191 results about "Swarm computing" patented technology

Methods, systems, and computer program products for high-performance cluster computing. Multiple components are operatively interconnected to carry out operations for high-performance RDMA I / O transfers over an RDMA NIC. A virtual machine of a virtualization environment initiates a first I / O call to an HCI storage pool controller using RDMA. Responsive to the first I / O call, a second I / O call is initiated from the HCI storage pool controller to a storage device of an HCI storage pool. The first I / O call to the HCI storage pool controller is implemented through a first virtual function of an RDMA NIC that is exposed in the user space of the virtualization environment. Prior to the first RDMA I / O call, a contiguous unit of memory to use in an RDMA I / O transfer is registered with the RDMA NIC. The contiguous unit of memory comprises memory that is registered using non-RDMA paths such as TCP or iSCSI.

The invention discloses an improved vibration compensation method suitable for an atom interference gravimeter. The method comprises the following steps: acquiring an atom interference signal and a vibration voltage signal output by a seismometer; initializing population parameters of the improved whale optimization algorithm, wherein the population parameters comprise a search space of a population size, a maximum number of iterations, a gain coefficient K and a delay coefficient tau; the method comprises the following steps: generating an initial whale population by adopting a method of combining Logistic chaotic mapping and Latin hypercube sampling LHS; calculating a phase shift caused by vibration noise and an atomic interference phase delta phi, and calculating the fitness of the whale individual by taking a root-mean-square error of compensated interference fringes as a target function; iteratively updating the population position through an improved whale optimization algorithm, outputting an optimal gain coefficient and an optimal delay coefficient, and obtaining an atomic interference signal after vibration compensation; according to the atomic interference gravimeter vibration compensation method, the global search capability, the local development precision and the stability of a vibration compensation algorithm are improved.

The invention discloses a tensor processing architecture based on reconfigurable calculation, a working mode of the tensor processing architecture and an FPGA (Field Programmable Gate Array) development board. The overall architecture comprises a QSFP interface, a DDR storage module, a memory access control module, a microcode control module, a matrix module, an on-chip shared storage module, a control network, an RISC-V processor, a reconfigurable computing interface module, a reconfigurable computing module, a reconfigurable configuration module and a result return network. The working modes of the overall architecture are divided into two modes: a CPU control mode and a cluster computing mode, wherein the CPU control mode is used for controlling global computing and output through a CPU during single-chip computing; the cluster calculation mode is used for being connected with an upper computer to cooperate with the upper computer to execute calculation tasks such as matrix calculation; the whole architecture is realized through an FPGA (Field Programmable Gate Array) development board, and the FPGA development board adopts Virtex UltraScale + VU13P as a main logic realization platform. Through the dynamic granularity configuration of the on-chip microcode, multiple operation modes can be realized, and the utilization rate of hardware is improved; and hardware realizes multiple configuration strategies, and the universality is high.

A first processing node of a cluster of processing nodes issues a first alert when first event data satisfies a trigger condition, and sends, to an alert data store external to the cluster, a first alert record of the first alert and suppression information based at least in part on the first alert. A second processing node of the cluster determines that second event data satisfies the trigger condition, obtains, from the alert data store, the suppression information indicating that an expiration time for suppressing the first alert is unexpired, and sends, to the alert data store, a second alert record of a second alert without issuing the second alert.

The invention provides an electric energy meter data distributed storage method based on an improved PoS consensus. The method comprises the steps of obtaining electric energy meter data, performing space-time characteristic analysis on the electric energy meter data, forming an electric energy meter cluster, and constructing an electric energy meter data space-time characteristic model; acquiring block chain network node information, and calculating a node performance index and network delay; based on an electric energy meter data space-time characteristic model, a node performance index and network delay, nodes are grouped to form a node cluster, and the affinity between the electric energy meter cluster and the node cluster is calculated; and according to the affinity, distributing ammeter clusters, designing a data popularity evaluation function, formulating a hot data replication strategy and a life cycle management strategy, and outputting a spatio-temporal optimization fragmentation scheme. According to the method, the time-space characteristics and network topology of the power data are comprehensively considered, the optimization target that data are close to users and nodes are close to the data is achieved, and the performance and reliability of the power block chain system are improved.

