300 results about "Request queue" patented technology

The invention provides a data all-flash storage optimization method and system based on cloud computing, and the method comprises the steps: receiving a storage operation request stream of a user terminal through collecting a multi-dimensional performance data set of each node of an all-flash storage cluster in real time, wherein the multi-dimensional performance data set comprises a storage space fragmentation rate, an input / output request queue depth and a network link bandwidth utilization rate; analyzing a to-be-stored data block identifier set and an operation mode label, traversing a historical access record library based on to-be-stored data block identifiers, extracting historical access time sequence characteristics and physical storage position proximity of associated data blocks, and combining the multi-dimensional performance data and the historical access time sequence characteristics to obtain a data block identifier set; according to the method, a dynamic access popularity prediction model in a preset time period is constructed, and finally, a cross-node fragmentation storage topological graph and a cache preloading strategy matrix are generated according to the proximity of the dynamic access popularity prediction model and a physical storage position, so that the optimization of all-flash storage is realized, and the data storage performance in a cloud computing environment is improved.

In some implementations, a rollout component may deploy, in a pilot mode, an updated version of a software application in a runtime environment executing a legacy version of the software application. The rollout component may process requests in a request queue using the legacy version to generate processing results that are dispatched to a client interface and may process, using the updated version, copies of the requests processed by the legacy version. The rollout component may indicate whether the updated version passes one or more tests based on a comparison between the validation data generated by the updated version and processing results generated by the legacy version. The rollout component may switch the updated version from the pilot mode to a live mode based on the updated version passing the one or more tests.

The embodiment of the invention provides a read-write request processing method, a protocol conversion module, electronic equipment and a storage medium, and relates to the technical field of chips. The method comprises the steps that a protocol conversion module is arranged in a processor cluster, an original read-write request is converted into a first read-write request conforming to a CHI protocol through a first conversion module in the protocol conversion module, and a corresponding transmission channel is distributed to the first read-write request; and the request queue module orderly sends the received first read-write request, and the second conversion module recombines the first read-write request information to a second read-write request conforming to the CHI protocol, and allocates a corresponding channel for the second read-write request. According to the embodiment of the invention, the internal data channel (AXI or similar AXI) is directly converted into the CHI interface in the processor cluster, the internal circuit resources of the processor cluster are reused, the circuit area is saved, and the access path time delay is reduced.

The invention relates to the technical field of network resource scheduling, and provides a heterogeneous network intelligent slice resource scheduling method and device, electronic equipment and a medium. The method comprises the following steps: performing three-dimensional joint modeling on heterogeneous network physical resources to obtain a dynamic resource state tensor, and performing a dynamic resource allocation decision by combining a deep reinforcement learning model with a service demand matrix to obtain an initial slice resource allocation matrix; and performing mixed integer linear programming through a preemption decision model in combination with the URLLC service request queue to obtain a resource allocation sequence, and performing occupancy rate updating on the dynamic resource state tensor according to the resource allocation sequence to obtain ground network resource data, and carrying out resource fusion in combination with the satellite beam capacity data to obtain a space-ground integrated resource pool tensor, thereby carrying out traffic prediction in combination with historical traffic data through a long-short memory network to obtain a bandwidth pre-adjustment strategy. Through organic integration of technologies such as mixed integer linear programming and traffic prediction, the overall network resource utilization rate is improved.

The invention relates to the technical field of project management, in particular to a large-model batch reasoning and data flow optimization system oriented to an MOE architecture. According to the method, a collaborative architecture of the request access module, the environment sensing module, the expert routing engine, the resource scheduling module and the dynamic optimization control module is set, the text length and the subject type are extracted by using the request access module, a basis is provided for accurate routing, and the GPU video memory, the I / O bandwidth and the request queue depth are acquired in real time through the environment sensing module, so that the real-time routing is realized. The system load is comprehensively monitored, meanwhile, an expert sub-network is activated through an expert routing engine according to request features, invalid calculation is avoided, weight loading and resource allocation are managed through a resource scheduling module, the I / O bottleneck is reduced, and finally an optimization strategy is intelligently triggered through a dynamic optimization control module based on routing conflict factors. The problems of large reasoning delay fluctuation and unbalanced resource utilization rate mentioned in the background technology are solved, and stable low-delay response and resource collaborative optimization in a high-concurrency scene is realized.

