481 results about "High performance computation" patented technology

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

The invention relates to the technical field of computer architecture and parallel computing, and discloses a parallel task scheduling algorithm for a heterogeneous multi-core processor, which comprises the steps of task modeling, resource mapping, dynamic load balancing, communication optimization, task scheduling decision and execution monitoring. Task allocation is adjusted in real time through dynamic load balancing, cross-core communication delay is reduced in combination with communication optimization, and an efficient task allocation sequence is generated by using an improved genetic algorithm. According to the method, the resource utilization rate and the task execution efficiency of the heterogeneous multi-core processor in a high-performance computing scene can be improved, meanwhile, the robustness and adaptability of an algorithm are enhanced, and the task allocation problem in a complex computing scene is effectively solved.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a neural network large model efficient reasoning method based on multiple GPGPUs. The method aims to solve the problems of high communication overhead, non-uniform load, low resource utilization rate, high data transmission delay and the like among multiple processors. Dividing a calculation task into a plurality of sub-graphs through static analysis and mixed granularity partitioning of a model calculation graph; distributing the sub-graphs to the optimal GPGPU based on a weighted cost function in combination with heterogeneous resource perception and a dynamic mapping strategy; a global pipeline scheduling plan is constructed by using communication topology perception, and calculation and communication overlap are maximized; data are loaded in advance through a host side hierarchical caching and asynchronous prefetching mechanism, and transmission delay is hidden; multi-stream concurrent execution and event-based lightweight synchronization are adopted on each GPGPU, so that waiting overhead is reduced. According to the method, the reasoning delay can be remarkably reduced, the throughput and the hardware utilization rate are improved, and the method has good adaptivity and expandability.

The invention discloses a high-low voltage linkage line loss comprehensive intelligent diagnosis system and method. The system comprises a distributed storage and high-performance calculation module, a data fusion and processing module, a multi-dimensional feature construction module, a high-low voltage linkage analysis module and a line loss intelligent diagnosis module. The method is used for carrying out line loss comprehensive intelligent diagnosis based on the system, and comprises the following steps: processing multi-source data in real time through the distributed storage and high-performance calculation module; bus-line-user archive data are fused, and a line loss index is calculated; extracting time, space and electrical three-dimensional characteristic indexes; performing high-low voltage linkage analysis based on the Pearson's correlation coefficient and the DTW distance; and fusing the system state model and the isolated forest anomaly detection model to output an anomaly diagnosis result. The method is suitable for accurate analysis and treatment of line loss in an intelligent power grid environment, and the efficiency and quality of line loss management can be improved.

The invention belongs to the related technical field of high-performance computing, and provides a memory architecture-oriented dual-precision general matrix multiplication optimization method and system in order to solve the problems of limited computing power and access efficiency and the like in the prior art. Decomposing the matrix into a plurality of sub-matrix blocks according to the slave core array topology; the slave core receives the sub-matrix blocks issued by the master core, divides the sub-matrix blocks into small sub-matrix blocks based on a uniform blocking rule, loads the small sub-matrix blocks to an independent buffer area of a local data memory based on a DMA double-buffer protocol, divides the small sub-matrix blocks in the buffer area into SIMD vectors according to the SIMD unit characteristics of the slave core, and sends the SIMD vectors to the slave core; vectorization calculation and caching operation are alternately switched according to an iteration period through different independent buffer areas; and after all the slave cores finish calculation, the master core collects results written back to the master memory by the slave cores to obtain a final operation result, and double breakthrough of calculation power and memory access efficiency is realized.

The invention relates to the technical field of forest fire management, in particular to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed in a cloud computing server cluster and is used for uniformly storing personnel information, fire scene information and geographic information data and realizing multi-source data fusion and authority management through a standardized API (Application Program Interface); the central intelligent analysis system is deployed in a high-performance computer, integrates a forest fire danger auxiliary decision-making model, a forest fire spreading trend prediction model, a force distribution dynamic display module and a collaborative plotting module, and supports fire spreading prediction and real-time decision-making instruction issuing based on multi-dimensional data such as weather, terrain and vegetation; the mobile intelligent analysis system realizes fire spreading trend analysis, mobile plotting and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves forest fire emergency response efficiency and resource scheduling accuracy.