The invention relates to the technical field of artificial intelligence and environmental information processing, discloses a carbon emission AI intelligent accounting method and system, and aims to solve the problems of weak data fusion capability, poor accounting real-time performance, insufficient model generalization and lack of self-evolution capability of the system in the prior art. The method comprises the steps that multi-source heterogeneous operation data are collected in real time, standardized management and spatial-temporal feature extraction are carried out, direct, indirect and implicit carbon emissions are calculated through a fusion graph neural network, a time sequence attention network and an accounting cluster of an integrated tree model, and online iterative optimization of model parameters is driven based on external calibration signals. According to the scheme, carbon emission accounting is converted into a high-precision minute-level response dynamic deduction mode, the transparency and traceability of accounting are remarkably improved, continuous self-adaptive optimization in a large-scale scene is supported, and reliable data support is provided for precise carbon reduction.

The invention relates to a charging robot path planning method. The method comprises the following steps: calculating an initial path from an initial grid to a target grid of a charging robot in a grid map by using an A * algorithm; dividing a search space determined by the initial path into sub search spaces; initializing in each sub-search space and expanding a population through a pseudo-reverse learning strategy; the obtained current population is randomly divided into two sub-populations, and the two sub-populations are respectively marked as a first sub-population and a second sub-population; for the first sub-population, iterative optimization is carried out through a first strategy according to the fitness, and for the second sub-population, iterative optimization is carried out through a second strategy according to the fitness; the first 50% of individuals in the first sub-population and the second sub-population form a third population; performing crossover operation on the No.3 population to obtain a current population; calculating the fitness of the current population individuals; and iteration is carried out until the number of iterations reaches the maximum number of iterations, or the optimal values of the two sub-populations tend to be the same, and an optimal path is obtained.

Technologies for automated security incident analysis include a computing device that clusters security incidents and runbooks into multiple clusters based on investigation similarity. For each cluster, the computing device determines a summary of all security incidents in the cluster with a large language model, determines criteria for inclusion of a security incident in the cluster, and determines a suggested investigation step with a retrieval augmented generation pipeline. The suggested investigation step includes a natural language description and a programmatic query. Upon receiving approval from a user, the computing device stores the cluster information in a curated query repository. The computing device may receive a security incident for investigation, assign the security incident to a cluster based on the stored criteria, and retrieve a suggested investigation step from the curated query repository. The computing device may provide the suggested investigation step to a user. Other embodiments are described and claimed.

The invention discloses a production scheduling optimization method and system based on multi-objective mixing, and the method comprises the steps: designing a first objective function for minimizing the late shift subsidy and a second objective function for minimizing the energy consumption cost, and determining a constraint condition; initializing a particle swarm, and calculating fitness values of each particle in the particle swarm corresponding to the first objective function and the second objective function; storing the non-dominated solution of each particle through an external storage library, calculating an entropy change value, a probability threshold value, a probability value and a random value of the external storage library, and determining a global optimal particle; updating each particle according to the global optimal particle, the entropy change value, the probability threshold and the constraint condition to obtain a non-dominated solution set; and selecting a compromise solution in the non-dominated solution set as a production scheduling optimization result through an approximate ideal solution sorting method. The method can reduce the subjectivity of weight setting, does not need repeated trial and error, improves the production efficiency and intelligent level of automobile production scheduling, and can be widely applied to the technical field of production scheduling.

