338 results about "Hyperscale" patented technology

Provided in the present application is a data routing system. The system comprises a first topological network, a second topological network and a third topological network, wherein the network structure of the first topological network is a toroidal network topology structure consisting of a first node group, the first node group comprises a first number of network nodes, and the network nodes in the toroidal network topology structure have connection relationships with upstream nodes and downstream nodes in each dimension; the second topological network is a fat-tree network topology structure consisting of the network nodes in the first topological network and a switch; there is a cascade relationship between the network nodes in the first topological network and the switch; the first topological network is nested in the third topological network, and the network structure of the third topological network is a fully connected network topology structure consisting of a second node group; and the second node group comprises a second number of first node groups, and there is a connection relationship between the first node groups. Thus, large-scale or ultra-large-scale networking can be realized at a relatively low cost.

The invention discloses a VLSI circuit layout optimization method and system based on deep learning in the technical field of integrated circuits, and the method comprises the steps: abstracting a large-scale integrated circuit into a hypergraph structure, receiving the hypergraph structure, generating a node embedding matrix through a hypergraph neural network, and optimizing a segmentation loss function through unsupervised training, obtaining a probability distribution matrix, determining segmented subsets based on the probability distribution matrix, mapping each subset to a physical layout unit, generating a physical region layout of the chip according to an area equilibrium constraint and a time sequence constraint, performing constraint feedback on a physical layout result, and dynamically adjusting parameters of the segmentation loss function. The method can be used for circuit layout of a super-large-scale integrated circuit, and an optimization method and means are provided for providing a high-quality VLSI circuit layout scheme.

The invention relates to the technical field of computer software, and discloses a large-scale social network simulation method and system based on a dynamic topological structure. The method comprises the following steps: distributing a user portrait and a memory to a corresponding agent, and constructing a social network for the agent; distinguishing a core agent from a conventional agent according to the influence measurement; the core agent is driven by a large language model and interacts with other core agents through a natural language, so that the attitude of a conventional agent to an event is influenced; the conventional agent updates the attitude through the trust weight, and adjusts the social network by using a dynamic link prediction engine; updating the memory of the intelligent agent, pushing a library and a social network; and continuously performing a dynamic evolution process at each time step until the deduction of the event is completed. According to the method, detailed description of individual behaviors is ensured, the expandability of the whole system is also considered, and the decision-making efficiency and the simulation precision of the super-large-scale social network simulator are remarkably improved.

An end-side-cloud three-in-one active chat robot system for high-emotional merchants comprises an end side, a local edge server and a cloud end, and the end side is used for collecting multi-modal data of a user and performing local lightweight real-time processing and response execution; the local edge server is used for receiving and fusing the multi-modal features and the context information from the end side, and carrying out sentiment calculation, dialogue management and active trigger decision making with medium complexity; the cloud end is used for operating a super-large-scale model and providing global knowledge management, long-term user portrait storage and model training optimization; and the end side, the local edge server and the cloud end carry out cooperative communication through an encrypted channel to form a distributed intelligent processing architecture. According to the method, global optimization is realized by integrating end-side lightweight sensing, edge multi-modal fusion and cloud long-term memory. A composite finite state machine (FSM) active questioning mechanism is combined with sentiment calculation, and is different from traditional rule type triggering. Off-line and on-line fusion scheduling and multi-agent role playing are combined to be applied to a high-emotional-quotient interaction scene, and the simulation is improved. Multi-modal emotion perception circulation is introduced, and the problem of single text emotion misjudgment is solved.

Technologies for providing energy efficiency technology in extreme mMIMO systems in a cellular network cellular network (e.g., 5G wireless network, 6G wireless network) are described. The method collects data representing conditions for potential energy saving (ES) modes, comprising morphology data and traffic pattern data. The method determines, using the collected data, one or more energy saving (ES) modes for one or more components of a cellular network in at least one of a time (T) domain, a frequency (F) domain, or a space (S) domain, wherein the one or more ES modes cause at least one adjustment to the one or more components in the at least one of the time (T) domain, the frequency (F) domain, or the space (S) domain.

