679 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 discloses a low-code platform and Wasm high-performance computing integration system and method, and relates to the technical field of front-end development. In order to solve the problems that an existing low-code platform is limited in calculation performance and poor in security isolation performance, the scheme adopted by the invention comprises five modules: a description and registration module establishes a metadata structure for a Wasm module; the dynamic loading and asynchronous compiling module realizes loading as required and compiling during operation, and supports caching, version verification and hot replacement; the authority sandbox and resource isolation module constructs a security environment based on authority configuration to prevent illegal operation; the parameter binding and safety bridging module encapsulates a uniform interface to realize standardized interaction; the packaging and life cycle management module packages the module into a visual component, and supports full life cycle management. According to the method, high-performance, high-safety and visual integration of the Wasm module in a low-code platform is realized.

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 relates to the field of artificial intelligence, in particular to a deployment method and system for a large language model, and the method comprises the steps: (1) a service request end receives at least one piece of service request information, and stores the service request information in a service request message queue; (2) responding to a pre-filling stage calculation unit request and a load condition, and distributing service request information; (3) finishing the processing of the pre-filling stage to obtain at least one operation result; (4) the load balancer determines a task operation calculation unit in the stage according to the load condition of the calculation unit in the decoding stage; and (5) inputting the operation result of the pre-filling stage into the task operation calculation unit of the decoding stage, and outputting the result. The method has the advantages that the pre-filling stage and the decoding stage are deployed on a machine with high-performance computing power and a large memory respectively, load tasks are balanced, maximum hardware utilization is achieved, idle computing power is reduced, overall delay is reduced, throughput is improved, and expansibility and fault tolerance of the system are enhanced.

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.

Memory has been playing a major role in the performance, scalability and applicability of General Compute systems, and more recently, in realizing Generative Artificial Intelligence (GenAI) and High-Performance Computing (HPC) systems that scale to thousands of GPUs, CPUs and special-purpose Accelerators. Embodiments herein disclose efficient software-defined protocol terminations and protocol translations utilizing Compute Express Link (CXL), including translations between CXL.mem and CXL.cache protocols. Also disclosed are CXL-based systems, Resource Provisioning Units (RPUs), and Memory Fabric Switches enabling dynamic memory pooling and sharing, host-to-host communication utilizing CXL.mem, CXL.cache and CXL.io, intent-based protocol translations, and optionally seamless interactions between CXL, UALink, NVLink, and / or Ethernet protocols, utilizing a broad range of semantics including IO, Cache, and Memory, optimizing memory access and reducing latency. Some embodiments also enable scalability, flexibility and security in high-performance architectures suited for data centers and next-generation computing environments.

The invention relates to the technical field of automobile material temperature simulation analysis, in particular to an automobile cabin temperature field simulation analysis method and system based on multi-physics field coupling. According to the method, high-precision and high-efficiency simulation is realized through thermal-solid-fluid-electric coupling modeling in combination with the genetic algorithm, the neural network and reinforcement learning AI dynamic optimization, and the problems that the multi-physical field coupling effect cannot be comprehensively reflected and the parameter optimization efficiency is low in the prior art are solved. Time sequence analysis and dynamic boundary condition adjustment are introduced, transient working condition simulation is supported, and temperature field changes in actual driving can be better predicted. Through simultaneous solution of the Maxwell equation and the heat conduction equation, the temperature rise risk of the electronic component is accurately predicted, and the method is suitable for heat management optimization of key components such as a battery pack and a motor controller of a new energy automobile. Cloud high-performance calculation and edge end lightweight model cooperation are adopted, efficient simulation and real-time early warning are achieved, and rapid analysis and design optimization of extreme working conditions are supported.

The present disclosure herein addresses the problem of synthesizing a series of documents and extracting or summarizing meaningful information or content embedded as tacit knowledge in the series of documents. The embodiment of the present disclosure provides a system and method for tacit knowledge generation using large language model (LLM) in document synthesis. The method of the present disclosure performs intelligent document generation orchestrating a generative artificial intelligence solution workflow. In the present disclosure, tacit knowledge of subject matter experts in a knowledge base or in a series of documents is extracted. Further a content capturing the tacit knowledge is generated leveraging a large language models (LLMs) framework as the underlying architecture. The system of the present disclosure is artificial intelligence (AI) accelerated, cloud agnostic, latency defined, and security enabled.

