19results about How to "Improve memory utilization" patented technology

This application provides a method, system, device, and medium for virtual terminal resource preloading and caching management based on the ISOBUS standard. This application innovatively constructs a multi-level caching architecture and combines a Markov chain-based resource prediction engine with adaptive memory management technology to achieve intelligent preloading and efficient caching of interface resources. Addressing the pain points of existing ISOBUS VT systems, such as high resource loading latency, low cache hit rate, and insufficient memory utilization, this invention provides a targeted technical solution. Experimental data verifies that this technical solution can reduce resource loading time by 50% to 70%, increase cache hit rate to over 92%, improve memory utilization by 40%, and reduce system crash rate by over 80%.

The application discloses a memory management method and electronic equipment, and relates to the technical field of computers, including any controller of a target cluster, based on the current memory management parameter of the controller, performing corresponding memory configuration on the controller, and running a preset test case on the controller to obtain the performance index of the controller under the current memory configuration, determining the fitness function value of the controller under the current memory configuration, and then determining the self historical optimal memory management parameter of the controller; if the current iteration number does not reach the iteration number threshold, updating the memory management parameter based on a target update speed, and continuing iteration based on the updated memory management parameter; if the current iteration number reaches the iteration number threshold, performing corresponding memory configuration on the controller based on the self historical optimal memory management parameter of the controller. The application realizes dynamic management of the memory of the controller, and achieves the technical effect of improving the memory utilization rate.

The invention discloses a strategy optimization method for cache garbage collection, relates to the technical field of computer cache management, and is used for solving the problem of low cache collection efficiency. According to the method, a cache storage area is abstracted into a plurality of cache partitions, a partition state vector composed of capacity, access and fragmentation degree operation indexes is constructed for each partition, and a strategy evaluation thread periodically reads the partition state vectors to calculate garbage recycling pressure and recycling priority. The upper limit of the number of scanning objects and the upper limit of time slices are distributed for the target cache partition by combining the idle time proportion of the processor, and a recycling execution thread conducts incremental recycling in a batch mode according to a strategy list and records the recycling progress; after recovery, index weight and scanning configuration are adjusted according to release capacity, fragment degree change, hit times and back-to-source request change, so that garbage recovery gives consideration to release space and overhead, the hit rate and the memory utilization rate are improved, and pause and invalid recovery caused by centralized recovery are reduced.

The invention discloses a GPU memory optimization method and system, and the method comprises the steps: obtaining the real-time change characteristics of a task load through obtaining the dynamic data flow information of the task load and an intermediate data generation rule; according to the real-time change characteristics of the task load, an optimization algorithm based on gradient descent is adopted to determine the distribution proportion and the reserved space of a memory pool, and adjusted memory pool configuration is obtained; extracting available memory block information from the adjusted memory pool configuration, and compressing the intermediate data with low access frequency by adopting a Huffman coding algorithm to obtain a compressed intermediate data storage unit; and if the access frequency of the compressed intermediate data storage unit is lower than a preset threshold value, temporarily storing the compressed intermediate data storage unit to a low-speed storage area, and reallocating memory resources according to the task priority to obtain an optimized memory allocation scheme. According to the method, the memory utilization rate is remarkably improved, the access delay is reduced, and efficient memory resource management is realized.

This application provides a license plate recognition method, apparatus, electronic device, and storage medium. The method includes: sliding a sliding window of preset length and width along the long side of a preset sliding area in a first image, acquiring multiple sliding window results generated during the sliding process; determining the cutting position of the image to be processed based on the multiple sliding window results and the cutting position; determining whether the license plate to be recognized is a double-layer license plate based on the cutting position; if the license plate to be recognized is a double-layer license plate, cutting and stitching the image to be processed according to the cutting position to obtain a second image; and inputting the second image into a license plate recognition model to obtain a first recognition result. In this technical solution, the electronic device only needs to store a single-layer license plate recognition model to simultaneously recognize both single-layer and double-layer license plates, improving memory utilization and reducing the computing power used.

Embodiments of the present disclosure disclose an adaptive memory conversion method and device for a distributed file system. A specific implementation of the method comprises: dividing a metadata set in a target directory to obtain a metadata subset group; setting a corresponding memory data structure form for each metadata subset; determining a target metadata subset; executing a corresponding access processing flow on the target metadata subset to obtain and output a final access result; obtaining access feature sets and system resource states of each metadata subset in the metadata subset group in real time to obtain a directory index set; determining memory data structure form conversion instructions corresponding to metadata subsets to be converted in the metadata subset group to obtain a conversion instruction set and a to-be-converted metadata subset group; and performing data reconstruction on the to-be-converted metadata subset group to obtain a converted metadata subset group. The implementation can improve memory utilization, reduce redundant memory occupation, and reduce unnecessary data access overhead.