The invention relates to the field of seismic exploration data processing, and particularly discloses a PySpark-based seismic data batch processing method and device, and the method comprises the steps: loading segment data through employing a segment library; the segment data is stored in different RDDs in a partitioning mode; defining, by a developer, a core function of data processing performed by each RDD block; constructing a module development template based on a core function of data processing performed by each RDD block; and outputting and storing a processing result of the module development template. According to the method provided by the invention, the parallelization processing of the calculation process is realized by carrying out block storage on the segment data, defining a core function for data processing and constructing a module development template, and meanwhile, the calculation efficiency and the processing speed are improved by utilizing the cluster calculation capability of Spark.

Disclosed in the present invention is a construction system for a big data analysis algorithm library, comprising: an analysis process construction module, an operator platform selection module, a code reverse generation module, a user code verification module, a cluster control host, and a cluster computing host. Cross-platform and cross-language algorithm fusion can be realized, and data analysis is realized in a complete and heterogeneous data analysis flow. Also disclosed in the present invention is a construction method for a big data analysis algorithm library.

The invention relates to the technical field of artificial intelligence, and discloses a self-adaptive control method and system applied to automobile battery faults, and the method comprises the steps: carrying out the hybrid coding of a fault diagnosis result and historical control parameters of an automobile battery, so as to obtain an initial particle swarm; constructing a multi-objective optimization function of the automobile battery; calculating a multi-objective optimization function value of each particle, and determining an individual optimal solution and a global optimal solution corresponding to the particle; dynamic weights and dynamic learning factors of the particles are generated, and then an updating model of the particles is constructed; updating the particles according to the updating model to obtain a new generation of particle population; and calculating a multi-objective optimization function value of each particle in the new-generation particle population, updating an individual optimal solution and a global optimal solution corresponding to the particles until the number of updating times reaches a preset iteration number threshold, and outputting an optimal control parameter to realize adaptive control of the automobile battery fault. According to the invention, the control efficiency applied to automobile battery faults can be improved.

The invention discloses a compilation system oriented to hybrid accelerator card cluster calculation and capable of guaranteeing numerical precision. The compilation system comprises an operator recognizer, an operation scheduler, a result comparator, a precision controller and a compiler. By strictly following the IEEE 754 standard and precisely scheduling hardware resources, the balance of high precision and high performance of numerical calculation is realized, so that numerical error accumulation is effectively reduced, and the consistency and reliability of calculation results are improved.

Provided in the present application are a three-dimensional reconstruction method, a three-dimensional reconstruction apparatus, a computing device cluster, a computer program product and a computer-readable storage medium. The method comprises: concatenating an input image, which is obtained by photographing a target scene, with a random variable, so as to obtain a concatenated tensor; implementing a first task, wherein the first task comprises converting, by means of a super-resolution network, the concatenated tensor into an output image having a resolution higher than the input image; implementing a second task, wherein the second task comprises training an NeRF neural network on the basis of the input image, a camera pose of the input image, and the output image; and jointly optimizing the first task and the second task, so as to further train the NeRF neural network. On the basis of the present application, high-resolution three-dimensional scene images with consistent viewpoints can be generated by means of the NeRF neural network.

The invention discloses a cluster computing resource allocation method. The method supports three resource service forms of a virtual machine, a container cloud and a bare computer. Wherein the virtual machine resources are used for deploying system software with a UI (User Interface) and a C / S (Client / Server) architecture; the container cloud resource is used for deployment of system software of a B / S architecture and operation of a large test sample; the bare computer resources are used for specific function system software, Kafka middleware and / or database deployment; and service computing resource elastic scheduling and control are deployed to realize cluster computing resource allocation. According to the method, multiple resource service forms are flexibly adopted, and virtual cluster management and high-availability design are applied, so that test resource automatic deployment strategy generation and computing resource flexible scheduling and control can be realized, and high-performance, high-reliability, servitization and easy-to-use cluster computing resource allocation and deployment services are provided for users.