The invention relates to the technical field of high-performance computing resource management, and discloses a computing resource allocation method for a distributed supercomputing center. The method comprises the following steps: on the basis of obtaining real-time computing task and supercomputing center resource data and uniformly quantifying, integrally predicting resource requirements of future tasks; constructing a mixed integer linear programming model with the minimization of the total operation cost as a single target, wherein the total operation cost is the sum of the energy cost, the carbon emission cost, the data transmission cost and the SLA default penalty cost; solving the model by taking the time-varying electricity price, the green energy ratio, the resource capacity and the network parameters of each center as constraint conditions to generate an optimal resource allocation scheme; and then, by dynamically monitoring the resource state and the task progress, the model is triggered to resolve when the resource utilization rate is detected to be unbalanced or default risks, so that self-adaptive adjustment is realized. According to the invention, global collaborative resource allocation across super computing centers is realized, and operation economy, environmental sustainability and service reliability are considered.

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.

The invention discloses a spaceborne high-performance calculation module and system based on a SpaceVPX standard and a control method. The calculation module comprises a board card following the SpaceVPX standard, and a core processor module, a storage module, a communication interface module, a monitoring module, a power management module and a VPX connector which are integrated on the board card. The core processor module adopts an anti-radiation reinforced high-performance processor; the storage module comprises a DDR4, a NORFLASH and an SSD (Solid State Disk); the communication interface module comprises an Ethernet, an SRIO high-speed interface and a debugging interface; the monitoring module collects state information through an I2C bus; the power management module provides stable power supply; the VPX connector leads out an SRIO bus, an ETH bus and an I2C bus, and star interconnection with other units on a satellite is achieved. Through standardized and modular design, the problems that an existing satellite-borne computing architecture is fixed, poor in expansibility and insufficient in reliability are solved, and high-performance, high-reliability and flexibly-combined satellite-borne data processing capacity is achieved.

The invention relates to the technical field of high-performance computing and artificial intelligence crossing, and discloses a method for automatically generating a simulation computing task on a supercomputing platform, which comprises the following steps of: receiving a task demand of a user; performing semantic understanding and parameter extraction on the input by using a parameter dynamic analysis engine to generate a simulation parameter table; based on the simulation parameter table and the input format specification of the target simulation software, an executable parallel computing task script is automatically generated through a self-adaptive script generator; according to the real-time resource state and the task requirement of the supercomputing platform, a dynamic scheduling algorithm is adopted to distribute the task script to the optimal computing node; in the task execution process, the running state is monitored in real time, and when abnormity is detected, a fault-tolerant and self-repairing mechanism is triggered; by automatically generating the task script, the user parameter configuration time is saved, and the overall scientific research efficiency is improved by more than 30%; natural language input and zero code configuration are supported, and common scientific researchers can quickly master the method.

The embodiment of the invention provides a spectrum red shift measurement method, device and equipment and a medium, which can be applied to the fields of astronomical information technology and high-performance computing technology, and the method comprises the following steps: receiving N observation spectrum data to be measured; determining a processing mode of the observed spectrum data; in response to the processing mode being a first processing mode, processing the observation spectral data through a first calculation path, the first calculation path performing red shift measurement calculation based on a first numerical calculation library by matching the observation spectral data with a set of template spectral data to obtain a calculation result, the calculation result of the first numerical calculation library is consistent with the calculation result of a reference algorithm within a preset precision range; in response to the processing mode being a second processing mode, the observed spectral data is processed through a second computational path that reconstructs the redshift measurement calculations into a tensor parallel model based on a second library of numerical calculations and performs on parallel computing hardware, the second library of numerical calculations supporting tensor calculations.

The invention relates to the technical field of browser-side high-performance computing, in particular to a WebAssembly-based high-performance front-end audio and video processing method and system, and the method comprises the following steps: designing a modularized Wasm runtime container, constructing a zero-overhead memory interaction mechanism, and establishing a hardware adaptive scheduling engine; the method has the beneficial effects that dynamic loading of an audio and video algorithm is realized by designing a modularized Wasm runtime container, serialization overhead of communication between threads is eliminated by utilizing a shared memory mechanism, a hardware self-adaptive dynamic scheduling system is created, and finally three core objectives are achieved: execution efficiency close to native codes is provided in a pure browser environment; establishing a standardized processing pipeline without dependence of a third party; intelligent scheduling and elastic expansion and contraction of terminal heterogeneous computing resources are realized, and Web end infrastructure support is provided for professional audio and video applications.