The invention discloses a robot hierarchical path planning method and device in a complex environment, a medium and a product, and the method comprises the steps: responding to a path planning request of a robot in an airport terminal, dividing the airport terminal into a plurality of dynamic Voronoi regions according to an underlying environment grid map and people flow density information, a minimum path cost algorithm with people flow density weighting is adopted to construct a global path anchor point sequence, the global path anchor point sequence is further split into a plurality of local path planning requests, a double-heuristic cost function is constructed, an improved algorithm is adopted to generate local sub-paths, and according to surrounding environment information collected by a robot in real time, the local sub-paths are calculated. And optimizing each local sub-path by adopting a reinforcement learning algorithm, generating motion control instructions for the robot at different control time points, and realizing dynamic obstacle avoidance of the robot. The path planning efficiency and the motion safety of the mobile robot in a super-large-scale dynamic environment are remarkably improved, and the technical problems that a traditional method is high in calculation complexity and poor in real-time performance are effectively solved.

A simulation method, apparatus and terminal for long-time-scale hierarchical operation of a power grid, and a medium. In the technical solution provided in the present application, on the basis of a hierarchical subordination relationship, simulation of a large-scale power grid is divided into three levels: a national level, a provincial level, and a prefectural level; by means of a hierarchical decoupling method, operation simulation of an ultra-large-scale and complex power grid is simplified into three stages: national-level power grid operation simulation, provincial-level power grid operation simulation, and prefectural-level power grid operation simulation; a lower-level simulation model only needs to perform operation simulation on the basis of key parameters output by an upper-level simulation model, and other than a limited number of key parameter interactions, the operation processes of simulation models at all levels are independent of each other; thus, the computational complexity of operation simulation of the power grid is simplified, and the association between upper-level and lower-level power grids is also taken into consideration, so that when facing massive scenario computation requirements, the operation time can be effectively reduced, and the operation efficiency can be improved.

Disclosed in the present application is a data routing system. The system is used for realizing data routing between computing nodes. The system comprises a first topological network and a second topological network, wherein the first topological network is nested within the second topological network. The network structure of the first topological network is an enhanced torus network topology structure formed by a first node group, wherein each network node in the enhanced torus network topology structure has a connection relationship separately with an upstream node, a downstream node and a non-adjacent node in each dimension. The network structure of the second topological network is a full mesh network topology structure formed by a second node group, wherein the second node group comprises a second number of first node groups, and there is a connection relationship between the second number of first node groups. In the system, by means of a hierarchical network structure in which an enhanced torus network topology is nested within a full mesh network topology, a large-scale or ultra-large-scale network is established, thereby effectively reducing the network cost and power consumption, and improving the network scalability.

The invention relates to the technical field of large language models, in particular to a high-concurrency large language model high-speed reasoning deployment method, which comprises the following steps of: extracting a weight coefficient corresponding to a user identity identifier, an emergency degree numerical value mapped by a request type and a resource occupation value converted by an estimated calculation amount according to the user identity, the request type and the estimated calculation amount; according to the method, the dynamic priority is generated by performing comprehensive operation on the user identity, the request type and the estimated calculation amount, so that differentiated services for reasoning tasks are realized, interactive requests with high urgency degree can bypass batch processing tasks with long time consumption, the response delay and delay jitter of key services are reduced, and the user experience is improved. Meanwhile, according to the complexity of the request text and the geographic coordinates of the user, the task is intelligently routed to the model node with the most suitable scale in the distributed network, so that the wide area network transmission delay is greatly reduced through edge processing, and the computing power waste caused by using a super-large-scale model to process the simple task is also avoided.

The invention relates to a power network structure weakness detection method based on multi-diagonal-block matrix decomposition, and belongs to the technical field of super-large-scale integrated circuits. According to the method, an original large-scale sparse matrix is converted into a band edge diagonal block structure and divided into a plurality of sub-matrixes capable of being solved independently by constructing a layering and blocking strategy for eliminating tree drive, and redundancy of full-matrix calculation is avoided. Meanwhile, a local approximate inverse algorithm of column norm truncation is designed, and target elements are calculated on the premise that a preset error threshold value is met. According to the method, the parallel computing architecture of the multi-core processor is fully utilized, the computing complexity is reduced by 2-3 orders of magnitude, the computing efficiency is effectively improved, the memory occupation is remarkably reduced, and a high-precision and high-efficiency solution is provided for detecting the weakness of the power network structure of the super-large-scale integrated circuit.