The invention discloses a video stream concurrent access front-end dynamic scheduling control system and method based on a digital twinborn scene, and relates to the technical field of computer software and network communication, the system comprises three core modules: a self-adaptive request queue module manages UE rendering resources based on a session token pool, distributes tokens through preemptive scheduling, and sends the tokens to a server; in combination with a GPU frame rate feedback dynamic adjustment strategy, queuing through a Promise asynchronous queue when the request fails; the front-end fusing and backoff retry module monitors the connection failure rate, when the connection failure rate reaches a threshold value, fusing is triggered, retry is delayed by adopting an exponential backoff strategy, and a semi-open state tends to recover; and the local cache collaboration module intercepts the request through Service Worker, caches the latest video clip, pushes the local content when the request is delayed, and ensures the freshness through ETag verification. The method and the device are used for efficiently managing loading and interaction of multiple users on real-time rendering pictures at a browser end.

The invention relates to the technical field of graphics processors, and provides a dynamic graphics processor sharing method, which comprises the following steps of: receiving submission of a plurality of deep learning tasks; generating a dynamic priority queue containing high-priority tasks and low-priority tasks; transmitting a resource state to the low-priority task through a broadcast mechanism, and triggering resource adjustment according to a comparison result of the real-time thread block usage amount of the high-priority task and a preset threshold value; according to the difference value between the real-time thread block usage amount of the high-priority task and a preset threshold value, the number of executable thread blocks of the low-priority task is dynamically adjusted; checking whether the number of thread blocks required by the kernel is smaller than or equal to the number of executable thread blocks of the current low-priority task, if yes, starting execution, and if not, reserving the kernel in a kernel request queue to wait for next cycle scheduling; and periodically updating the dynamic priority queue until all deep learning tasks are completed. According to the technical scheme, the overall efficiency of the system can be improved, and resource waste is reduced.

The invention provides a chip verification method and device, equipment, a medium and a program product, and the method comprises the steps: receiving a first operation instruction of a to-be-verified module and a second operation instruction of a reference model in parallel, the reference model being a function model used for simulating the expected behavior of the to-be-verified module; searching a first instruction record matched with the first operation instruction from a request queue of the reference model, and searching a second instruction record matched with the second operation instruction from a request queue of the to-be-verified module; and determining a verification result of the to-be-verified module according to a comparison result of the first operation instruction and the first instruction record and / or a comparison result of the second operation instruction and the second instruction record. According to the invention, a structured and reusable universal chip verification method is realized, so that a tedious customized scoreboard design is replaced, and the efficiency and accuracy of verification work are greatly improved.

The invention discloses a cache resource request method and device, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps that a consumer determines a target cache resource needing to be requested; when the consumer determines that the priority request cache partition corresponding to the target cache resource is a private cache partition, comparing whether the first current available size of the private cache partition is greater than or equal to the size of the request cache resource; if yes, applying for a first target cache resource fragment of the target cache resource type from a first provider of the private cache partition; if not, judging whether a request record of the shared cache partition exists or not; if not, comparing whether a second current available space corresponding to the consumer in the shared cache partition is greater than or equal to the size of the requested cache resource; if yes, applying for a second target cache resource fragment of the target cache resource type from a second provider of the shared cache partition; and if not, generating a request record, and writing the shared cache resource request instruction into a shared cache resource request queue.

The subject application relates to separate command address (SCA) protocol-based memory controllers. For instance, a method may include receiving, by a separate command address (SCA)-based memory controller, a plurality of commands, scheduling, by the SCA-based memory controller, the plurality of commands using two or more data path scheduler (DPS) request queues, converting the scheduled plurality of commands into SCA-based commands, selecting, from the scheduled SCA-based commands, one or more scheduled SCA-based commands for a sequence execution, and executing in sequence the selected one or more SCA-based commands using one or more logical units (LUNs) of a channel in a memory component.

The invention provides a large-scale concurrent network request management method based on a computing power host management platform, belongs to the technical field of computer networks, and aims to reduce the request pressure on a rear-end host through technologies such as merging same or similar requests, allocating priorities for the requests, maintaining request queues and controlling the request sending frequency. The utilization efficiency of resources is improved, so that the system performance and the user experience are improved.

There is provided a computer-implemented method for handling updates to a computing environment, the method comprising: receiving, from a user, a change request comprising a text description justifying a change in hardware and / or software within the computing environment; applying natural language processing to the text description to extract one or more text features from the text description; performing a validation check of the received change request by applying a trained classification model to the extracted one or more text features, the classification model having been trained on a dataset of historical change requests; and if the received change request fails the validation check, preventing submission of the change request into a queue of change requests and / or implementation of the change associated with the change request.