The invention discloses a decentralized high-performance multicast method and system based on Gossip and RDMA, and relates to the field of computer network communication. The method has better reliability, realizes zero-loss transmission of data packets through message de-duplication, timeout retransmission, ACK / NACK convergence and a dynamic topology adaptation mechanism, ensures data consistency, and solves the problem of insufficient reliability of traditional UDP multicast; the method has the advantages that the real-time performance is high, the delay is low, the throughput is high, the zero copy and kernel bypass characteristics of the RDMA and the batch processing mechanism of the MPMC are combined, the average delay is lower than 10 microseconds, the throughput in a large data packet scene reaches 374GB / s, and the low-delay requirements of high-frequency transaction, high-performance calculation and the like are met; the method has high expansibility, is based on a decentralized architecture of an improved Gossip protocol, does not need core node maintenance topology, supports dynamic joining or quitting of nodes, can adapt to large-scale cluster expansion, and solves the expansibility bottleneck of centralized multicast.

A data processing architecture controls data processing arbitration in a high performance computing system that includes one or more premises. Individual premises can include one or more server computers executing an instance of a local file system and including one or more temporary data storage devices. Individual instances of the local file system can access files stored in objects of a primary data store. Individual objects of the primary data store can be accessed using a common identifier indicating a storage location of the individual objects in the primary data store.

The invention relates to the technical field of high-performance computing, and provides an inter-thread data sharing method, electronic equipment, a storage medium and a program product.The method comprises the steps that a plurality of threads in a thread block are divided into at least one cooperative thread group, and the threads in each cooperative thread group are from at least two different thread bundle groups; when a write operation of a first thread in any cooperative thread group is received, writing target data into a shared register resource associated with any cooperative thread group; and when a read operation of a second thread in any cooperative thread group for the target data is received, executing a synchronous operation, and reading the target data from the shared register resource after the execution is completed. According to the method, the cooperative thread group spanning different thread beam groups is constructed, and the shared register resources are utilized for data exchange, so that efficient data sharing is directly carried out among the threads spanning the thread beam groups through the register, and the method has the advantages of low delay, high bandwidth and capability of remarkably improving parallel computing performance.

Conventional high performance computer connections are electron-based systems, which require the memory packages to be as close as mechanically possible to the computation engine. Low power and high bandwidth communication, e.g. photonic, links can drastically change the architecture of high-performance computers by eliminating the bottlenecks in communication and augment existing memory systems to allow them to be both high capacity and high bandwidth simultaneously. A computer system comprises: a plurality of memory aggregation devices configured to retrieve data from and store data in a plurality of random access memory modules forming a unified contiguous memory address space disaggregated from a processing unit; a plurality of computational devices configured for simultaneously launching a plurality of data signals including memory read and / or write requests for the data to the plurality of memory aggregation devices; and a plurality of communication links coupling each of the plurality of memory aggregation devices to each of the plurality of computational devices for transferring the data therebetween.

The invention provides a storage and calculation integrated structure and a chip, and belongs to the technical field of integrated circuits, and the storage and calculation integrated structure comprises at least one near storage core grain which is used for providing large-capacity storage; the in-memory computing unit is used for providing computing processing, and the near-memory core grains provide cache space for data and / or instructions processed by the in-memory computing unit; the at least one logic processing core particle is used for loading the data and / or instructions stored in the near memory core particle into the in-memory computing unit, and updating and storing the data and / or instructions processed by the in-memory computing unit into the near memory core particle; and the logic processing core particle is integrated with the near memory core particle and the in-memory computing unit through hybrid bonding and / or TSV (Through Silicon Via). The storage and calculation integrated structure is applied to high-performance calculation, the high-performance calculation requirement and the large-capacity storage requirement can be met at the same time, the high-performance calculation of the system is maximized, and support is provided for the development of the calculation power technology from the hardware structure.

The invention discloses an awareness perception quantum gravitational nerve acceleration unified computing platform and method based on formalized verification, and belongs to the field of high-performance computing, medical AI and quantum information processing. A quantum gravitational coupling neural processing unit (QGCNPU); a consciousness information integral calculation core (phi ICC); formalizing a receipt chain verifier (FRCV); and the mirror reflection symmetry correction module (MRSCM) is used for eliminating the continuous projection mistake and the discrete Boolean mistake through topological equivalence verification of a local observer and a global GodCam visual angle. The system adopts an FPGA + ASIC + quantum processor three-layer heterogeneous architecture, and supports real-time pathological modeling (precision gt; gt) of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and ALS; 99.7%), drug target discovery (acceleration by 50 times) and consciousness state monitoring (time resolution lt; and meanwhile, the mathematical proving performance and the clinical auditing performance of the calculation process are ensured, and the requirements of the global 152 billion dollar neural science and technology market and the 89 billion dollar quantum calculation industry are met.

