85 results about "Hybrid computation" patented technology

Placing an application on a private portion and a public portion of a hybrid computing environment for processing. An application may be received for placement and processing. A primary processing objective and a split preference of the application may be determined. The split preference indicates whether the application can be processed using one or both of the private portion and the public portion of the hybrid computing environment. The application may be placed on one or both of the private portion and the public portion of the hybrid computing environment for processing, based on the primary processing objective and based on the split preference.

The invention provides a distributed computing system based on hybrid computing resources, used for reasonable allocation of resources, to meet the requirement for diversity of computing tasks. The system comprises a computing engine layer and a resource scheduling layer. The computing engine layer is composed of a plurality of deep learning frameworks constructed on the same Spark computing engine, and access interfaces of all the deep learning frameworks are packaged in a unified mode for the computing engine layer. The resource scheduling layer comprises a plurality of heterogeneous computing resources, and the heterogeneous computing resources comprise at least one of a CPU, a GPU and an FPGA. In the resource scheduling layer, different task queues are divided according to the task types of the to-be-processed tasks, different logic clusters are divided according to the types of computing resources carried by different physical machines, and the tasks in the task queues are distributed to the corresponding logic clusters for execution according to the task types of the to-be-processed tasks.

The invention discloses a power consumption sensing scheduling system and a power consumption sensing scheduling method for parallel application for hybrid computation environments. The power consumption sensing scheduling system comprises a user layer, a scheduling layer and a resource layer. The user layer transmits user requests to the scheduling layer, the scheduling layer transmits execution tasks and data required by the execution tasks to the resource layer and comprises an analysis module, a task clustering module, a processing unit selection analysis module and a task distribution module, analysis results of the analysis module are transmitted to the task clustering module, clustering results of the task clustering module are transmitted to the processing unit selection analysis module, the processing unit selection analysis module comprises a time computation module and a power consumption computation module, selection analysis results of the processing unit selection analysis module are transmitted to the task distribution module, and the resource layer comprises a plurality of DVS (dynamic voltage scaling) processing units and a plurality of non-DVS processing units. The power consumption sensing scheduling system and the power consumption sensing scheduling method have the advantages that DVS and non-DVS hybrid characteristics of the system are taken into consideration on the premise that application execution time minimization is a scheduling task, and execution power consumption of the application is reduced to the greatest extent.

The invention discloses an optimized video stitching method, comprising: inputting predefined pattern images; proceeding with a transformation which combines planar and cylindrical transformation; proceeding with a merging calculation which combines linear difference merging and alpha blending calculation; and proceeding with a horizontal stitching processing by putting the processed images horizontally together into one seamless wide-angle image. The optimized video stitching method according to the invention further comprises a camera position calibration flow comprising: finding a planar matrix by using predefined pattern images; proceeding with a planar transformation of image; proceeding with an image registration by using a block matching method to find out the stitching points on the planar surface; and proceeding with a cylindrical transformation by transforming the stitching points from the planar surface to cylindrical surface.

The invention provides a network flow analysis method based on a GPU, Hadoop / Spark hybrid computing framework. The method mainly comprises the following steps: constructing a GPU computing analysis framework and a Hadoop / Spark computing analysis framework, selecting the GPU or Hadoop / Spark computing analysis framework to process real time or offline network flow, wherein the GPU computing analysis framework is deployed on a stand-alone node installed with the GPU, the Hadoop / Spark computing framework is a distributed processing system and deployed in a server cluster, preferentially adopting the GPU computing analysis framework to process the real-time or offline network flow when the size of the available memory of the GPU is greater than or equal to twice network flow data. Through the construction of the GPU computing analysis framework and the Hadoop / Spark computing analysis framework, the GPU or Hadoop / Spark computing analysis framework is selected for processing the real-time or offline network flow, thereby effectively responding the real-time or offline statistical analysis processing of the high-speed network flow; and an operator, a maintainer and a manager can conveniently backtrack the analysis data.

In accordance with exemplary implementations, application computation operations and communications between operations on a host processing platform may be adapted to conform to the memory capacity of a parallel accelerator. Computation operations may be split and scheduled such that the computation operations fit within the memory capacity of the accelerator. Further, the operations may be automatically adapted without any modification to the code of an application. In addition, data transfers between a host processing platform and the parallel accelerator may be minimized in accordance with exemplary aspects of the present principles to improve processing performance.

The method of predicting gas composition in a multistage separator includes solutions to the regression problem of gas composition prediction that are developed using an ensemble of hybrid computational intelligence (CI) models. Three separate homogeneous and one heterogeneous ensemble of hybrid computational intelligence (EHCI) models are developed using a parallel scheme. The homogeneous models have the same types of CI models used as base learners, and the heterogeneous model has of different types of CI models used as base learners. Various popular CI models, including multi-layer perceptron (MLP), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), are used as base learners of ensemble models.