The invention provides an MCM-GPU-oriented resource storage optimization method, device and equipment, and the method comprises the steps: carrying out the static analysis of an operator-level data flow diagram of a calculation task in a compiling stage, and extracting the data dependence relation and life cycle characteristics of each operator; generating a storage optimization scheme based on the data dependency relationship and life cycle characteristics of each operator; in the operation stage, task and data collaborative allocation is carried out according to a data prefetching strategy and a data replacement strategy, task and data collaborative allocation and the calculation task are executed in parallel, and a data flow diagram and an operator dependency relationship of the task are analyzed by utilizing a compiling period; in combination with the data locality characteristics of the MCM-GPU multi-level storage architecture and the concurrency requirements of different modules for data access, an appropriate GPU module is selected to carry out data optimization management, so that frequent data exchange between a GPU and a CPU is reduced, and the performance potential of the MCM-GPU architecture in a super-large-scale task is brought into full play.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input through each of a plurality of layers of a neural network using a plurality of hardware accelerators to generate an output. The plurality of layers includes a fully connected layer having a plurality of parameters arranged in a row dimension and a column dimension. One of the methods includes generating a plurality of parameter blocks by partitioning a plurality of parameters along a row dimension and a column dimension; determining a ratio of the number of parameters along the row dimension to the number of parameters along the column dimension; and determining whether to use row fragmentation or column fragmentation in conjunction with the plurality of hardware accelerators to calculate an output for the fully connected layer, and then using the row fragmentation or the column fragmentation to calculate an output for the fully connected layer.

Systems and methods for making use of non-persistent storage as the journaling storage media for a virtual storage system are provided. According to one embodiment, in order to meet the needs of Extreme Low Latency Workloads while also seeking to provide predictable performance and the lowest possible latency, ephemeral storage of the virtual storage system is used to preserve state information (e.g., in the form of boot arguments and an operation log journal) across a host failure recovery scenario in which the virtual storage system is expected to be redeployed within a compute instance brought up by a cloud environment of a hyperscaler on the same host, thereby providing improved data durability (fewer host failure scenarios that result in lost data) as compared to the use of ephemeral memory of the compute instance and lower write latency than the use of persistent storage provided by the cloud environment.

The invention provides a data routing system. The system comprises a first topology network, a second topology network and a third topology network. The network structure of the first topology network is a surrounding network topology structure formed by a first node group, the first node group comprises a first number of network nodes, and the network nodes in the surrounding network topology structure are respectively connected with upstream nodes and downstream nodes in each dimension. And the second topology network is a fat tree network topology structure formed by network nodes and switches in the first topology network. And each network node and the switch in the first topology network have a cascade relationship. The first topology network is nested in the third topology network, and the network structure of the third topology network is a full-interconnection network topology structure formed by the second node groups. The second node groups comprise a second number of first node groups, and the first node groups are connected. Therefore, large-scale or super-large-scale networking can be realized with relatively low cost.

The invention discloses a near-field channel state information feedback method under a super-large scale array based on Transform, which comprises the following steps: in a downlink XL-MIMO system in a near-field area, a user side performs two-dimensional matrix processing on acquired CSI data and transmits the processed CSI data to a coding end; two-channel coding compression is carried out on the CSI matrix, a first channel uses an encoder to extract channel related characteristics, and a second channel enables an original CSI matrix to pass through a linear network so as to reserve original information; compressing the output of the two channels, and splicing to generate a low-dimensional code word to be fed back; the base station side decoding end splits the low-dimensional code word and decompresses the low-dimensional code word of the corresponding channel; decompressed dual-channel code words are respectively input to two ends of a decoder; and carrying out size change on the output of the decoder to obtain the estimated original CSI. According to the method, angle and distance information of a near-field channel is fully utilized, and efficient CSI compression and high-precision reconstruction are realized.