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 relates to an SOVD server execution method. The method comprises the following steps: receiving a request from an SOVD client; when a controller supporting traditional UDS diagnosis exists in a high-performance computer HPC deployed with the SOVD server, whether the request needs to be forwarded to the controller through other diagnosis protocols or not is judged according to the request; and when it is judged that the SOVD client needs to be forwarded to the controller, obtaining a diagnosis protocol of the controller from a diagnosis communication configuration file through a traditional diagnosis adapter CDA module, and communicating with the controller in a corresponding mode according to the diagnosis protocol so as to reply to the SOVD client. Wherein the communication parameters of the controller are pre-configured in the corresponding nodes of the diagnosis communication configuration file. The invention further relates to an SOVD server, a computer program product and a computer readable storage medium.

The invention relates to the technical field of electric power system intelligence, in particular to an electric power system large model parameter scale compression and high-performance computing resource optimization method, which comprises the steps of obtaining a pre-trained large model, generating a lightweight small model by model distillation, sparsely cutting an optimized structure, performing fine tuning training to improve performance and the like. Through the technical means of model distillation, sparse clipping, semi-supervised fine tuning and the like, the model parameter quantity is compressed to 10%-30% of the original scale, and the reasoning delay is reduced by 40% or above. Meanwhile, the lightweight model shows high applicability and accuracy in scenes such as electric power intelligent questioning and answering, report generation and new energy prediction, and technical support is provided for intelligent transformation of the electric power industry.

The invention relates to the technical field of electric signal data analysis, in particular to a high-performance computing equipment power state analysis method and system based on modularization. The method comprises the following steps: in a modular assembly of a high-performance computing equipment power supply, collecting contact resistance change data of an equipment power supply connector through a distributed sensor network, and calculating a resistance change rate; carrying out partial discharge monitoring on a cable of an equipment power supply, and recording discharge capacity trend data; taking the resistance change rate and the discharge capacity trend data as equipment electric energy characteristic parameters; according to the invention, the accuracy, comprehensiveness, practicability and reliability of the power supply system are remarkably improved by accurately monitoring electric energy parameters, early warning and positioning mechanical faults, evaluating electromagnetic thermal coupling interference, comprehensively analyzing the power supply state and outputting a report.

The invention discloses a construction method of a single-cell multi-omics data analysis system based on a containerization technology, and the construction method of the single-cell multi-omics data analysis system based on the containerization technology comprises the following steps: constructing a single-cell multi-omics analysis modular toolkit; a production mirror image is constructed on the high-performance cluster through a Single container engine, and deployment of a production environment is completed; a single-cell multi-omics automatic analysis platform is built through a snakemake process engine; and a JupyterLab interactive analysis platform for exploratory analysis of the single-cell multi-omics data is constructed. According to the single-cell multi-omics data analysis system construction method based on the containerization technology, the data acquisition link in the analysis process is accelerated, an automatic working process is formed to solve the problem of complex process, and the actual requirements of multiple users on a high-performance computing cluster for scientific exploration and analysis of single-cell data are met.

The invention discloses a method and device for supporting multi-radar high-resolution networking and a storage medium, and relates to the technical field of radar data processing. The method comprises the following steps: determining a space range of a networking field; according to the spatial range and the target resolution, defining a grid structure of a networking field data body; allocating computing resources for high performance computing; aiming at each radar, pre-calculating a mapping area of the detection range of the radar in the grid structure; loading the original polar coordinate observation data of each radar into the computing resource in parallel; solving a networking value of each grid point in the grid structure layer by layer based on the mapping region and original polar coordinate observation data, and generating a networking result; and post-processing and storing the networking result, and releasing the computing resources. According to the method, the solution is directly solved from the original polar coordinate observation data based on the mapping region, so that the generation and conversion of massive intermediate data are avoided, and the technical bottleneck of huge memory and video memory consumption is fundamentally solved.

