10 results about "Pipeline scheduling" patented technology

This disclosure provides interleaved pipeline scheduling methods, apparatus, devices, and storage media, particularly relating to the computer technology field, and more particularly to the artificial intelligence technology field, including deep learning, neural networks, and parallel computing. [Solution] A specific solution includes obtaining micro-batch scheduling parameters based on the pipeline splitting dimension, interleave dimension, and cumulative count; determining the transmission interval of a micro-batch requiring cache scheduling based on the micro-batch scheduling parameters; and performing cache scheduling on the computation results of the micro-batch requiring delayed transmission based on the transmission interval and cache units. According to this disclosure, a cache unit, for example, a cache queue, can be created, the computation results of some computation units can be cached in the cache unit, and the computation results can be transmitted with a delay. This allows for delayed transmission of the computation results of some micro-batches, improving the applicability of interleaved pipeline scheduling.

The application provides a container pipeline scheduling management method and system based on an ARM+DSP heterogeneous multi-core processor and a medium, and the method comprises the following steps: configuring scheduling related attributes of a container, wherein the scheduling related attributes of the container comprise a container duration time DT and a container interval time IT, and the container comprises an ARM container and a DSP container; according to the scheduling related attributes of the container and a pre-established container linked list, a next container to be run is selected according to a time window static scheduling strategy, and the container pipeline is controlled to run. Compared with the prior art, the application realizes independent running of various containers under the premise that the containers are sequentially arranged, do not affect each other and can interact with each other under the condition of sharing physical resources.

The application provides a data transmission module, a control method and an optoelectronic hybrid computing system. The data transmission module is applied to a runtime subsystem of the optoelectronic hybrid computing system, and the data transmission module is used for providing a data transmission management interface for the runtime subsystem. The data transmission module comprises a data transmission interface unit, a memory management unit, a direct memory access control unit and a pipeline scheduling unit. The data transmission interface unit is used for receiving a transmission call of the runtime subsystem. The memory management unit is used for managing allocation and release of on-board memory of a field programmable gate array. The direct memory access control unit is used for controlling access transmission between a graphics processor and the field programmable gate array. The pipeline scheduling unit is used for pipeline execution of data transmission and optoelectronic computing. In this way, the data transmission between the GPU and the FPGA can be managed by the data transmission module, and a complete transmission process of GPU-FPGA-GPU can be supported.

The invention provides a Simulink code generation method and device based on instruction pipeline perception, and relates to the technical field of embedded code generation, and the method comprises the steps: carrying out instruction dependency analysis and delay modeling on a target Simulink model, and generating a dependency graph for describing a dependency relationship between modules and an instruction delay model combined with target processor system structure parameters; based on the dependency graph and the instruction delay model, determining an optimal execution sequence of each module through assembly line perception scheduling, and performing assembly line scheduling based on the optimal execution sequence to obtain a scheduling result; and based on the scheduling result, utilizing a local pipeline optimization strategy to generate a target code. According to the method provided by the invention, the data dependence path between the modules is determined by utilizing topology analysis, and the execution sequence is adaptively selected through the minimum penalty priority algorithm to avoid unnecessary waiting and resource conflicts, so that the number of pauses of the instruction assembly line is effectively reduced on the premise of ensuring the semantic consistency of the model.

The invention discloses a natural gas pipeline network scheduling method, electronic equipment and a storage medium, relates to the technical field of natural gas pipeline scheduling, and aims to determine a better natural gas pipeline network scheduling scheme. The method comprises the following steps: obtaining a linearized mathematical model; solving the target function based on the flow constraint and the operation flow constraint of the compressor at the supercharging node to obtain an initial optimization result; according to the first search step size, based on the initial optimization result and a penalty sequence linear programming algorithm, solving the linearized mathematical model to obtain a first optimization result; according to a second search step length, based on the first optimization result and a penalty sequence linear programming algorithm, solving the linearized mathematical model to obtain a second optimization result; based on the third search step size, the second optimization result and a penalty sequence linear programming algorithm, solving the linearized mathematical model to obtain a third optimization result; and determining a natural gas pipeline network scheduling scheme based on the third optimization result.