The invention relates to the technical field of information analysis and processing, in particular to a universal quality analysis coding system based on LLM. The efficiency bottleneck of traditional quality analysis is broken through, automation, intellectualization and high efficiency of quality data analysis are achieved, the quality analysis efficiency, the semantic understanding depth and the consistency of analysis results can be remarkably improved through the system, particularly, outstanding advantages are shown when super-large-scale data sets are processed, and the method is suitable for popularization and application. Researchers in more disciplinary fields can quickly master and apply the quality analysis method, and the method has higher adaptability and flexibility in cross-field application. Compared with a traditional quality analysis method, the method has the advantages that analysis and processing of super-large-scale research samples can be achieved, an efficient, scientific and innovative research method and technical support are provided for quality research, high academic value is achieved, and the automatic and intelligent progress of quality research is promoted.

The invention relates to a multi-mode multi-agent RAG-based power dispatching method and system, and the method comprises the steps: generating a dispatching plan through the cooperative work of a plurality of heterogeneous agents with different division of labor, and receiving query input containing a power dispatching task; the planner agent decomposes the power dispatching task into a plurality of sub-tasks which are mutually dependent and generates a corresponding execution plan; according to the execution plan, the searcher agent sequentially executes two stages of fine-grained multi-modal retrieval on each sub-task from the multi-modal power knowledge base, coarse-grained whole-page retrieval is carried out in the first stage, fine-grained area fine reading and modal expansion are carried out in the second stage, and evidence related to the current sub-task is obtained and added into an evidence pool; and according to all evidences in the evidence pool, the responder agent answers the initially input query to generate a scheduling plan. According to the method, the problems of timeliness and multi-scene adaptability in super-large-scale power dispatching are effectively solved, and an innovative path is provided for guaranteeing dynamic safety and stability of a power grid.

The invention discloses a special tariff business settlement method and device, equipment, a storage medium and a product, and relates to the technical field of information technology data support, and the special tariff business settlement method comprises the steps: when a settlement instruction is received, extracting corresponding special tariff information from a real-time data warehouse, the real-time data warehouse synchronizes the service data of the special settlement service database in real time, the service data is obtained by performing data extraction on the obtained ticket file, and the service data is fragmented and stored to the special settlement service database through a distributed storage model loading fragmentation rules from a data source; and settling the special tariff information to obtain a special tariff settlement report. According to the invention, the distributed storage model is adopted to store the service data in the special settlement service database in a fragmented manner, so that super-large-scale file data volume can be borne; the special tariff information is extracted from the real-time data warehouse, the special tariff settlement report is output through automatic settlement, and the settlement efficiency of the special tariff is improved.

The invention discloses a time sequence and congestion collaborative optimization-oriented GPU (Graphics Processing Unit) parallel acceleration global wiring method, which comprises the following steps of: according to a given netlist, dividing a super-large network by adopting a Kruskal algorithm in combination with a lookup set, and constructing a time sequence propagation path of the super-large network; performing network decomposition based on the timing margin estimation of the pins; calculating the time sequence weight of the two-pin network by adopting a time sequence weight calculation method based on an Elmore delay model; performing path cost calculation by adopting a cost function which comprehensively considers the time sequence and the congestion cost; executing two-level GPU parallel kernel acceleration mode wiring; optimizing the network topology by adopting a delay-aware pin connection improvement technology; the invention relates to a congestion-driven GPU (Graphics Processing Unit) accelerated routing and routing strategy for executing non-critical networks. According to the method, a high-quality wiring result with balanced time sequence congestion can be quickly obtained, the time sequence performance is effectively improved, and the requirement of a current super-large-scale high-performance circuit design wiring stage can be met.

The invention relates to the technical field of data storage, in particular to a data management method and system of a storage device. The data management method comprises the steps that data in a storage device are classified and divided into hot data and cold data, the hot data are data which are frequently accessed, and the cold data are data which are not accessed for a long time; and all data is compressed and deduplicated by applying a differential privacy technology so as to reduce the storage space and ensure that the data privacy is not damaged. According to the method, three-dimensional optimization of privacy protection intensity, storage resource utilization rate and access performance is realized in the field of storage systems, and a theoretically complete engineering solution is provided for operation of a super-large-scale data center.

The invention discloses a data routing system. The system is used for realizing data routing between computing nodes. The system comprises a first topology network and a second topology network, and the first topology network is nested in the second topology network. The network structure of the first topology network is an enhanced surrounding network topology structure formed by first node groups, and network nodes in the enhanced surrounding network topology structure are connected with upstream nodes, downstream nodes and non-adjacent nodes in all dimensions. And the network structure of the second topology network is a full interconnection network topology structure formed by the second node group. The second node group comprises a second number of first node groups, and the second number of first node groups are connected. The system establishes a large-scale or super-large-scale network by nesting a hierarchical network structure of an enhanced surround network topology and a full interconnection topology, effectively reduces the network cost and power consumption, and improves the network expandability.