The invention discloses an FPGA (Field Programmable Gate Array) coprocessor for accelerated execution of an intelligent contract, which belongs to the field of hardware acceleration and comprises a controller module, an operation module, a memory module and a stack module, the controller module is used for receiving a byte code instruction of an external smart contract and generating a control signal to control the operation module to carry out corresponding operation and / or the stack module to carry out corresponding operation; the memory module is used for receiving and storing to-be-operated data output by the smart contract and outputting corresponding operation data to the stack module, and then the stack module inputs the data to the operation module; the operation module comprises a calculator module, a multiplier module, a divider module and a hash operation module; and the stack module is used for storing the data obtained by the calculation of the operation module, and can also execute the operations of in-stack, out-stack, copying and exchange according to the control of the control signal, and outputting the data stored in the stack module to the memory module. The method is suitable for block chains and other high-performance computing applications.

The invention discloses a risk control platform based on event driving, a risk control method, equipment, a medium and a program product, and relates to the technical field of risk control management, and the platform comprises a message access and event generation module which is used for obtaining and generating a risk control event set and a meta factor set corresponding to a standardized risk control information set; the element factor analysis module is used for determining a summary factor set according to the risk control event set and the element factor set; the rule and index calculation module is used for determining a risk control index set corresponding to the summary factor set and a target rule set; and the result summarization and event driving module is used for performing calculation according to the risk control index set and the risk control rule through the standardized business interface to obtain a rule calculation result, and executing a risk control task through the rule calculation result to obtain a comprehensive risk control result. According to the invention, high-performance calculation of rules in the risk control platform can be realized, and real-time risk control requirements are met.

The invention discloses a water conservancy project river water quality real-time monitoring system based on multispectral remote sensing. The problems that traditional monitoring space coverage is limited, data feedback lags behind, precision is insufficient and early warning is low in efficiency can be solved. The system comprises a multispectral remote sensing data acquisition module, a data transmission module, a data processing and analysis module, an early warning and display module and a power management module. The acquisition module takes an unmanned aerial vehicle or a satellite as a carrying platform, and acquires river water quality characteristic spectrum and illumination data through a cuboid sensor with a multispectral detection unit and an illumination intensity sensor; the processing module accurately calculates parameters such as chlorophyll a concentration and the like by combining a high-performance computer with preprocessing software and an updatable regression type water quality inversion model; the early warning display module displays a result through a touch screen and realizes early warning through sound-light alarm and information pushing; all the modules are cooperatively used for monitoring according to preset intervals, and real-time, comprehensive and accurate monitoring of the river water quality is achieved.

The invention discloses a knowledge graph-based asset digital twin management platform data encryption method, which comprises the following steps of: determining a digital data storage position, constructing a data management knowledge graph, and performing dynamic attribute-based encryption on graph nodes according to a graph structure and an edge relation weight of the data management knowledge graph to obtain first encrypted data, and determining the data quality grade of the operation information in the platform database according to the operation access information and the statistical data, performing hierarchical encryption on the operation information in the platform database in combination with the data attribute to obtain second encrypted data, and performing dynamic searchable symmetric encryption on the second encrypted data according to the time sequence information to obtain third encrypted data. According to the method, encryption granularity can be refined from a'library level 'to a'node-edge-time sequence point level', online matching of security strength, data value and real-time requirements is realized, decryption overhead in a high-performance computing scene can be reduced, and the method has great significance in promotion of industrial digital transformation.

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 relates to the technical field of ion beam physical calculation, in particular to an ion beam optical property evaluation method based on a CPU-GPU hybrid parallel stable double-gradient conjugate algorithm. According to the technical scheme, the method comprises the following steps that a CPU-GPU hybrid parallel architecture is initialized, and CPU end and GPU end memory allocation, CUDA related object creation and GPU equipment initialization and parameter setting are completed; constructing a linear equation set, a sparse coefficient matrix A and a right-end vector b, and setting an initial solution vector, convergence precision epsilon and related parameters of the number of iterations; and analyzing the sparseness of the coefficient matrix A. Through combination of three core mechanisms of CPU-GPU cooperative calculation, dynamic load balancing and intelligent preprocessor selection, efficient, stable and universal evaluation of the ion beam optical characteristics is successfully realized, and an excellent solution is provided for solving the solving problem of a large-scale sparse linear equation set in the field of high-performance calculation.