The invention relates to a transformer area group optimization method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining voltage, current and frequency of each edge node in a to-be-optimized transformer area group, photovoltaic output data of the transformer area group, energy storage charge and discharge state data and electric vehicle charging power data, and presetting various operation constraints set for the transformer area group; and inputting the voltage, the current, the frequency, the photovoltaic output data, the energy storage charge and discharge state data, the electric vehicle charging power data and the various operation constraints into a pre-constructed multi-target optimization model, obtaining optimization strategies meeting the various operation constraints, and performing optimization guidance on the operation of the transformer area group according to the optimization strategies. The transformer area group level multi-objective optimization task is calculated through the cloud high-performance computing cluster, so that an optimization strategy which is more adaptive to the current transformer area group is obtained, edge setting is controlled according to the optimization strategy to execute optimization output, and the load balance and operation robustness of the transformer area group are ensured.

The invention belongs to the technical field of edge computing, and discloses an Internet of Things gateway collaborative management system based on edge computing, which comprises a resource virtualization unit used for deploying a protocol independence abstract engine in a gateway cluster, analyzing and adapting various Internet of Things protocols, constructing a proprietary protocol conversion matrix, obtaining standardized equipment attribute data, and storing the standardized equipment attribute data in a server; establishing an equipment shadow model based on the standardized equipment attribute data; the edge collaborative scheduling unit is used for outputting panoramic state data through the equipment shadow model, abstracting the gateway cluster into a tough topology directed graph based on the panoramic state data by adopting an edge computing task scheduling algorithm, and coordinating the gateway cluster; the intelligent load balancing unit is used for monitoring the gateway cluster calculation load and the network transmission load at the same time, and predicting the load imbalance rate according to the gateway cluster calculation load and the network transmission load; the efficient, flexible and intelligent cooperative management of the Internet of Things gateway cluster is realized.

The invention discloses a cluster computing resource allocation method. The method comprises the following steps: constructing a heterogeneous computing resource pool comprising an MT CPU, an FT CPU and an MTDSP cluster; an intelligent algorithm basic calculation library is deployed, hardware mapping of a deep learning algorithm is realized through an irregular matrix multiplication optimization module, and the irregular matrix multiplication optimization module comprises a microkernel automatic generation unit which dynamically generates an assembly instruction assembly line supporting three FMAC units according to matrix dimensions; the multi-core parallel strategy unit is used for providing M-dimensional and K-dimensional parallel calculation strategies and a dynamic scheduling mechanism; dynamically selecting a row block size parameter # imgabs0 # and a column block size parameter # imgabs1 # based on the matrix shape to carry out self-adaptive adjustment on the block size; physical resources are abstracted into containers or virtual machine instances through resource virtualization, and computing resources are dynamically allocated according to matrix dimension characteristics of the intelligent training module. According to the method, automatic resource deployment strategy generation and flexible computing resource scheduling are realized, and high-performance cluster computing resource allocation and deployment services are provided for users.

Presented herein are systems and methods for using machine learning (ML) models to identify species-biased antimicrobial peptides (SB-AMPs) that target select microbial population. A computing system may retrieve a training dataset comprising AMP sequences and non-AMP sequences. The AMP sequences may target the select microbial populations. The computing system may generate, for each of the AMP and non-AMP sequences, a respective plurality of properties. The computing system may provide as input to a ML model, the AMP sequences, the non-AMP sequences, and the respective plurality of properties for each of the AMP sequences and non-AMP sequences. The computing system may determine, based on providing the input to the ML model, a set of rules defining values for the respective plurality of properties to discriminate between the AMP sequences and the non-AMP sequences.

