553 results about "Memory pool" patented technology

The invention provides a data management method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining key value cache data generated by a first type of computing power unit in a pre-filling stage, transmitting the key value cache data to a memory pool, and caching the key value cache data in the memory pool; and in response to a data acquisition demand of the second type of computing power unit in a decoding stage, transmitting the specified key value cache data cached in the memory pool to the second type of computing power unit according to the data acquisition demand. According to the technical scheme of the invention, the memory pool is arranged and the aggregated transmission channel is utilized, so that the transmission efficiency of the key value cache data between the computing power devices is effectively improved, the data transmission delay is reduced, and the network transmission protocol overhead is reduced, thereby improving the data interaction efficiency of the computing power devices in a pre-filling stage and a decoding stage, and improving the user experience. The utilization of computing power resources is optimized, and the throughput and performance of large model reasoning are improved.

The invention discloses a memory fault repairing method and device, equipment, a medium and a computer program product, and relates to the technical field of computers.The memory fault repairing method includes the steps that memory error information sent by a memory controller is obtained, a fault target memory page can be positioned, and a standby memory page is obtained from a standby memory pool; the memory address mapping table is updated, the physical address of the target memory page with the fault is mapped to the physical address of the standby memory page, the repairing process does not depend on triggering of a system management interrupt mechanism, memory repairing in the system running stage is achieved, the system downtime caused by memory fault repairing is shortened, and therefore the memory repairing efficiency is improved. The problem of high delay in memory fault processing can be solved, and the technical effect of improving the memory fault repairing efficiency is achieved.

The invention discloses a long video multi-modal understanding and question-answering method and system based on large model and retrieval enhancement generation. The method comprises the following steps: 1) a multi-modal feature extraction module; 2) a multi-modal synchronization and alignment mechanism; 3) constructing a structured memory pool; 4) querying a drive generation mechanism; 5) incremental updating and memory compression strategy; and 6) unifying the multi-modal representation space. The invention provides a long video multi-mode understanding method fusing a large language model and retrieval enhancement generation, and aims to break through the limitation of a traditional method in the aspects of single-mode processing and semantic fragmentation. According to the method, video image features are extracted through a visual model (such as YOLO and ViT), voice transcription and environment voice description are obtained in combination with an audio model (such as Whisper and Qwen-Audio), and unified coding of vision, voice and audio in a long video is achieved. Then, a structured memory pool is constructed through semantic consistency segmentation and timestamp alignment technologies to store time slice data of different modalities.

The invention discloses a compatible operation method for an Android camera HAL in a container based on memory access virtualization, and the method comprises the steps: taking a DMA-Buf memory heap of a Linux kernel host system as a target memory heap, taking an ION memory heap of an Android container system as a source memory heap, building a process exclusive memory management context, an FD cache table and a synchronous fence pool, and carrying out the execution of the process exclusive memory management context, the FD cache table and the synchronous fence pool; kernel registration and node binding of virtual ION equipment, pre-allocation of a first memory pool and access hook registration of kernel layer equipment are completed, and when an ION file descriptor is obtained in an HAL process, legality of a container process is verified, and context binding is initialized to the file descriptor; intercepting a memory allocation request of the HAL process, analyzing and adapting parameters, and preferentially multiplexing first memory pool resources to obtain an ION handle; when a data sharing request is processed, matching an FD cache or generating a new FD through an ION handle; when the HAL process releases the memory, resources are recycled according to a memory source, and when the HAL process exits, the context is cached or destroyed, so that cross-system memory operation compatibility is realized.

The invention discloses a multi-source data fusion type intelligent data management system, which comprises a data preprocessing module used for accessing a heterogeneous data source and generating a standardized data frame; the cognitive sub-graph construction module is used for generating cognitive sub-graphs and forming a cross-source evolutionary multi-cognitive hypergraph; the event aggregation module is used for aggregating entities and relationships according to event fingerprints and outputting a multi-source observation chain; the causal checking module is used for performing causal comparison and conflict detection and outputting a checking result, a positioning report and a treatment suggestion; the fusion updating module is used for distributing weights and performing weighted fusion, outputting fusion representation and confidence score, recording conflicts and updating the hypergraph; and the memory pool module is used for storing the hypergraph, the check result and the fusion representation, executing weight adjustment, forgetting and elimination, and outputting a service interface. According to the method, intelligent fusion and dynamic management of multi-source heterogeneous data are realized by constructing a cross-source self-evolution multi-cognitive hypergraph and an active evolution type fusion memory pool.

