamically allocating tasks to corresponding agents and monitoring task execution states; real-time resource competition among the intelligent agents is solved through an evolutionary game mechanism; and performing scheduling ending evaluation and iteration. Through the scheme of the invention, the scheduling efficiency, security and robustness are improved.

This invention discloses a full lifecycle management system and method for scientific computing programs. The system includes a build environment subsystem and a production environment subsystem. The former provides computer resources for the build process of the scientific computing program throughout its lifecycle, while the latter provides computer resources for the testing and deployment processes. This invention, through a full lifecycle management method for scientific computing programs, operates on corresponding computing resources, encompassing a series of steps including querying, triggering scheduling, build execution, result distribution, and test deployment. It automatically generates the executable file of the scientific computing program and configures its runtime dependencies. Simultaneously, it automatically generates a corresponding description file recording the entire lifecycle process. Furthermore, based on version management of these description files and their sets, it achieves full lifecycle traceability and cross-platform migration and deployment of scientific computing programs in a high-performance computing environment.

A real-time target detection method and system based on RTSP flow and NPU collaborative optimization belong to the technical field of computer vision and artificial intelligence, and are characterized by comprising the following steps: adopting dynamic memory optimization management of a hybrid pipeline architecture, and performing single-time continuous copying through a CPU (Central Processing Unit); through deep integration of innovative technologies such as DMA direct transmission NPU continuous memory pool management, dynamic batch processing scheduling, hybrid assembly line processing, intelligent equipment load balancing, parallel preprocessing optimization and vectorization post-processing, RTSP flow collaborative optimization and the like, the NPU utilization rate is improved to 85% or above, the overall average FPS is improved by 200% or above, the assembly line parallelism degree achieves three times of performance gain, and the production efficiency is greatly improved. The data transmission delay is reduced by 80%, the system stability is remarkably improved, performance degradation is avoided after long-time operation, and the method is suitable for various scenes such as edge calculation and cloud reasoning.

The invention relates to the field of computing power scheduling, and discloses an artificial intelligence-based computing power scheduling method, which comprises the following steps of S1, full-dimensional data acquisition: acquiring hardware resource data, task operation data, user demand data and heterogeneous computing power characteristic data through distributed sensing nodes; s2, data processing and feature construction: preprocessing the data collected in the S1, extracting multi-dimensional features through feature engineering, and constructing a feature matrix adapted to an AI model; s3, computing power demand intelligent prediction: learning the feature data preprocessed in the S2 by using a federal deep learning model, and outputting time-phased and type-divided computing power demand prediction results; and S4, scheduling strategy optimization generation. Hardware resources, task operation, user requirements and heterogeneous computing power characteristic data are comprehensively collected by relying on edge-core two-level distributed sensing nodes, the collection frequency can be dynamically adjusted between 50ms and 1mi n through a self-adaptive algorithm, and real-time performance and resource economy are both considered.

The invention provides a cloud edge collaboration algorithm arrangement and deployment method, system and device and a storage medium, and relates to the technical field of cloud edge collaboration.The method comprises the steps that algorithm task requirements input by a user are analyzed, and after corresponding task information is obtained, an intermediate representation file is generated; collecting computing power state information of the plurality of edge devices to determine a target edge device used for executing an algorithm link, and generating an operation parameter configuration file according to a hardware architecture of the target edge device and algorithm characteristics of the intermediate representation file; and issuing the intermediate representation file and the operation parameter configuration file to the target edge device, and performing automatic compiling and deployment according to the chip type of the target edge device. By issuing the intermediate representation file and the operation parameter configuration file to the target edge device and automatically executing compiling and deployment, the effects of cross-platform automatic adaptation, rapid deployment and efficient operation are achieved, and the problem that in the prior art, task organization and deployment flexibility is insufficient is solved.

The invention discloses a power grid scattered resource aggregation scheduling method and device based on block chain data, terminal equipment and a storage medium, and the method comprises the steps: allocating a scheduling weight for each block chain node, and converting the scheduling weight into a probability space; and then a random entropy value is generated through a verifiable random function, a historical packaging node private key and a block chain hash value so as to ensure the randomness when a packaging node is selected through the random entropy value subsequently, so that the packaging weight of the node and the qualification weight of the node participating in power dispatching in a block chain consensus process can be bound; according to the method and the system, the probability of nodes with high scheduling weights is higher through a weight and probability space mapping mechanism instead of hardware computing power, and the monopoly of packaging weights is avoided by combining generation of random entropy values, so that each block chain node is possible to obtain the packaging weights, and a final scheduling decision can realize global optimization scheduling.

