106 results about "Workgroup" patented technology

Quantum-based dispatch of workgroups is described. An example of an apparatus includes a computer memory to store data for processing, including data for an application; and one or more processors including a graphical processing unit (GPU), the GPU including multiple chiplets, each of the multiple chiplets including compute containers and a cache, each compute container including a plurality of processing resources, and a dispatcher for dispatching workgroups to the processing resources of the GPU, wherein dispatching workgroups includes dispatching workgroups for the application according to a selected workgroup quantum, the selected workgroup quantum having a certain size and shape.

The invention provides a synchronization signal processing method, electronic equipment and a computer readable storage medium, and the method comprises the steps: executing a first synchronization instruction by a meta thread, generating an arrival and request response signal carrying a working group slot position identifier and a barrier object identifier according to first synchronization information, and sending the arrival and request response signal to a synchronization engine; the execution engine executes the meta-thread function instruction and generates an arrival signal according to the second synchronization information; aggregating the arrival signals with the same slot position identifier and barrier object identifier to obtain a final arrival signal; the synchronization engine determines a target barrier object and updates a count value, and constructs a response signal and sends the response signal to the target meta-thread when the count value meets a preset condition. According to the technical scheme, the synchronization signal processing overhead can be reduced, and the parallel execution efficiency is improved.

The invention provides a resource allocation method, a resource allocation device, electronic equipment and a medium, and the method comprises the steps: determining whether the number of available memory logic sub-blocks in a shared memory meets the demand of a work group task or not according to the demand number of shared memory resources; under the condition that the number of available memory logic sub-blocks in the shared memory meets the requirements of the working group tasks, the corresponding memory logic sub-blocks are allocated to the working group tasks, and for each thread bundle task in the working group tasks, the corresponding thread bundle task is allocated to the corresponding thread bundle task based on the required number of universal register resources of the corresponding thread bundle task. Determining whether the number of available register logic sub-blocks in one execution unit in the plurality of execution units meets the requirement of a corresponding thread bundle task; and under the condition that the number of the available register logic sub-blocks in one execution unit in the plurality of execution units meets the requirement of the corresponding thread beam task, allocating the corresponding register logic sub-blocks in the execution unit to the corresponding thread beam task. The problem of storage space fragmentation is solved.

A processor includes a plurality of processing elements. The processor is configured to execute a work graph including a plurality of nodes representing kernels executable by one or more processing elements of the plurality of processing elements. A first processing element of the one or more of the processing elements associated with a first node of the plurality of nodes is configured to assign each logical division of a sparse input matrix to a bin of a plurality of bins. Responsive to a dispatch condition associated with a bin of the plurality of bins, the processor is configured to dispatch a workgroup to at least a second processing element associated with at least a second node of the plurality of nodes corresponding to the bin. The workgroup includes a plurality of work items based on one or more logical divisions of the sparse input matrix assigned to the bin.

The invention provides a station-level production task generation method for a ship manufacturing workshop. The method is applied to the field of general control systems, and comprises the following steps: extracting process characteristic parameters in structured model data; obtaining real-time data of a workshop, and calculating a processing load value corresponding to each process characteristic parameter according to the real-time data of the workshop; setting a capacity adaptive threshold value, and matching a corresponding station type and a working group based on a comparison result of the processing load value and the capacity adaptive threshold value; according to the process adaptation range of the station type and the operation capability of the working group, dividing production batches and distributing station resources, and generating an initial station-level production task; and acquiring execution data of the initial station-level production task in real time, calculating a task execution deviation rate based on the execution data, dynamically adjusting a productivity adaptive threshold value or resource allocation of production batches according to the task execution deviation rate, and outputting the optimized station-level production task. Therefore, the production task generation efficiency is improved.

The invention provides a computing chip, a reduction operation method, a related device and a medium, and the computing chip comprises a plurality of computing units which are used for executing a computing task containing a thread bundle and a working group, and each computing unit comprises a loading storage unit and a local shared memory; the data caching unit is used for caching the work group level reduction results from the plurality of computing units and executing a reduction operation across work groups; the global memory is used for storing a grid-level reduction result after the reduction operation of the data cache unit; wherein a first-level hardware reduction module is integrated in the loading storage unit, and is used for executing a thread bundle and reduction operation in a working group, and submitting an obtained working group reduction result to a local shared memory; and a second-stage hardware reduction module is integrated in the data cache unit, is coupled with the first-stage hardware reduction module and is used for executing reduction operation across working groups. According to the method, the memory access delay and the calling overhead are reduced, and the execution efficiency of the grid reduction operation is improved.