The invention provides a memory pooling method and system for model reasoning acceleration and a computer program product, and relates to the technical field of computers. The model is a separation pooling architecture, the separation pooling architecture comprises a pre-filling node pool, a decoding node pool and a CXL memory pool which are separated, and the memory pooling method for model reasoning acceleration comprises the steps that a reasoning request is allocated to a first pre-filling node in the pre-filling node pool based on a first scheduling strategy; the first pre-filling node processes the reasoning request to obtain a key value cache; storing the key value cache in a CXL memory pool; selecting a first decoding node in the decoding node pool based on a second scheduling strategy; the first decoding node obtains a key value cache from a CXL memory pool based on the reasoning request; and the first decoding node generates a reasoning result according to the obtained key value cache.

The invention provides a city calculation basic model and equipment based on a Mama time sequence, and relates to the technical field of data processing. In the model provided by the embodiment of the invention, a Mama framework is combined with multi-task time sequence analysis. Aiming at the non-stationarity of urban time series data, a frequency prompt network is designed for decoupling in combination with a Koopman operator theory and dynamic mode decomposition, a steady mode and non-stationary fluctuation are analyzed from the angles of time and frequency, and a frequency mode memory pool (FPMP) is introduced for realizing cross-scene feature reuse. The advantage of a Mama model in capturing long-term time dependence is utilized, and an attention mechanism similar to Mama is designed to simulate channel dependence in a multivariable time sequence. A learnable supplementary sequence and a multi-task prompt mark are introduced to support dynamic multi-task adaptation, and the contradiction between urban data isomerism and task diversity is relieved. Shared knowledge and specific task knowledge are balanced through shared experts and specific task experts of the multi-task attention module, the negative migration problem in multi-task learning is relieved, and the generalization ability of the model is enhanced.

The invention discloses an impurity sorting and removing system based on machine vision, and the system comprises a data collection and preprocessing layer which is responsible for obtaining multi-dimensional physical characteristics of tea leaves and impurities, and constructing three-dimensional scene representation; the feature analysis and decision-making layer is used for extracting light field fusion features by utilizing a ResNeSt-50 backbone network, directly outputting a preliminary classification confidence coefficient through a full connection layer, forming a first layer of decision-making candidates, simulating branches by combining rigid body dynamics to predict a blade movement track, performing Bayesian correction on the preliminary classification confidence coefficient after a shielding probability graph is sampled and calculated through Monte Carlo Dropout, and forming a second layer of decision-making candidates; meanwhile, a Cook-Torrent BRDF model is integrated to carry out material reflection correction, a feature map after material correction is output, and a correction value is converted into decision threshold offset; and the knowledge storage and update layer is used for constructing a bimodal memory pool composed of a gradient direction matrix and an LRU elastic cache, and generating a pseudo sample supplement long-tail category in combination with the four-layer full-connection pulse neural network.

Memory has been playing a major role in the performance, scalability and applicability of General Compute systems, and more recently, in realizing Generative Artificial Intelligence (GenAI) and High-Performance Computing (HPC) systems that scale to thousands of GPUs, CPUs and special-purpose Accelerators. Embodiments herein disclose efficient software-defined protocol terminations and protocol translations utilizing Compute Express Link (CXL), including translations between CXL.mem and CXL.cache protocols. Also disclosed are CXL-based systems, Resource Provisioning Units (RPUs), and Memory Fabric Switches enabling dynamic memory pooling and sharing, host-to-host communication utilizing CXL.mem, CXL.cache and CXL.io, intent-based protocol translations, and optionally seamless interactions between CXL, UALink, NVLink, and / or Ethernet protocols, utilizing a broad range of semantics including IO, Cache, and Memory, optimizing memory access and reducing latency. Some embodiments also enable scalability, flexibility and security in high-performance architectures suited for data centers and next-generation computing environments.