The invention discloses a large-model adaptive batch reasoning system based on time delay measurement, and the system comprises a request receiving and queue management module which is used for monitoring and receiving an LLM reasoning request sent by an external user or an application; the real-time delay sensing and batch monitoring module is used for measuring and analyzing performance indexes directly related to the current batch processing strategy in real time in the LLM reasoning process; the Token-by-Token batch processing and reasoning execution module is used for selecting a corresponding number of requests from the request queue according to the size of the current batch determined by the scheduler, organizing the requests into an effective calculation batch and submitting the effective calculation batch to the LLM reasoning core at the bottom layer to execute one or more decoding steps; and the double-stage batch adjustment decision module is used for obtaining the normalized time delay ratio. The invention also discloses a large-model adaptive batch reasoning method based on time delay measurement. According to the invention, substantive improvement and intelligent management of the overall performance of the LLM reasoning service are realized.

The invention relates to the technical field of data processing, and provides a deep learning inference engine tensor optimization method and system for GPU acceleration. Performing asymmetric quantization on the first weight tensor of the original model to generate a second weight tensor, and updating the original model based on the second weight tensor to obtain an updated model; blocking the data tensor according to a preset dimension to generate input sub-blocks, and loading the input sub-blocks and a convolution kernel in the update model to a shared memory; calling the input sub-blocks from the shared memory, and processing the input sub-blocks of different batches in parallel based on a single kernel function formed by convolution kernels to generate a processing result; and based on the GPU utilization rate and the request queue length corresponding to the to-be-processed data, performing dynamic output adjustment to output a processing result. The performance and efficiency of a deep learning inference engine are improved, memory occupation and calculation delay are reduced, and powerful support is provided for deep learning application of GPU acceleration.

The invention provides a model reasoning scheduling method and system, an electronic device and a storage medium, the system comprises a plurality of pre-filling server nodes and a plurality of decoding server nodes, and the method comprises the following steps: responding to received user request information, and combining a pre-filling scheduling model based on deep reinforcement learning, obtaining a target pre-filling server node selected from the plurality of pre-filling server nodes and a first priority; sending a pre-filling request to a request queue corresponding to the first priority in the target pre-filling server node, and enabling the target pre-filling server node to process the pre-filling request; after the pre-filling request is processed, obtaining a target decoding server node selected from a plurality of decoding server nodes and a second priority by combining a decoding scheduling model based on deep reinforcement learning; sending a decoding request to a request queue corresponding to the second priority in the target decoding server node, and enabling the target decoding server node to process the decoding request; and reasoning efficiency is improved.

Systems and methods disclosed herein for improving data retrieval for dashboards using data caching. The system may implement a cache memory to store results for executed queries and / or anticipated queries, allowing for data to be retrieved asynchronously, and may implement a request queue to distribute the queries among processing nodes.

The invention provides an electronic book borrowing management system based on OPAC bidirectional hitching, and relates to the technical field of library informatization. Comprising the steps that a hardware acceleration layer constructs a dynamic resource distribution matrix through an FPGA coprocessor and calculates a matrix norm in real time, and request queue scanning and a redundant copy generation algorithm per 100 ms are achieved in combination with an ASIC anti-conflict controller; a system service layer deploys a dynamic priority scheduling engine, realizes intelligent resource distribution based on a cross-campus coordination coefficient and an edge cloud coordination strategy, records borrowing operation through a block chain evidence storage service and executes automatic copyright division auditing; the user interaction layer provides a bidirectional association retrieval interface and a priority borrowing channel based on credit scores. According to the system, the resource scheduling efficiency in a high-concurrency scene is improved through matrix norm calculation accelerated by hardware, an elastic copy allocation mechanism and an anti-conflict algorithm solidified in ASIC, and cross-campus resource collaboration and accurate copyright revenue allocation are realized in combination with geographic position scoring and block chain technologies.

A method includes: receiving an initial request message in a request queue of the messaging system; receiving an aggregation reply message in a reply queue of the messaging system, wherein the aggregation reply message is received from an integration system that processes the initial request message, and wherein the aggregation reply message includes an aggregation identifier associated with the initial request message; in response to receiving the aggregation reply message, starting a timeout process; monitoring the timeout process; holding one or more messages that are in the reply queue and that include the aggregation identifier until the timeout process expires or an expected number of responses has been received; and making available the one or more messages that are in the reply queue and that include the aggregation identifier based on the timeout process expiring or the expected number of responses having been received.