The invention provides an instruction-level simulation and performance modeling system for a parallel computing architecture, and belongs to the technical field of computer architecture and simulation verification, and the system comprises an instruction modeling layer which is used for analyzing and executing an intermediate instruction set defined by the architecture; the scheduling execution layer is used for simulating a multi-thread and multi-core parallel execution process; the storage access layer is used for constructing a hierarchical storage access and bandwidth and delay model; and the performance analysis layer is used for collecting and counting key indexes such as an execution period, an instruction utilization rate and memory access delay, and realizing accurate performance modeling of the parallel architecture. According to the method, the performance bottleneck of the design scheme can be rapidly evaluated in the early stage of architecture design, the simulation speed is high, the module configurability is high, the modeling precision is adjustable, and the method is suitable for functional verification, micro-architecture exploration and compiler performance analysis of parallel computing architectures, accelerator chips, heterogeneous multi-core processors and the like.

The invention particularly relates to a task scheduling method and system based on a distributed scheduling framework, and relates to the technical field of distributed computing and task scheduling. The resource coordinator is connected with a conflict detection module; a conflict resolution arbitration module; and a final consistency state synchronization module. According to the invention, the lock-free design of the optimistic scheduling decision module and the local cache mechanism break through the dependence of the traditional scheduling on the global real-time consistent state, and realize the top-speed decision in the high-concurrency scene; the local cache with timeliness deviation adopts a hierarchical index structure and a dual-mode updating mechanism, so that a scheduler is supported to complete calculation only by depending on local data while the cache freshness and the updating overhead are balanced, cross-node real-time communication is not needed, and the decision delay is lower.

The invention discloses a method for heterogeneous scheduling of computing power under a large model training reasoning framework, relates to the technical field of computing power scheduling, and solves the technical problem that task interruption or resource waste is easily caused by incomplete task migration, splitting and recovery mechanisms when computing power is insufficient or equipment is abnormal. According to the method, parameters such as the load rate, the residual computing power and the temperature of the CPU, the GPU and the FPGA are dynamically collected, the calculation amount, the duration and the delay requirement of the task are combined, a quantitative model is established, the resource utilization rate is increased, a temperature prediction model is established in stages, a dynamic threshold value is set in combination with the equipment type, the environment temperature and the health degree, temperature early warning and migration in the task execution process are achieved, and the task execution efficiency is improved. The frequency reduction risk caused by overheating of equipment is reduced, the task interruption loss is ensured to be reduced by more than 50% through task splitting, intelligent migration and pause recovery mechanisms aiming at insufficient computing power or equipment abnormity, and the stability of the system during peak flow or hardware failure is improved.

The invention discloses a cryptanalysis task breakpoint continuation type fault-tolerant scheduling method, which relates to the technical field of distributed computing scheduling, and is characterized by comprising the following steps of: acquiring task attribute information, historical scheduling data and distributed node running state data of a cryptanalysis task; performing feature recognition on the task attribute information to obtain a task feature result, and matching the task feature result with tasks and nodes in the node operation state data to determine fault-tolerant scheduling core parameters; the method has the advantages that accurate breakpoint positioning and continuous calculation state recovery are achieved in combination with the breakpoint continuous calculation model, repeated calculation after task interruption is avoided, time and resource consumption is greatly reduced, the execution continuity of a cryptographic analysis task is improved, the method is convenient to adapt to a large-scale distributed resource scene of an E-level super-calculation cluster, and the method is suitable for large-scale distributed resource scenes of the E-level super-calculation cluster. The parallel computing power advantage of the E-level super computing cluster can be fully exerted, and the execution continuity of the cryptographic analysis task is improved.

The embodiment of the invention provides a task processing device, a task processing method, electronic equipment and a storage medium. The task processing device comprises a calculation scheduling core particle and a function core particle which are integrated in a single package, the calculation scheduling core particle comprises a hardware subsystem and a calculation core, and the hardware subsystem is configured to receive and analyze task information to obtain at least one task. And assigning at least one task to at least one of the functional core and the computing core according to the task type; the computing core is configured to execute computing operations according to tasks allocated by the hardware subsystem, and the functional core particles comprise at least one of a quantum computing management core particle and a ray tracing core particle. According to the task processing device, through a unified task scheduling distribution mechanism, the adaptive capacity of the task processing device in an end-side mixed task scene is remarkably improved, so that a single computing chip can efficiently support various heterogeneous workloads.

The invention provides a cross-security domain computing power resource federation and privacy protection system. The system comprises a scheduling management node, a communication gateway, a plurality of computing nodes, a verification node and a key management node. The scheduling management node decomposes the calculation general task into a plurality of subtasks and plans different node execution paths; the communication gateway encapsulates the subtask data into an encrypted data packet containing a position identification segment and an encrypted data segment which can be independently decrypted; the computing node is integrated with the trusted execution environment to provide hardware-level security isolation; the summarizing node is used for receiving sub-task results which are processed by the computing nodes and comprise encrypted data segments; the verification node realizes calculation integrity verification; and the key management node confirms a task completion state by collecting the position identification segment, and coordinates and starts a data segment aggregation decryption process. According to the method, the data and code privacy of the computing task is ensured while the computing power island is broken, and a cross-domain computing power resource sharing mechanism with balanced safety and performance is established.