A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

The invention relates to the field of data analysis, and discloses a fusion platform construction method and system based on intelligent information processing and data analysis, and the method comprises the steps: building a server cluster of to-be-processed data; calculating the cluster load of the server cluster, identifying the security factor of the server cluster, calculating the security coefficient of the server cluster, configuring the streaming data processing framework of the server cluster, calculating the data processing index of the streaming data processing framework, and optimizing the streaming data processing framework. Transmitting to-be-processed data to a data buffer area by using a multi-modal data preprocessing component to obtain buffer area data, analyzing the streaming processing logic of the buffer area data, constructing a continuous processing algorithm set of the optimized streaming data processing framework, and obtaining a continuous processing algorithm set of the optimized streaming data processing framework; and performing continuous processing on the buffer data based on a continuous processing algorithm set to obtain a final data tuple. According to the invention, the timeliness and stability of information processing and data analysis can be improved.

The invention provides a digital quantum simulation method, which comprises the following steps of: performing symmetry analysis and subspace decomposition on a target Hamiltonian to obtain an effective Hamiltonian and a symmetry constraint condition; constructing a target unitary operator according to the effective Hamiltonian, and initializing a quantum circuit population of the target unitary operator; calculating entanglement characteristics of each quantum line in the quantum line population, performing structure variation on the quantum line population based on the entanglement characteristics, and performing line fragment crossing in combination with a symmetry constraint condition to obtain a candidate line set; performing line parameter optimization on the candidate line set based on the fidelity gradient to obtain an optimized line set, and extracting gate distribution structure features of the optimized line set; and non-uniform sampling is carried out on the optimized line set based on gate distribution structure characteristics to obtain a sampling line subset, an optimal quantum line is screened out from the sampling line subset based on the deviation between an experimental observation value and a theoretical expected value, and quantum simulation is carried out based on the optimal quantum line.

The invention provides a large model calculation method and device oriented to a low GPU resource card group calculation system. The method comprises the steps that parameters of a part of a large language model are unloaded into a CPU memory in the GPU resource card group calculation system; obtaining a reasoning request, wherein the reasoning request comprises a batch processing size and a sequence length; in the cue word processing stage, whether the sequence length is larger than two times of the dimension of a hidden layer of the large language model or not is judged, and if yes, the calculation intensity of all attention layers and all multi-layer perceptron of the large language model is calculated; judging whether the calculation intensity of all the attention layers is greater than that of the multi-layer perceptron or not, if yes, unloading all the attention layers in a GPU video memory and unloading all the multi-layer perceptron in a CPU memory, and if not, unloading all the multi-layer perceptron in the GPU video memory and unloading all the attention layers in the CPU memory; and using the unloaded GPU resource card group computing system to run the large language model for reasoning to obtain a reasoning result of the reasoning request.

The invention relates to a heterogeneous cluster scheduling method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a resource occupation condition of at least one first kernel meeting a response condition; sorting the first kernels based on the occupation conditions of the resources to obtain a kernel scheduling sequence; obtaining at least one to-be-completed task submitted by the client, and sorting the to-be-completed tasks based on resource requirements for executing the to-be-completed tasks to obtain a task execution sequence; based on the kernel scheduling sequence and the task execution sequence, determining a target first kernel corresponding to each to-be-completed task; and allocating each to-be-completed task to the respective corresponding target first kernel. By using the method disclosed by the invention, the utilization rate of cluster computing resources can be improved, and the probability of task congestion is reduced. The training and reasoning process of machine learning can be independent of a specific chip, the flexibility and transportability of a machine model are improved, and the load of a cluster is more balanced.

Embodiments of the present disclosure provide a data processing method and cluster, a computing device, a computer readable storage medium, and a computer program product. The data processing method is applied to an application program interface service unit of the data processing cluster. The method comprises: receiving a data processing request sent by a client for a target storage unit, wherein the data processing request carries data to be processed and a unit path of the target storage unit; analyzing the data to be processed, and when it is determined on the basis of an analysis result that the data attribute of the data to be processed is a target data attribute, encrypting the data to be processed to obtain ciphertext data and an encryption key; and sending the ciphertext data and the encryption key to the target storage unit on the basis of the unit path, thereby minimizing an attack surface of a malicious user to a node level, and reducing the risk of sensitive data leakage.