The invention discloses an archive management robot control system and method, and belongs to the technical field of robot operation and control, and the method comprises the steps: obtaining RFID scanning data and visual grayscale image data of a surface area of an archive rack, and generating a gap position data set; updating a preset priority dynamic matrix according to the gap position data set and a preset file borrowing request queue; based on the priority dynamic matrix and a preset multi-layer potential field model, robot advancing track parameters are adjusted, and a composite path instruction set is generated; and outputting the composite path instruction set to a robot driving unit to execute path tracking. According to the method, multi-modal sensing data fusion, dynamic priority matrix updating and multi-layer potential field model path planning technologies are adopted, high-precision space sensing, self-adaptive task scheduling and intelligent path planning can be achieved, and the operation efficiency and system reliability of the archive management robot are remarkably improved.

The present application relates to a data replay method, a data processing unit, a network interface card, a device, and a storage medium. The method comprises: when a storage stack is restarted, acquiring a replay-pending request queue from a persistent memory, the replay-pending request queue being a queue obtained by mapping a transmission buffer area into the persistent memory, and the transmission buffer area being used for receiving a request from a host side; on the basis of the replay-pending request queue, acquiring a replay-pending request from the persistent memory; and resending the replay-pending request to a target acceleration component directed to a storage device, so as to instruct the storage device to store target data which resides in the persistent memory and corresponds to the replay-pending request. In the method, a request queue, a request, and data corresponding to the request are stored in the persistent memory in advance, and if the storage stack is restarted, the replay-pending request is recovered from the persistent memory, eliminating the need for DMA and encryption / decryption techniques, and shortening an interruption duration of a storage data path, thereby improving data storage efficiency on the host side.

The invention provides a method and a device for releasing prefetch requests. The method comprises the following steps: acquiring an earliest prefetch request from a prefetch request queue as a prefetch request to be processed; if it is judged that the to-be-processed prefetch request is hit in a virtual-real address translation cache and hit in a target cache, or the to-be-processed prefetch request is hit in the virtual-real address translation cache and hit in an address missing state tracking register; and if so, releasing the to-be-processed prefetch request and the related prefetch request from the prefetch request queue. The device is used for executing the method. According to the prefetching request releasing method and device provided by the embodiment of the invention, the extra power consumption of data prefetching is reduced.

Provided in the embodiments of the present disclosure are a file processing method, a file system in user space, a device, and a computer program product. The file processing method is applied to a file system in user space, wherein the user state file system comprises a file system process and a file system in user space (fuse) process which is in communication connection with the file system process. The file processing method comprises: a fuse process acquiring a file processing request; the fuse process caching the file processing request, wherein the fuse process comprises a caching request queue used for caching the file processing request; and when a file system process exits, the fuse process determining a request processing state of the file processing request to be a suspension state, such that a file processing link where the fuse process is located maintains a normal state. In the embodiment, when a file system process exits, a fuse process may cache a file processing request, thereby ensuring the normal operation of a user process.

The invention discloses a device and a method for realizing a register block shared by a general-purpose computing core and a tensor core, a tensor core read request unit sends a read request and stores the read request in a tensor core read request queue, and a life cycle counter and a clock cycle counter are triggered to start counting; the life cycle counter records life cycle values of read requests in the tensor core read request queue; the tensor core read request scheduler transmits a to-be-executed read request to the register block when the life cycle value of the to-be-executed read request reaches a preset value, so as to read a tensor core operand from the register block and store the tensor core operand in a tensor core operand cache; the priority of the general calculation core is higher than that of the tensor core before the life cycle value of the to-be-executed read request reaches a preset value; the clock period counter triggers the tensor core operand cache to send the tensor core operand to the tensor core execution unit when the count value reaches a preset trigger value. According to the invention, the read bandwidth of the register block can be utilized to the maximum extent, and the overall performance is effectively improved.

The invention discloses an on-chip network system based on cache consistency, belongs to the technical field of computer network communication, and aims to solve the technical problems of global consistency flow overload, on-chip network scheduling delay, directory node load imbalance and too high core resource occupation of a traditional router architecture. Comprising a multi-core cluster, distributed directory nodes, a multi-channel network-on-chip and a hardware acceleration module, cores are connected through a bus, and local cache consistency is achieved based on an MSI protocol; a directory node divides a physical address space into a plurality of fragments based on an address hash fragmentation mechanism, each directory node maintains a sparse directory table, and when the length of a request queue of any directory node exceeds a set threshold value, a dynamic load balancing mechanism is triggered; the multi-channel network-on-chip provides a control channel, a data channel and a configuration channel, and each channel performs message scheduling based on a dynamic priority mechanism; and the hardware acceleration module is used for processing three types of consistent transactions.