The invention discloses a direct memory access (DMA) communication device for a computing network convergence computing architecture and a working method of the DMA communication device. The DMA communication device comprises a DMA transaction processing module and a protocol conversion module; the DMA transaction processing module is used for analyzing related DMA read-write requests, realizing processing of the DMA read-write requests and receiving of response data of the DMA read requests and completing processing of interrupt requests at the same time, and the protocol conversion module is used for completing conversion of read-write requests between a DMA interface and an AXI interface and mapping of response states. The invention aims to realize efficient protocol conversion between AXI and DMA interfaces, avoid the problem of DMA read request starvation caused by resource competition and ensure the consistency of DMA data access so as to improve the performance of an accelerator in high-performance calculation and artificial intelligence application, reduce transmission delay and improve data transmission bandwidth and energy efficiency.

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.

The invention relates to the technical field of battery testing, and provides a flow battery bipolar plate flow channel diversion performance testing device, which comprises an intelligent testing galvanic pile, which is composed of a plurality of detachable flow channel units, each flow channel unit comprises a bipolar plate and two flow channel frames, the flow channel frames adopt a modular design, and the flow channel units are connected with the intelligent testing galvanic pile; a plurality of micro pressure sensors, temperature sensors and flow sensors are embedded in the shell; the intelligent liquid path system comprises a plurality of independent liquid path circulating units, and each unit is composed of a liquid tank, an intelligent liquid pump, a liquid supply pipeline and a liquid return pipeline; a plurality of independent test chambers are arranged in the multifunctional shell, and a temperature and humidity control system and an intelligent drainage system are arranged in the shell; and the data acquisition and analysis system consists of a high-performance computer, a data acquisition card, a signal amplifier and analysis software. The problems that in the prior art, the local flow characteristic of the electrolyte on the surface of the carbon felt is difficult to accurately evaluate, and the requirement of optimizing flow channel design on refined data cannot be met are solved.

The invention discloses a high-performance computing memory optimization method and system based on cache classification, and the method comprises the following steps: S1, dividing a physical memory page into different categories according to the number of cache groups mapped to a target cache, and distributing the divided categories to a target process running in the target cache; and S2, when the target process requests the physical memory page from the operating system, allocating the physical memory page of the same category to the target process based on the category corresponding relationship between the process and the physical memory page. According to the method, the problem of cache conflict among a plurality of processes under the condition of high CPU utilization rate is solved, so that the performance of the whole system is improved.

The invention provides an automatic SEM image analysis and process defect detection system based on a full database, and relates to the technical field of semiconductor manufacturing, the system combines self-adaptive feature extraction and density anomaly detection of a high-performance calculation unit through dynamic feature fusion (Sp1) of a multi-scale SEM image and a multi-channel acquisition device, and realizes automatic analysis of the SEM image. Precise classification of defect types and automatic identification of new defects are achieved, the feature range and the fusion weight are dynamically adjusted, traditional static detection limitation is broken through, adaptive analysis of process context is supported, the detection precision is improved to 98%, the misjudgment and missing judgment rate is reduced by 30%-50%, especially in advanced processes such as 3nm, the new defects can be rapidly identified, rules can be updated, and the method is suitable for large-scale popularization and application. The process research and development efficiency and the product reliability are remarkably improved, and the flexible and intelligent detection capability is provided for semiconductor manufacturing.

The invention discloses a blasting parameter intelligent computing platform, which belongs to the field of blasting engineering, and comprises a distributed computing resource scheduling module, which adopts a distributed computing architecture to decompose a computing task into a plurality of sub-tasks, and distributes the sub-tasks to different computing nodes for parallel computing; through an intelligent scheduling algorithm, according to factors such as the load condition and the computing power of each computing node, computing tasks are dynamically allocated, dispersed computing resources in a network are fully utilized, the utilization rate of the computing resources is improved, and dependence on single high-performance computing equipment is reduced; data are processed in real time through the edge calculation preprocessing module, and the data transmission time is shortened; the lightweight calculation model library can quickly complete blasting parameter calculation; and the real-time feedback and dynamic optimization module realizes real-time adjustment and optimization of blasting parameters, so that accurate blasting parameters can be provided in time according to actual conditions in the tunnel construction process, and the construction progress is improved.

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 long distance communication, e.g. photonic or electronic, links can drastically change the architecture of high-performance computers by eliminating the bottlenecks in communication. 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; one or more 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 one or more computational devices for transferring the data therebetween.

A development system for predictive diagnostics is provided. During operation, the system can perform a first diagnostic test on a distributed computing system based on a first restriction level indicating resource consumption of a first set of hardware units. The distributed computing system can include a plurality of computing devices with processing and memory resources. The system can generate a first log comprising a first set of parameter values indicating an output of the first diagnostic test at the first restriction level of the distributed computing system. The system can configure a first diagnostic tool with the first set of parameter values to emulate the first diagnostic test. The system can then apply the first diagnostic tool to obtain a second set of parameter values indicating an output of the first diagnostic test at a second restriction level, which can be higher than the first restriction level.