The invention discloses an active power distribution network optimization scheduling method for large-scale electric vehicle access, and particularly relates to the technical field of power distribution network optimization scheduling, and the method specifically comprises the following steps: S1, a first layer is two-stage optimization scheduling implemented by DEMS; S2, a second layer is real-time scheduling executed by the regional EV-EMS; and S3, optimized scheduling models in each stage are established and solving methods are adopted. According to the invention, the scheduling strategy isoptimized through three stages; day-ahead scheduling and intra-day correction of controllable resources of the power distribution network and real-time control of EVs charging are cooperatively realized; the influence of uncertainty factors such as RES power generation and EVs charging behavior randomness is reduced; by adopting a 'centralized+distributed 'hybrid computing framework, the computing time is greatly reduced, the problem of dimensionality disasters is solved, in addition, the adverse effect of local communication failure on overall optimal scheduling is also reduced, and the requirement on the reliability of a communication system is reduced.

The invention discloses a system and a method for classifying network messages on basis of hybrid computation hardware. According to the system and the method, centralized dispatching is carried out on various different computation hardware resources by a central processor to construct a multistage classified processing assembly line, matching rules are reasonably divided according to the characteristics of different computation hardware and configured in each stage of the assembly line, classified processing for simple rules can be finished by a special hardware chip, and complex custom classifying rules can be cooperatively realized by the special hardware chip, a universal parallel processor and a universal central processor, thus enhancing the capacity of processing messages.

The invention relates to a high-precision hybrid calculation method and device and equipment for shock wave instability, and a storage medium. The method comprises the following steps: initializing flow field unit information, and setting a reconstruction mode and a data format at the current moment as a preset mode; reconstructing the flow field unit information according to the flow field information and the reconstruction mode at the current moment; according to a reconstruction result, calculating a flow field interface flux by adopting a data format at the current moment; obtaining a stability matrix according to the flux of the grid interface; analyzing the feature vector of the stability matrix through a matrix stability analysis method, and further obtaining the position where shock wave instability occurs; using a high-robustness numerical format for calculation at an unstable position, and using a low-dissipation numerical format for calculation at other positions. The shock wave is stably captured by applying targeted treatment to the shock wave instability position. According to the method, shock waves can be stably captured, and then accurate distribution of the surface heat flow of the hypersonic aircraft is obtained.

The invention discloses an unmanned aerial vehicle group task allocation method and system based on cloud and fog hybrid computing and a readable storage medium. The method comprises the following steps that unmanned aerial vehicle groups are grouped according to different usage scenarios and execution task levels; the entry and exit point is used by a specific type of unmanned aerial vehicle group, and a fog computing node is created; the cloud computing center selects a corresponding standby unmanned aerial vehicle set to send a task preparation instruction; after the instruction is received, the unmanned aerial vehicle set sends a response data packet to the fog computing node of the entry and exit point to which the unmanned aerial vehicle group belongs; the fog computing node performscomprehensive analysis, determines an unmanned aerial vehicle group executing the task, and allocates the task to each unmanned aerial vehicle to be flied in a targeted manner; and the cloud computing center and the unmanned aerial vehicle group keep a communication state all the time, and task redistribution is performed according to task execution conditions. According to the invention, by useof the cloud-fog hybrid computing technology, the task allocation intelligence and high efficiency of the unmanned aerial vehicle group are realized, and the time delay and the computing pressure of the cloud are effectively reduced.

A hybrid system-on-chip provides a plurality of memory and processor die mounted on a semiconductor carrier chip that contains a fully integrated power management system that switches DC power at speeds that match or approach processor core clock speeds, thereby allowing the efficient transfer of data between off-chip physical memory and processor die.

The invention discloses a method for solving a nuclide migration governing equation on basis of a hybrid computation architecture. The method comprises the following steps of reading parameters, wherein computation parameters, boundary conditions and computational grid files are read in; generating matrixes, wherein a rigidity sparse matrix, a time evolution sparse matrix and loading vectors are generated, corresponding sparse matrixes are generated, and a later rigidity sparse matrix and a later time evolution sparse matrix are generated; carrying out circulation computation for solution, wherein a finite difference method is used for carrying out the circulation computation, a Jacobi iteration method is adopted in a circulation process for solving a matrix equation of AX=B, and the obtained solution vector Z is the nuclide concentration field datum of the time point. According to the method, data matrixes in a higher grid body number scale can be stored, and solving speed is higher.

A hybrid computing system comprising a quantum computer and a digital computer employs a digital computer to use machine learning methods for post-processing samples drawn from the quantum computer. Post-processing samples can include simulating samples drawn from the quantum computer. Machine learning methods such as generative adversarial networks (GANs) and conditional GANs are applied. Samples drawn from the quantum computer can be a target distribution. A generator of a GAN generates samples based on a noise prior distribution and a discriminator of a GAN measures the distance between the target distribution and a generative distribution. A generator parameter and a discriminator parameter are respectively minimized and maximized.