The present application relates to the technical field of computers, and provides a processor resource scheduling method, device and system. The method comprises: request reception: receiving a current task request, forwarded by a proxy node, of a client user corresponding to any container; request enqueuing: adding the current task request to a task request queue of a processor corresponding to the client user; resource allocation: if a processor resource is idle, dequeuing a task request at the head of the task request queue, and allocating the processor resource to the currently dequeued task request, so that the processor executes a task corresponding to the currently dequeued task request; and result returning: upon completing task execution, returning a task execution result to the proxy node, and releasing the processor resource, and if the task request queue is not empty, jumping to the step of resource allocation. The present application significantly reduces the probability of task request failure frequently caused by insufficient resources when multiple tasks compete for a processor resource.

The invention provides an asynchronous communication system, method and device based on Netty and a medium, and belongs to the technical field of communication. The system comprises: an asynchronous network communication module, which is used for establishing asynchronous non-blocking network communication connection between a client and a server by using a Netty asynchronous non-blocking I / O model; the memory management module is used for carrying out allocation and recovery management on the memory in the communication process through a memory pool and object pool technology; the protocol adaptation module is used for adapting to different protocols through a self-defined codec and carrying out encoding and decoding processing on data of different protocols; and the back pressure control module is used for introducing a machine learning model, predicting the processing capability of the server and the request trend of the client, dynamically adjusting a semaphore threshold value or the length of a request queue, and realizing adaptive flow control. According to the invention, the communication efficiency is improved, the memory management is optimized, the protocol adaptation is flexible, and the adaptive flow control is realized, so that the performance and service adaptability of a communication system are enhanced.

The invention discloses a flow limiting method, device and equipment for AI large model calling and a medium, and the method comprises the steps that a gateway server constructs a target query key name through a client identifier and an application identifier after receiving a target calling request; if the matched distributed current limiter is not cached, obtaining a target calling level of the request; a ring buffer maintained by the model server is inquired, the residual flow value of the calling level under the current flow limiting section is obtained, the ring buffer divides time units according to the standard duration and maps a request queue, and the queue stores calling requests of all levels; and finally, constructing a target distributed flow limiter based on the flow limiting section and the residual flow value, and forwarding the request to the model server. By dynamically calculating the residual flow and intelligently constructing the flow limiter, high-precision and multi-level adaptive flow control is realized, the resource allocation and request fairness of the large model service is effectively balanced, and meanwhile, the efficient and stable operation of the system is ensured.

Cache, cache management method, and electronic device. The cache includes: multiple cache lines, a first read request queue and a second read request queue; a first read request queue is configured for storing and sending a first read request to a memory controller; the first read request is configured for requesting data from memory and storing the data in the memory controller; the number of the first read requests stored in the first read request queue is greater than that of the multiple cache lines; the second read request queue is configured for storing and sending a second read request to memory controller; and the second read request corresponds one-to-one with the first read request, and the second read request is used to request data corresponding to the first read request from the memory controller when a cache line corresponding to the first read request is idle

The invention relates to a solid state disk dynamic wear balancing method and system based on data popularity, and the method comprises the steps: tracking the access characteristics of each data page in a solid state disk through a page-level counter, and enabling the access characteristics to comprise the write operation frequency, the modification period and the survival time; the data pages are dynamically divided into high-heat data, medium-heat data or cold data based on a preset threshold value, and corresponding heat layering labels are marked; collecting accumulated writing times and I / O request queue length of each channel of the solid state disk in real time, and calculating wear imbalance degree and load imbalance degree; performing data exchange according to a preset exchange mode priority based on the popularity hierarchical label, the wear imbalance degree and the load imbalance degree; and updating the logic mapping table and the channel state parameters, carrying out statistics on the balance income and the system overhead, and dynamically correcting the tolerance coefficient based on the current load type according to the income-overhead ratio.

This invention provides an e-book lending management system based on OPAC bidirectional linkage, belonging to the field of library information technology. It includes: a hardware acceleration layer that constructs a dynamic resource distribution matrix and calculates the matrix norm in real time using an FPGA coprocessor, combined with an ASIC anti-collision controller to implement request queue scanning every 100ms and a redundant copy generation algorithm; a system service layer that deploys a dynamic priority scheduling engine, achieving intelligent resource allocation based on cross-campus collaboration coefficients and edge-cloud collaboration strategies, while recording borrowing operations and performing automated copyright revenue sharing audits through blockchain notarization services; and a user interaction layer that provides a bidirectional association search interface and a priority borrowing channel based on credit scoring. The system improves resource scheduling efficiency in high-concurrency scenarios through hardware-accelerated matrix norm calculation, an elastic copy allocation mechanism, and an anti-collision algorithm embedded in ASICs. Combined with geolocation scoring and blockchain technology, it achieves cross-campus resource collaboration and accurate allocation of copyright revenue.