The invention relates to the technical field of full-link monitoring, and discloses a Skwalking-based micro-service system full-link monitoring method, which comprises the following steps of: 1, automatically collecting full-link tracking data without invasion, loading an Agent probe through a bytecode enhancement technology when a micro-service instance is started, and loading the Agent probe through a bytecode enhancement technology; and the Agent probe automatically intercepts service calling under the condition of not modifying a service code. According to the technical scheme, a Kafka cluster is introduced between a data acquisition end and a processing platform to serve as a buffer channel, and an L1 and L2 distributed OAP processing architecture capable of being horizontally expanded is designed, so that the technical effect of guaranteeing the integrity and real-time performance of mass monitoring data in a super-large-scale micro-service environment is achieved; compared with a transmission architecture in which a monitoring probe is directly connected with a central server in the prior art, the transmission architecture solves the problem that the accuracy of a monitoring result is damaged due to the fact that data congestion, delay and even packet loss are easy to occur during data peak flood due to the lack of an effective buffering and peak clipping mechanism.

The invention relates to a low earth orbit satellite network measurement method, device and equipment, a medium and a product. The method comprises the steps that a topological graph of a low earth orbit satellite network is acquired, an Euler loop is determined according to the topological graph, and the topological graph comprises a plurality of satellite vertexes and edges between the adjacent satellite vertexes; determining a detection path set according to the Euler loop, and determining a report path set according to the detection path set; and collecting network state information of each satellite vertex based on the detection path set, and transmitting the network state information of each satellite vertex to a ground station for network measurement based on the report path set. By adopting the method, the whole-network state measurement of the super-large-scale low-orbit satellite network can be realized.

The embodiment of the invention provides an abnormal data block repairing method, a storage medium, an electronic device and a computer program product, and the method comprises the steps: recognizing abnormal data blocks on a plurality of storage devices, and obtaining the positioning information of the abnormal data blocks and the copy distribution information of the abnormal data blocks; according to the copy distribution information of the abnormal data block, obtaining copy data of the abnormal data block and importing the copy data into a shared storage; generating a mapping relation according to the positioning information of the abnormal data block; the mapping relation is the mapping relation between the abnormal data block and the storage device where the abnormal data block is located; according to the mapping relation, importing the copy data of the abnormal data block in the shared storage into the storage device where the abnormal data block is located, and repairing the abnormal data block. The problem that the abnormal data recovery speed is low in a super-large-scale cluster scene is solved, it is ensured that the abnormal data can be recovered at the fastest speed, and the overall recovery efficiency and the system stability are improved.

The invention discloses an enhanced high-emotional commerce interaction system based on end-side cloud collaboration. The enhanced high-emotional commerce interaction system comprises end-side equipment, an edge server and a cloud platform, the end side device is provided with an enhanced processing capability and comprises a lightweight finite-state machine decision module, a local model inference engine and an intelligent data preprocessing module. And the edge server is responsible for multi-modal information fusion, complex scene decision and distributed coordination management. The cloud platform is responsible for super-large scale model training, global knowledge management and system optimization. The system adopts a dynamic capability distribution strategy, and autonomously selects a processing path according to scene complexity. The end-side device can independently process a simple scene, and only requests the edge server for cooperative processing in a complex scene. By means of the design, system delay is remarkably reduced, privacy protection is enhanced, and system reliability is improved. According to the scheme, system delay is remarkably reduced, and millisecond response can be realized in a simple scene; privacy protection is enhanced, sensitive data are processed on the end side, and data transmission is reduced; the system reliability is improved, and basic services can still be provided in a weak network environment; personalized interaction is realized, and the end side continuously learns habits and preferences of the user; resource utilization is optimized, and edge and cloud computing loads are reduced.