This application provides a dynamic memory management method, apparatus, electronic device, and storage medium, relating to the field of computer technology. It generates dynamic memory resource demand information by acquiring at least two performance indicators of the system in real time, thereby determining the resource allocation strategy for a first type of memory management unit used for data compression and storage and a second type of memory management unit used for data exchange to non-volatile storage media. Based on this strategy, the operating parameters of both units are configured, and the system memory resources are scheduled and managed. Therefore, it solves the problems in existing technologies where static allocation strategies are used, a dynamic coordination mechanism between the two types of memory management units is not established, and resource allocation cannot be adjusted in real time according to system status. This achieves the technical effects of dynamically adapting memory resources, improving system response efficiency, increasing memory utilization, and optimizing overall system performance and stability.

The embodiment of the present application discloses a memory management method, device, limited device, terminal, storage medium and program, wherein the memory management method is applied to a resource limited device, comprising: dividing a memory region managed by a virtual machine into a first memory region, a buffer common area and a second memory region; wherein the first memory region and the second memory region are two end used memory regions of the memory region managed by the virtual machine; the buffer common area is an intermediate unused memory region of the memory region managed by the virtual machine; the first memory region and the second memory region allocate memory space from the buffer common area on demand; the first memory region is used to store a management virtual machine stack and an indirect access variable stack; and the second memory region is used to store application RAM data. The technical scheme of the embodiment of the present application can effectively improve the memory utilization of the operating system of the resource limited device.

The invention discloses a matrix multiplication optimization method and system oriented to an RISC-V architecture, and the method comprises the steps: detecting the hardware characteristics of a target RISC-V platform, and adaptively configuring optimization parameters according to a detection result; performing memory layout and data rearrangement on the input matrix B to realize memory optimization; a multi-level partitioning strategy is adopted to adapt to a memory hierarchical structure, and multi-level partitioning calculation is achieved; carrying out RVV vectorization core calculation by using RISC-V vector instruction extension to realize data-level parallel optimization; the instruction delay is hidden through loop expansion and a software assembly line, and a data prefetching mechanism is adopted to realize instruction-level parallel optimization; performing multi-core parallel computing by using an OpenMP multi-thread technology to realize thread-level parallel optimization; and calculating residual elements by using a vector register to realize tail processing optimization. According to the method, the cache hit rate and the calculation performance degree are improved, the hardware potential is fully excavated, and the method is suitable for edge devices with high requirements.

The application discloses a model inference acceleration method, system, device and medium of a vehicle-mounted edge device, the method comprising: by analyzing the inference task request and dynamically hot loading the model weight, mapping the pre-filling stage and the decoding stage to different CUDA streams for parallel processing, and introducing a forced preemption mechanism to ensure the real-time performance of high-priority tasks. Through paging memory management and KV Cache hierarchical replacement strategy, the memory resources are optimized, and combined with power consumption monitoring and bottom layer hardware instruction set optimization, the technical problems of low resource utilization, high-priority task delay and excessive power consumption of the existing vehicle-mounted edge device in multi-task concurrency are solved. The application can effectively improve the inference throughput and system stability of the model in the vehicle-mounted edge device, solve the technical problems of low resource utilization, high-priority task delay and excessive power consumption of the existing vehicle-mounted edge device in multi-task concurrency, and is suitable for complex computing scenarios of intelligent vehicles.

The invention discloses a memory allocation method and system, a medium and a product, which are applied to the technical field of storage, and the method comprises the following steps: monitoring a memory access condition of a running task to obtain memory space release information of the running task; determining a memory space physical address to be addressed based on the memory space release information; addressing the memory space physical address to be addressed to obtain a continuous addressed address; and allocating the memory space for the memory space application instruction based on the continuous addressing address and the unaddressed memory space physical address. In this way, the memory utilization rate can be increased, and therefore the real-time task processing performance is improved.

This invention discloses a GPU memory optimization method and system. By acquiring dynamic data flow information and intermediate data generation patterns of the task load, the real-time variation characteristics of the task load are obtained. Based on these characteristics, a gradient descent-based optimization algorithm is used to determine the memory pool allocation ratio and reserved space, resulting in an adjusted memory pool configuration. Available memory block information is extracted from the adjusted configuration, and a Huffman coding algorithm is used to compress low-access-frequency intermediate data, resulting in compressed intermediate data storage units. If the access frequency of the compressed intermediate data storage units is lower than a preset threshold, the compressed intermediate data storage units are temporarily stored in a low-speed storage area, and memory resources are reallocated according to task priority, resulting in an optimized memory allocation scheme. This invention significantly improves memory utilization, reduces access latency, and achieves efficient memory resource management.