A method for compressing work item coordinate data for work items in a work group and sending the data across an interface between a computation requesting unit and a computation sequencing unit. A work item valid mask is created in dependence on the number and positions of work items in the work group, the work item valid mask indicating valid work items in the work group. A first swizzle mask indicates which bits of a swizzle index for each work item in the work group correspond to the value of a first coordinate for that work item. A second swizzle mask indicates which bits of the swizzle index for each work item in the work group correspond to the value of a second coordinate for that work item.

The embodiment of the invention provides a scheduling method of a working group, a graphics processor and electronic equipment, the scheduling method of the working group is applied to an arbitration module of the graphics processor, the graphics processor further comprises at least two execution modules, and the scheduling method comprises the following steps: aiming at each execution module, executing the execution modules; determining a first score of the execution module based on the resource request of the working group and the residual resource information of the execution module; under the condition that the first score of each execution module is smaller than a first score threshold value, a target execution module is determined from the at least two execution modules based on the first waiting duration of each execution module, and the first waiting duration of the execution module is the duration needed by the prediction execution module to release the target resource information; the target resource information is determined based on the resource request of the working group and the residual resource information of the execution module; and scheduling the working group to the target execution module.

The application relates to an unmanned aerial vehicle (UAV) intelligent fault detection system and method for inspection, which comprises an UAV fault monitoring server, a communication network, ground fault detection terminals and random fault detection terminals. Each ground fault detection terminal forms a detection work group with at least one random fault detection terminal. The random fault detection terminals are connected with the ground fault detection terminals in the same detection work group through the communication network. The ground fault detection terminals are connected with the UAV fault monitoring server through the communication network. The detection method comprises three steps of system configuration, dynamic detection and ground detection. The application can effectively meet the needs of fault detection and troubleshooting of UAVs with different structures, and can effectively realize the cooperation of synchronous detection in the UAV operation process and ground static detection, so that the flexibility and convenience of UAV detection operation are greatly improved.

The present invention describes how a workgroup expert system can be established to mimic a real world task-expert and possess the equal four expert-Human-Intelligent (expert-HI) Problem-Solving (PS) competencies, including: 1) real-time concurrent workgroup-AI PS-processing, 2) real-time semantic workgroup-AI PS-transactions, 3) real-time task-domain workgroup-AI PS-collaborations and 4) real-time fine-grained adaptive workgroup-AI PS-services, based on multi-node workgroup architectures with derived workgroup-software methods and developed workgroup-system disciplines. Therefore, according to the Turing Test, the workgroup expert-task system should be deemed "Strong-AI-PS competent" for solving any task that is handled by one task expert with the help of functional processors, while all the current nodes-service-infrastructures with four node-AI-PS competencies can only mimic a group of real world functional processors with pre-developed logic-modelled processor-Human-Intelligent (processor-HI) PS-competencies for solving a pre-defined / specific multi-function-modelled task-oriented problem and should be deemed "Weak-AI-PS competent".

The invention discloses a scheduling method, a graphics processor, a scheduling device, a chip and equipment, and belongs to the field of chips. The scheduling method is executed by the GPU, the GPU at least comprises Y scheduling units, and each scheduling unit comprises a core or a processing unit. One core comprises at least one processing unit; the method comprises the following steps: recording an xth scheduling unit which is finally scheduled during nth scheduling; wherein the scheduling granularity of the nth scheduling is a working group or a working item, and one working group comprises a plurality of working items; in the (n + 1) th scheduling, scheduling is started from the (x + 1) th scheduling unit, so that polling scheduling in the Y scheduling units is realized; wherein the scheduling granularity of the (n + 1) th scheduling is a working group or a working item; n is a positive integer, and x is a positive integer smaller than or equal to Y. When multiple times of scheduling are executed, different scheduling units are cyclically executed for the first scheduling granularity, and therefore load balancing of the multiple scheduling units can be achieved for multiple times of scheduling.

This specification provides a Rootfs quota management system, method, computer device, and storage medium, relating to the field of computer technology. The Rootfs quota management system includes a master control component, worker components, and instruction execution components corresponding to the worker components. The worker components can connect to the container runtime of their respective worker nodes. The master control component is used to obtain the quota request of a target task, which corresponds to a target container, and the target container mounts a corresponding root file system (Rootfs). Based on the quota request, it queries quota management data, determines the quota instruction, and sends the quota instruction to the target worker component. The worker component, when successfully connected to the container runtime, receives the quota instruction sent by the master control component. If the target container corresponding to the container identifier carried by the quota instruction exists and is running, it calls the instruction execution component to execute the quota at the writable layer of the target container's Rootfs. This improves the flexibility and compatibility of quota management.

Policy-based genomic digital data sharing facilitates a variety of sharing scenarios, including public access, tenant-to-tenant sharing, workgroup sharing, and access by external service providers. Genomic digital data can be published to the platform and controlled by access tokens that are generated based on access policies. The policies can support conditions that are evaluated at execution time and effectively place control of access to information in hands of the owning tenant. Sharing conditions can be easily specified to support various use cases, relieving administrators from excessive access control configuration.