The invention particularly relates to an Internet of Things floating point time sequence data compression transmission and decompression reading method based on a GPU. According to the scheme, an asynchronous pipeline scheduling mechanism driven by a lightweight asynchronous event is adopted in the data batch transmission and compressed data flow return process between a CPU and a GPU, so that the problem of high transmission delay caused by data transmission in an existing GPU compression scheme is solved; on the other hand, an accurate and rapid floating-point number-to-integer conversion method with theoretical guarantee is provided, and the problem of precision loss existing in a traditional rapid conversion method is solved; besides, a bit-plane adaptive coding scheme is also designed, so that an optimal storage strategy can be dynamically selected for different parts of the data, and the negative influence of an abnormal value on the compression ratio is effectively overcome. According to the scheme provided by the invention, on the premise of ensuring that the data is absolutely lossless, the compression and decompression speed obviously superior to those of the existing CPU and GPU schemes can be realized, and the data communication transmission compression ratio and throughput in the Internet of Things system can be obviously improved.

The application relates to a general-purpose GPU-oriented JPEG decoding batch processing and double-buffer pipeline scheduling method, belongs to the field of heterogeneous computing and image processing, and comprises the following steps: obtaining code stream data of a to-be-decoded JPEG image; performing entropy decoding and inverse quantization on the code stream data by a CPU to obtain a plurality of DCT coefficient blocks; if the size of the to-be-decoded JPEG image is not smaller than a preset size threshold, the DCT coefficient blocks are cached in a ring buffer located in a host memory; the state of the ring buffer is continuously monitored; when a preset batch triggering condition is met and a GPU is detected to be available, the DCT coefficient blocks in the ring buffer are batch copied to GPU display memory; based on the DCT coefficient blocks in the GPU display memory, integer IDCT transformation and color space conversion calculation are performed by the GPU to obtain an original image corresponding to the to-be-decoded JPEG image, and the original image is read back to the host memory; wherein the double-buffer pipeline is adopted to perform batch copying of the DCT coefficient blocks, integer IDCT transformation, color space conversion calculation and reading back of the original image. Efficient real-time decoding is realized.

Some embodiments provide systems and methods for gas pipeline pathway analysis, control and communication. In some embodiments, a physical pipeline scheduling and control system comprises a server or computer-based computing system configured to receive a user input corresponding to a request for a gaseous resource, automatically obtain data from a plurality of data sources, automatically simulate a plurality of potential pathways of the gas pipeline network, automatically output a pathway recommendation comprising at least a first recommended pathway, receive a user selection of a selected recommended pathway, automatically format a nomination for the selected recommended pathway, and automatically access and control a separate electronic submission system and electronically submit the nomination for the request for the gaseous resource to the electronic submission system for execution.

The application relates to the technical field of data governance, in particular to a distributed data governance system based on a new type of scheduling algorithm of a pipeline. The system comprises a basic framework unit, a pipeline scheduling unit, a unified service unit and a data management unit; the basic framework unit is used for managing a network framework supporting system operation; the pipeline scheduling unit is used for managing scheduling of an inter-process communication pipeline; the unified service unit is used for unified standardized management of data; and the data management unit is used for comprehensive governance of data. The application can realize distributed data governance, improve the fusion degree of the system and enhance the extensibility of the system; can improve the transmission effect of a data exchange process, improve the integrity and security of data; through centralized scheduling management of data, the framework is convenient for node expansion of the system, can support high-concurrency access of a large user, and can realize visual management of a data full life cycle, shorten a data management cycle and reduce cost waste.

Embodiments of the present disclosure provide a model training method, a model training device, an electronic device and a storage medium. The model training method comprises: in response to obtaining an insertion layer configuration corresponding to a first model, determining insertion layer information according to the insertion layer configuration, wherein the insertion layer configuration comprises parallel strategy information, and the insertion layer information comprises an insertion layer number, an insertion layer type and insertion layer position information; updating the first model according to the insertion layer information to obtain a second model; and performing staggered pipeline parallel training on the second model. The method can decouple from runtime performance measurement, avoid complex search and analysis, ensure the availability of staggered pipeline scheduling in the composition stage, improve system flexibility and compatibility, support high-performance training under different model structures and different parallel configurations, and improve training efficiency.