The invention provides an end-side cloud collaborative big data analysis method and system, and the method comprises the steps: employing the hierarchical retrieval under end-side cloud collaboration, determining a retrieval starting level based on a deployment and control target type and retrieval task timeliness, and triggering continuous retrieval according to a preset collaborative strategy: carrying out the matching of an associated camera range according to the deployment and control target type and an incident site; in the terminal layer retrieval, each associated camera performs deployment and control target retrieval on historical feature data in a local short-term time window, and triggers edge layer retrieval when the historical feature data are not hit; according to edge layer retrieval, an edge box calls historical feature data in a middle-term time window uploaded by all associated cameras in an area, deploy and control target retrieval is carried out, and cloud layer retrieval is triggered when a target is not hit; and in the cloud layer retrieval, a cloud server screens feature data in a long-term window of a retrieval area from a cloud database according to a retrieval task and performs deployment and control target retrieval. The problems of high transmission pressure, low retrieval efficiency, resource waste and the like are solved.

The invention discloses a PLC equipment data optimization storage method, device and equipment and a medium, and relates to the technical field of data compression, quaternary Huffman coding adopted by the method optimizes the construction process of a coding tree through a virtual node processing mechanism, under the same data distribution condition, the average code length is lower than that of traditional binary Huffman coding, and the data transmission efficiency is improved. And the compression rate is obviously improved. For common sensor data, control parameters and the like in the PLC equipment, the storage space occupation can be greatly reduced by improving the compression efficiency. Meanwhile, the size of the window can be adjusted in real time according to the data change rate by adopting a dynamic sliding window mechanism, a larger window is used when the data distribution is stable, and a smaller window is used when the data change is frequent, so that the coding rule can reflect the change of the data distribution in time, and the coding adaptability and stability are improved.

The invention discloses a computing power resource dynamic scheduling system supporting trusted data exchange. The computing power resource dynamic scheduling system comprises an underlying hardware adaptation layer, a trusted data exchange layer, a core computing power optimization layer, a dynamic scheduling layer and a monitoring and feedback layer, wherein the bottom hardware adaptation layer comprises a chip migration adaptation module and is used for realizing instruction set mapping and capability portraying of heterogeneous hardware; the core computing power optimization layer comprises a low-delay reasoning system, an efficient N-dimensional parallel system and a heterogeneous memory management system; and the dynamic scheduling layer outputs a computing power distribution strategy based on the capability portrait and the task characteristics. According to the scheme, through deep fusion of hardware adaptation, credibility guarantee, computing power optimization and intelligent scheduling, a full-stack computing power scheduling system in a credible scene can be constructed, and the scheduling efficiency is improved.

This invention discloses a memory allocation method, system, medium, and product, applied in the field of storage technology. The method includes: monitoring the memory access status of running tasks to obtain memory space release information of the running tasks; determining the physical address of the memory space to be addressed based on the memory space release information; addressing the physical address of the memory space to be addressed to obtain consecutive addressable addresses; and allocating memory space for a memory space request instruction based on the consecutive addressable addresses and the unaddressed physical addresses of the memory space. This improves memory utilization, thereby enhancing the real-time processing performance of tasks.

This application provides a path planning method, system, electronic device, and computer program product based on BeiDou grid codes. The method includes: acquiring the four-dimensional spatiotemporal space of the target path planning task and performing hierarchical division to form a four-dimensional spatiotemporal grid; generating a four-dimensional code containing longitude, latitude, altitude, and time codes for each grid based on BeiDou grid codes; generating and storing a set of spatiotemporal neighborhood codes at the current level; searching based on this set, and generating cross-level index markers using historical planning data when no matching grid is found; determining the target level based on these markers, determining the linked list insertion position through one-hot encoding bit-by-bit comparison, and establishing a cross-level mapping; continuing the search at the target level to obtain the four-dimensional spatiotemporal grid sequence and outputting the corresponding path. This method achieves spatiotemporally coupled planning and dynamic hierarchical management, improving the efficiency, accuracy, and flexibility of path planning, and is suitable for scenarios such as UAVs that require consideration of spatiotemporal constraints.

This application provides a memory layout optimization method, an electronic device, a vehicle, and a storage medium. The method includes: during the operation of an electronic control unit (ECU), collecting access data of variables in the memory of the ECU; extracting data features from the access data of the variables; generating a memory allocation scheme for the variables based on the data features; and updating the variable layout of the memory based on the memory allocation scheme. This application can dynamically generate a memory allocation scheme for variables based on the variable access data collected during the operation of the ECU, and optimize the memory layout based on the memory allocation scheme, effectively improving the performance of the ECU.

The invention discloses an embedded model scheduling method and device based on video memory optimization and electronic equipment, through combination of fine-grained video memory virtualization and a dynamic compression technology, precise scheduling and efficient utilization of limited video memory resources of embedded equipment are realized, and the embedded model scheduling efficiency is improved. Meanwhile, environmental dependence is eliminated through self-adaptive matching of the container mirror image, so that the technical problems of serious video memory fragmentation and insufficient resource utilization rate caused by neglecting the physical constraint of the video memory of the embedded equipment in the prior art can be solved, and the purposes of remarkably improving the video memory utilization rate, reducing the resource fragmentation and improving the resource utilization rate are achieved. And the large-scale deployment stability of the real-time reasoning task in the edge calculation scene is ensured.