The processing system (100) includes a scheduling mechanism for generating data for fine-grained reordering of workgroups of kernels to generate data blocks, such as for communication across devices to enable overlap of AllReduce communication and producer computations over a network. This scheduling mechanism enables a first parallel processor (702) to schedule and execute a set of workgroups of producer operations to generate data for transmission to a second parallel processor (704) in a desired traffic pattern. Concurrently, the second parallel processor schedules and executes a different set of workgroups of producer operations to generate data for transmission in a desired traffic pattern to a third parallel processor (706) or back to the first parallel processor.

The invention discloses a data processing method and device for a matrix multiplication kernel function, a medium, equipment and a product, and the method comprises the steps: respectively configuring a corresponding ready state identifier for each output block in an output result of a main cycle stage; wherein each ready state identifier is triggered to be updated by the sub-working group executing the relevant calculation task of the corresponding output block; after the nth ready state identifier represents that the main loop calculation task of the nth output block is completed, post-processing operation of the tail sound stage is executed on the nth output block, so that execution time overlapping of the main loop stage and the tail sound stage is achieved; wherein 1 < = n < = N; n is the total number of the output blocks. According to the method, the idle waiting time of the general core can be effectively shortened, and the utilization rate and the calculation efficiency of hardware resources are remarkably improved.

The invention belongs to the technical field of cloud edge collaboration, and particularly relates to a computing resource optimal configuration method oriented to cloud edge collaboration. Comprising the steps that K0, the operation sequence of data matrix operation examples is optimized through memory addresses and demand quantity sorting of the data matrix operation examples; k1, configuring a data memory and a shared memory in the computing service unit; k2, establishing a parallel data buffer area in the shared memory for pre-reading; k3, optimizing and matching the scheduling function to ensure that the computing service unit can realize better data multiplexing; and K4, optimizing a calculation work group load distribution strategy to improve the utilization rate of calculation resources. According to the computing resource optimization configuration method, the computing efficiency of the cloud edge cooperation task can be improved, the resource consumption of data matrix processing is reduced, and the cloud edge cooperation processing capability is improved.

Embodiments herein dynamically calculate a scale / offset on a per tile (or per block) basis rather than on a per tensor or channel basis. This enables the scale to be determined in place in the compute unit (e.g., a workgroup)—e.g., without having to perform a second pass or retrieve data from main memory. The scale for the tile can be determined by the compute unit using different techniques. In one embodiment, the scale is determine from the data in the tile itself. In another embodiment, a historical scale could be used.

The application provides an intelligent safety supervision method and system for a construction site. The steps of the method include: obtaining a feature vector of a worker based on a preset monitoring device, comparing each feature vector in a feature vector set corresponding to each work group with the feature vector of the worker, determining the work group to which the worker belongs if the comparison is successful, and recording the work trajectory of the worker based on the preset monitoring device; if the comparison fails, determining the position of the worker at each time point based on the preset monitoring device, and constructing a to-be-judged space-time curve; comparing the to-be-judged space-time curve with each standard space-time curve to obtain a single comparison vector corresponding to each standard space-time curve; assembling each single comparison vector into a combined comparison vector, inputting the combined comparison vector into a pre-trained neural network model, and outputting the work group to which the worker belongs from the pre-trained neural network model.

The invention provides a computing unit, an instruction fetching request processing method, electronic equipment and a medium. The computing unit comprises an instruction cache unit; the first instruction processing unit is used for generating a first instruction fetching request when a first instruction fetching condition is met, and the first instruction fetching request points to a thread beam-level instruction needing to be acquired; the second instruction processing unit is used for executing work group level instructions, and each work group level instruction comprises a plurality of thread beam level instructions; the instruction fetching request processing unit is used for generating a second instruction fetching request when a second instruction fetching condition is met, the second instruction fetching request points to a working group-level instruction needing to be acquired, arbitrating the first instruction fetching request and the second instruction fetching request according to a preset strategy, selecting one instruction fetching request from the first instruction fetching request and the second instruction fetching request and sending the selected instruction fetching request to the instruction caching unit; and sending the corresponding instruction data to the first instruction processing unit or the second instruction processing unit according to the source of the instruction fetching request. According to the invention, the fetch requests of the work group-level and thread beam-level instructions can be processed in parallel.

A graphics processing unit (GPU) schedules recurrent matrix multiplication operations at different subsets of CUs of the GPU. The GPU includes a scheduler that receives sets of recurrent matrix multiplication operations, such as multiplication operations associated with a recurrent neural network (RNN). The multiple operations associated with, for example, an RNN layer are fused into a single kernel, which is scheduled by the scheduler such that one work group is assigned per compute unit, thus assigning different ones of the recurrent matrix multiplication operations to different subsets of the CUs of the GPU. In addition, via software synchronization of the different workgroups, the GPU pipelines the assigned matrix multiplication operations so that each subset of CUs provides corresponding multiplication results to a different subset, and so that each subset of CUs executes at least a portion of the multiplication operations concurrently.