The invention discloses a heterogeneous computing power resource allocation method and system, and belongs to the technical field of computing power scheduling. The method comprises the following steps: analyzing an edge task through a content value analysis model, and generating a task label containing real-time, accuracy and exploratory demand scores; based on the task labels, a distributed computing power distribution protocol is utilized to match and distribute computing power resources for the tasks, and primary distribution and execution are completed; in the task execution process, the data value is evaluated through the lightweight evaluation model; if the evaluation value exceeds a dynamic threshold value, a computing power scheduling model agent is triggered to perform centralized secondary distribution, and the agent decides an optimal uploading path based on deep reinforcement learning and adjusts the threshold value so as to efficiently upload high-value data to a headquarter cloud and drive a large model to autonomously evolve. Through the double-track parallel architecture, the resource conflict between the real-time guarantee of the edge task and the evolution of the cloud model is solved, and the efficient and self-adaptive allocation of the computing power resource is realized.

The invention is suitable for the technical field of computers, and provides a CPU and GPU cooperative work heterogeneous computing method and platform, and the method comprises the following steps: S1, carrying out the meta-task analysis of an input computing task, extracting the computing feature metadata of the computing task, and predicting the performance of the computing task based on a pre-trained performance prediction model; dynamically deciding execution path planning of the task between the CPU and the GPU; s2, according to the execution path planning, carrying out adaptive resource collaborative configuration; and S3, on the basis of the calculation feature metadata and the current hardware state, through a parameterized kernel template or a just-in-time compilation technology, heterogeneous perception optimized kernel codes are generated. The method effectively solves the problems that a task scheduling strategy is rigid, the bottleneck of memory and data transmission is prominent, calculation kernel optimization is insufficient and is lack of adaptability, and a system is lack of self-evolution and learning ability.

The invention discloses a multi-architecture chip compatible localized cloud platform virtualization method, which is suitable for a localized cloud computing environment constructed by taking a localized CPU (Central Processing Unit) as a core. Comprising the following steps: when a plurality of domestic CPUs and compatible architecture information are collected at a bottom layer of a domestic cloud platform, establishing a unified hardware abstract model; constructing a virtualization compatibility layer on a domestic cloud platform virtual machine monitor; designing a cross-architecture virtual device agent module, and redirecting a domestic peripheral driver; and a unified scheduling and security isolation strategy is constructed, and efficient cooperation and trusted operation of the multi-architecture virtual machine under the localized cloud platform is realized. According to the method, instruction-level compatibility and unified operation environment construction of the heterogeneous chip in the localized cloud platform are realized, and the performance and stability of cross-architecture migration of the virtual machine are remarkably improved.

The invention discloses a PCIe-based CPU, NPU and GPU interconnection cluster management method, and belongs to the technical field of interconnection cluster management. According to the method, an interconnection link is established through a PCIe switch, and bandwidth is dynamically allocated by means of the link to adapt to task requirements; multi-priority data transmission is supported, and bandwidth resources are distributed in order according to priorities; a multi-element operation mode is configured in advance, and physical and virtual computing power resources are distributed according to needs; and an equipment state monitoring mechanism is established, idle equipment enters a sleep mode, and meanwhile, all transmission links are combed and an optimal path is selected. According to the method, fine scheduling of cluster resources is realized, the bandwidth and computing power resource utilization rate is improved, the adaptation capability to different tasks is enhanced, the orderliness and stability of data transmission are guaranteed, the cluster operation energy consumption is reduced, and efficient and stable operation of the interconnected cluster is facilitated.

The invention provides a task scheduling method and device, a storage medium and electronic device.The method is applied to a runtime library of a host and comprises the steps that in response to a received calculation task, a kernel driver is called to obtain performance data of at least two core particles, and a scheduling score corresponding to each core particle is determined according to the performance data; scheduling the calculation task to the corresponding core particle for execution based on the scheduling score; the scheduling score is determined on the basis of the performance data of the core particles, task scheduling is executed, accurate adaptation of the calculation task and the core particles is achieved, and the task execution efficiency and the resource utilization rate under the multi-core-particle architecture are effectively improved.

The invention discloses a high-speed hardware acceleration system for a Kyber anti-quantum cryptography algorithm and an implementation method, and relates to the technical field of hardware acceleration of the anti-quantum cryptography algorithm, the high-speed hardware acceleration system comprises a dynamic resource management and scheduling center, a reconfigurable NTT computing cluster unit, a streaming polynomial coefficient cache network and a runtime security scheduler; and the dynamic resource management and scheduling center is respectively connected with the reconfigurable NTT computing cluster unit, the streaming polynomial coefficient cache network and the runtime security scheduler. According to the high-speed hardware acceleration system for the Kyber anti-quantum cryptography algorithm and the implementation method, the overall operation throughput rate and the response speed of the system are effectively improved, idle loss caused by fixed resource allocation or data waiting is reduced, the flexible reconstruction and reuse capability of hardware resources is improved, and the implementation efficiency is improved. Therefore, the same set of physical unit can efficiently adapt to different core operation modes in the Kyber algorithm, and the hardware utilization efficiency is improved.