The embodiment of the invention provides a multi-node parallel acceleration method, device and equipment based on a high-performance computing platform and an RoCE network and a medium, and relates to the technical field of cluster computing, and the method comprises the following steps: constructing a communication stack based on the RoCE network, and realizing an interconnection transmission network between nodes from a sending end to a receiving end; a computing task and a communication transmission task of the high-performance computing platform are executed through a CPU core binding technology of the high-performance computing platform; based on the transparent large page function, processing a memory allocation request initiated by an application program of an operating system and a request of the application program of the operating system for accessing a memory through a virtual address; before a main computing task of the high-performance computing platform starts, pre-processing is executed through multiple threads, open type multi-processing threads are started, the main computing task is executed through the multiple open type multi-processing threads, and a next round of pre-processing task is started in parallel. According to the scheme, parallel acceleration of multiple nodes in cluster calculation is realized.

The invention provides a cluster computing resource isolation method and device, electronic equipment and a storage medium, and relates to the technical field of cluster computing resource management. And scheduling the containerized task to an adaptive physical graphics processor node based on the information, intercepting a resource access request during task operation, allocating an isolated video memory space and a computing context according to an allocated virtual graphics processor resource limit, and dynamically maintaining an isolated state of multiple tasks, so that the task access efficiency is improved. The problems of inter-task interference, limited hardware compatibility, insufficient resource allocation flexibility and low utilization rate caused by lack of a flexible virtualization scheme for decoupling from hardware and imperfect resource scheduling and isolation mechanisms in the prior art can be solved.

The invention discloses an efficient management system, method and device for a complex electronic system and a medium, and belongs to the technical field of complex electronic system management.The efficient management system comprises a cluster management node, a CPU cluster, an FPGA management node cluster, an FPGA computing node cluster and a RapidIO management node; the cluster management node directly manages the CPU cluster and the FPGA management node; the FPGA management node is used as an agent for the FPGA computing node; the cluster management node, the CPU cluster and the FPGA management node are mutually communicated through the Ethernet; the FPGA nodes are mutually communicated through a RapidIO (Input / Output) bus; and the FPGA management node and the FPGA node communicate with each other through a high-speed port. According to the method, the computing efficiency of the embedded cloud native application can be improved, and the convenience of development and deployment can be improved.

This disclosure provides a node anomaly detection method, apparatus, electronic device, and storage medium. The method is applied to a computing cluster comprising multiple computing nodes. The method includes: performing risk prediction on a target node to be predicted among the multiple computing nodes; determining the predicted anomaly probability of the target node for multiple anomaly types, including multiple types such as faults, slow nodes, and silent data corruption (SDC); identifying risk nodes from the target nodes based on the predicted anomaly probabilities; performing anomaly detection on the risk nodes based on the predicted anomaly probability of each risk node to obtain anomaly detection results for the risk nodes; and identifying anomaly nodes from the risk nodes based on the anomaly detection results. Embodiments of this disclosure can improve the overall performance of the computing cluster and the proportion of computing nodes in the cluster that are in an available state.

The invention discloses a novel cross-physical cluster visualization method, and relates to the field of high-performance computing, and the method comprises the steps: S1, enabling a local cluster management node to be in communication connection with a remote cluster management node through an independent network line; s2, initiating a visual job application at a local end; s3, the remote cluster management node allocates nodes after receiving the application, and a visualization function module executes visualization operation; s4, the protocol forwarding module converts a graphic access protocol used by the operation into a protocol suitable for browser access; and S5, integrating the job information by the local cluster, generating a link, opening the link, and accessing the visual job by using a browser. According to the novel cross-physical cluster visualization method provided by the invention, visual operation graphic display is realized through a graphic processing tool of a remote cluster computing node, a remote cluster management node converts a graphic access protocol into a browser applicable protocol, and a user can conveniently access through a browser through reverse proxy.