The present disclosure provides a synchronization signal processing method, an electronic device and a computer readable storage medium, the method comprising: a meta-thread executing a first synchronization instruction, generating an arrival and request response signal carrying a workgroup slot position identifier and a barrier object identifier according to first synchronization information and sending the signal to a synchronization engine; an execution engine executing a meta-thread function instruction, generating an arrival signal according to second synchronization information; aggregating arrival signals with the same slot position identifier and barrier object identifier to obtain a final arrival signal; the synchronization engine determining a target barrier object and updating a count value according to the final arrival signal, and constructing a response signal and sending the signal to the target meta-thread when the count value meets a preset condition. Through the above technical solution, the synchronization signal processing overhead can be reduced, and the parallel execution efficiency can be improved.

The application relates to a video recording storage scheduling method, system and device, computer equipment and a computer readable storage medium. When a new video recording channel is accessed to a video recording storage device, based on the residual disk capacity and residual write capacity of each storage working group, it is determined whether the load balancing pressure of the video recording storage device is the video recording write capacity or the disk capacity. If the load balancing pressure is the video recording write capacity, based on the size of the residual write capacity of each storage working group and the code stream size of each newly accessed video recording channel, the code stream of each newly accessed video recording channel is evenly distributed to each storage working group. If the load balancing pressure is the disk capacity, based on the size of the residual disk capacity of each storage working group and the code stream size of each newly accessed video recording channel, the code stream of each newly accessed video recording channel is evenly distributed to each storage working group, thereby effectively improving the reliability of video recording storage.

The present disclosure provides a data processing method and device, electronic equipment and computer readable storage medium, the method comprising: sending reduction request information to a reduction processing unit by a computing unit, the reduction request information comprising reduction mark information and reduction data attribute information; determining a target workgroup from the computing unit according to the reduction mark information by the reduction processing unit, reading reduction data from the to-be-processed data of the data storage unit corresponding to the target workgroup according to the reduction data attribute information, and performing reduction processing on the read reduction data to obtain a reduction result. Through the above technical solution, the data can be read from different types of storage during the reduction operation, resulting in low efficiency of data reduction.

An electronic device for performing a data table-based syntax-wise job fit assessment, according to the present disclosure, comprises: a memory storing at least one process for performing a syntax-wise job fit assessment operation; and at least one processor for performing the job fit assessment operation according to the process. The at least one processor may be configured to: configure a standard dataset on the basis of competency elements for each job group or duty; extract clustering keywords from the standard dataset on the basis of a plurality of clusters configured for each job group or duty; divide and label each text in the standard dataset according to preset semantic roles; build a keyword pool using the clustering keywords and the labeled text; and assess job fitness for input data on the basis of a syntax-wise degree of matching with the keyword pool.

Cloud provider accounts can be integrated into a software-as-a-service platform. Configuration options can be provided to support various levels of granularity so that different cloud provider accounts can be provided to different tenants, workgroups, users, applications, and the like. From a user perspective, the fact that data is being stored at a cloud provider account can be transparent in that the same features and authentication process can be supported across different cloud provider types. In practice, limited temporary derived credentials can be generated from underlying credentials to provide fine-grained control of access to cloud provider account resources while avoiding administrative overhead.

The application relates to the technical field of blockchains, and discloses a blockchain system based on a sharding protocol and a working method thereof, which comprises a collaboration chain, a plurality of working groups connected with the collaboration chain, and a plurality of working chains connected with the collaboration chain and not reaching a storage upper limit; each working group is composed of a plurality of working chains based on the sharding protocol; and each working chain in the working group is a group-in-zone partition of the working group. The horizontal expansion of the performance and storage of a single blockchain is realized, the limitation that a single blockchain can only be expanded vertically is broken, and the service capacity of the working chain is improved.

Fast matrix multiplication in a multithreaded processing system having one or more processing units, each processing unit operates a plurality of threads grouped into a plurality of workgroups. At least a portion of a first matrix input is stored in a cache dedicated to a first processing unit as a first matrix subunit. At least a portion of a second matrix input is stored in a local memory dedicated to the first processing unit as a second matrix subunit. A plurality of output matrix subunits is generated by launching a plurality of workgroups. A subset of one or more second matrix subunits is assigned to a workgroup. Each of the first matrix subunits is multiplied with a corresponding second matrix subunit to obtain an output matrix subunit. During the generation of the plurality of output matrix subunits, the first matrix subunits are concurrently accessed by each launched workgroup from a cache dedicated to the first processing unit.