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 discloses a low-light image enhancement method and system based on edge extraction and feature fusion, and aims to improve the image quality under a low-light condition, and the method comprises the steps: differential convolution edge extraction, feature fusion and image enhancement, effective extraction of image edge information through a differential convolution kernel, and combination of global and local features through a feature fusion module. The image enhancement module utilizes a deep learning network to improve image brightness and suppress noise, the method is suitable for embedded equipment with a high-performance computing environment and limited resources, the performance is excellent on a plurality of data sets through experimental verification, the image definition and details are remarkably improved, and the method is suitable for the fields of security monitoring, automatic driving and the like and has a wide application prospect.

The invention discloses a software and hardware collaborative RDMA network card SR-IOV parameterization configuration method, which can reduce redundant calculation steps through the collaboration of FPGA hardware acceleration and driving parameterization configuration, greatly improves the power consumption compared with a traditional pure software design scheme, and enables the VF performance of a network card to be close to the level of a physical network card. According to the method, the QP number, the MAC address and the RDMA enabling mark of the VF are dynamically adjusted through parameterization of the configuration file, and the operation and maintenance management efficiency can be effectively improved without manual item-by-item configuration; forwarding and isolation of CF traffic are achieved through the FPGA hardware layer, traffic sniffing between virtual machines is prevented, and safety is enhanced; the realized VF supports standard Ethernet communication and RDMA at the same time, the resource utilization rate is improved, and the requirements of hybrid scenes such as high performance computing (HPC) and cloud computing are met.

The invention provides a memory database starting method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: detecting a current project scale and a performance index of an original server, and determining whether to trigger a memory processing mechanism; if a memory processing mechanism is triggered, selecting a memory server according to high-performance computing and expansibility requirements, accessing an original server by utilizing a hot plug technology, configuring a memory database and initializing a memory computing cluster; configuring a to-be-synchronized database table by utilizing a metadata management function, loading original data to a memory database through a full-amount synchronization and incremental updating mechanism, and starting real-time data synchronization; starting a memory computing cluster, automatically detecting a memory processing state on an original server when a service task is triggered, and routing a large quantity of data tasks meeting conditions to a memory server to execute parallel computing. According to the method, the memory can be dynamically started for data processing, the operation efficiency is improved, and the user experience is improved.

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

The 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 invention discloses a reconfigurable heterogeneous radar data computing module and computing device, and relates to the technical field of computing devices, the reconfigurable heterogeneous radar data computing module comprises a ZYNQ processor, a digital signal processor (DSP) and a neural processing unit (NPU), the ZYNQ processor is connected with the DSP and the NPU; the ZYNQ processor comprises an ARM processor and an FPGA (Field Programmable Gate Array) logic resource; the FPGA logic resource is used for carrying out parallel class algorithm processing on the radar data; the DSP is used for carrying out serial algorithm processing on the radar data; the NPU is used for performing artificial intelligence algorithm processing on the radar data; the ARM processor is used for scheduling FPGA logic resources according to the calculation tasks, and the DSP and the NPU execute corresponding algorithms. According to the reconfigurable heterogeneous radar data calculation module, the high-performance calculation requirement can be met.

The invention relates to the field of high-performance computing cluster resource scheduling management, and discloses a Slurm scheduling specification integration method and system, and the method comprises the following steps: 1, analyzing a heterogeneous job description file, and extracting a resource demand parameter and a dependency relationship through a regular expression rule base; 2, based on the extracted original parameters, converting the original parameters into SLURM standard parameters through a preset mapping rule; step 3, according to the converted standard parameters; 4, calling a Slurm interface command to submit a script; and step 5, monitoring the execution state of the submitted job, and triggering a re-submission process for the abnormal job with the resource overrun or dependency missing. Through a multi-level analysis architecture and a regular expression rule base, job description files in different formats are effectively compatible, the problem that analysis of a non-standardized input format by a traditional method fails is solved, unified processing of cross-platform job definition is achieved, and the manual adaptation cost is remarkably reduced.

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.

Embodiments for communicating using a switch configured to establish multiple types of communication routes. First and second upstream switch ports (USPs) communicate with first and second hosts according to first and second Compute Express Link (CXL) protocols, respectively. A downstream switch port (DSP) communicates with a device according to a third CXL protocol. The switch couples the first USP to the DSP via a first route traversing a single Virtual CXL Switch (VCS), and couples the first USP to the second USP via a second route traversing two VCSs. Optionally, the switch includes a Resource Provisioning Unit (RPU) coupling the two VCSs of the second route, terminating the first and second CXL protocols, and translating between CXL messages conforming to the first and second CXL protocols.