The invention provides a low-cost fruit and vegetable detection system and a low-cost fruit and vegetable detection method based on a cloud-local collaborative architecture. The low-cost fruit and vegetable detection method comprises the steps of using the cloud-local collaborative architecture as a core, deploying or calling an existing super-large-scale pre-training model at a cloud end, pre-training by using mass multi-modal pre-training data of a large model, and performing strong feature extraction and reasoning capability. Through prompt word engineering, tasks such as advanced feature extraction and complex mode recognition of fruit and vegetable images are completed. The user can directly call the cloud model without self-training or fine tuning; a lightweight model is deployed on edge equipment, and optimization design is carried out for a specific fruit and vegetable quality detection scene. The local lightweight model completes specific tasks such as pest and disease damage detection and defect detection; a local lightweight model adopts a LoRA fine tuning technology, and a small number of parameters are finely tuned only for a specific fruit and vegetable quality detection task. Based on the technical scheme of the invention, stable performance is ensured, and the large model training and deployment cost is effectively reduced.

The invention relates to the technical field of wireless communication, and discloses a sparse channel estimation method and system for 5G-A5G-A super-large scale MIMO. The system comprises a quantitative sensing preprocessing module, a layered sparse reconstruction module, a high-precision guide correction module and a dynamic threshold decision module, and realizes efficient and accurate recovery of channel sparse characteristics under strong quantitative interference through nonlinear distortion compensation, multi-resolution sparse recovery, high-precision reference signal guide correction and parameter adaptive adjustment. The estimation performance is improved; and the calculation complexity is balanced.

The invention discloses a real-time quality detection method for Internet of Things big data of a new energy power grid. The method comprises the following steps: firstly, collecting equipment time sequence data in a new energy power grid by a lower-layer Internet of Things platform, and uniformly pushing the equipment time sequence data to a Kafka platform; a user needs to deliver quality inspection standard data to the Flink to be loaded in a broadcast stream mode; meanwhile, the system can realize automatic synchronization after change of measuring point grouping, detection rules and parameter thresholds by means of the characteristics of Flink broadcast streams according to metadata reloaded in a period agreed by a user; the method comprises the following steps: firstly, using an asynchronous IO to consume kafka, then indicating Flink to use asynchronous IO to consume kafka, using configuration in a quality inspection standard data broadcast stream, then carrying out data detection, and finally dropping the detected data into a distributed time sequence database cluster by virtue of Flink Sink. The problems that an existing offline scheme is poor in timeliness, insufficient in adaptability and difficult to cope with super-large-scale concurrency are solved.

The invention discloses a simulation analysis method and system for an advanced packaging structure containing a high-density interconnection structure, and the method comprises the steps: carrying out the independent modeling of the high-density interconnection structure during the building of a model, and remarkably reducing the complexity of the model; in the grid division process, uniform grid division is adopted for a high-density interconnection area, the local current density and electromagnetic field distribution simulation precision are ensured, meanwhile, the overall calculation amount is reduced, and the super-large-scale calculation problem caused by traditional full-structure fine modeling is solved. According to the method, high-efficiency and high-precision simulation of high-density interconnection packaging is achieved, a systematic solution is provided for signal integrity, thermal management and reliability of the advanced packaging technology, and the method effectively solves the problem that in the prior art, calculation is too large and cannot be analyzed due to direct and accurate modeling. And the technical problem of inaccurate result caused by a simplified model approximate solution method is solved.

The invention relates to the technical field of computers, and discloses a multi-FPGA collaborative deep neural network parallel acceleration method. According to the method, unified compiling and automatic calculation graph segmentation mapping based on a dynamic programming algorithm are carried out on a deep neural network model through a host control node, a heterogeneous strategy fusing data, the model and assembly line parallel is generated, and the problems of single FPGA computing power resource limitation and memory wall are effectively solved; direct communication between the FPGAs is achieved through a high-speed point-to-point internet, an asynchronous assembly line execution mechanism and a hardware-level data packaging technology are combined, communication delay is reduced, assembly line bubbles are eliminated, and a multi-FPGA cluster reaches a near-linear speed-up ratio; computing distribution is optimized in real time through a global load balancing unit, and the equipment utilization rate is increased; and meanwhile, a transparent deployment interface is provided through the hardware abstraction layer, so that the super-large-scale neural network can efficiently run on a multi-FPGA platform with low delay, and the overall calculation efficiency is improved.