The invention discloses an intelligent parking dynamic scheduling optimization method based on multi-source data fusion, and the method comprises the following steps: S1, collecting multi-source data, and carrying out the preprocessing of the multi-source data; s2, constructing a heterogeneous graph structure, and generating a node feature matrix and a heterogeneous adjacency tensor; s3, extracting a state fusion vector, constructing a joint scoring matrix, and generating a candidate path set; s4, setting target parameters, executing path search and pheromone iteration, and outputting a regional path set; s5, adjusting a node preference weight, executing regional path reordering, and outputting an optimal vehicle parking path; and S6, recording the whole scheduling process, updating the network parameter weight through the sliding window memory pool, and generating an incremental learning sample set. According to the invention, high-precision, low-delay and personalized intelligent parking dynamic scheduling under the driving of multi-source data can be realized, and the parking efficiency and the user experience are effectively improved.

The invention discloses an incomplete multi-mode crisis event detection method based on a memory pool and a mode perception expert system, and relates to the field of machine learning, and the method specifically comprises the steps: building a prompt strongly related to an event instance based on the memory pool, and complementing a missing mode through a large model; a CNN encoder, a ResNet encoder and a CLIP encoder are utilized to construct a double-flow encoder, and text and visual feature extraction is achieved; capturing fine-grained event information and clues; the distribution difference between a complemented sample and a complete sample is identified based on a modal perception expert system, and the quality of fusion representation is improved; and inputting the mixed features into the classification head to complete a crisis event detection task. According to the invention, through the prompt construction module based on the memory pool, a large model is introduced to realize careful completion of a missing mode, and the distribution difference between a completion sample and a complete sample is accurately identified based on a mode perception expert system, so that a multi-mode data missing scene can be effectively coped with, and the accuracy and timeliness of crisis event detection are improved.

The invention relates to the technical field of printing task scheduling, in particular to a printing task scheduling method and system based on a main control chip and a terminal, and the method comprises the steps of initial task classification, dynamic priority adjustment, task queue recombination, resource allocation execution, termination mechanism triggering and the like. The high-priority task response speed is optimized through system load monitoring and a waiting time coefficient, and a real-time task pool and an elastic buffer pool are divided to improve the scheduling efficiency. According to the method, the real-time task pool and the elastic buffer pool are divided through an algorithm of'dynamic memory pool partitioning + task life cycle binding ', and the memory blocks are pre-allocated according to the task types, so that the real-time performance and the scheduling efficiency of the high-concurrency printing tasks are improved, rapid response of high-priority tasks can be realized, delay of key tasks is reduced, and the efficiency of the high-concurrency printing tasks is improved. The resource utilization rate and the scheduling fairness are improved, and the method is suitable for high-concurrency printing scenes.

The invention provides a self-adaptive information retrieval and optimization system based on retrieval enhancement generation, which comprises a prospective answer module used for receiving a query statement and context information and inputting the query statement and the context information into a large language model so as to generate a prospective preliminary answer; the confidence coefficient detection module is used for setting a confidence coefficient score threshold value to carry out keyword-level confidence coefficient evaluation on the content in the prospective preliminary answer, correcting a low-confidence-coefficient part in the answer according to an evaluation result, and outputting a complete answer content as a confidence coefficient answer; the knowledge graph extension query module is used for executing named entity recognition operation on the confidence answer and obtaining an extension query answer in combination with a pre-constructed small knowledge graph; and the context memory pool module is used for storing the context information input for the first time and related information fragments obtained in each round of retrieval process, preferentially performing information retrieval from the memory pool in the subsequent retrieval process, and accessing the external index database only when no matched content exists in the memory pool.

The invention discloses a neural radiation field rendering method based on dynamic hash coding, and the method comprises the steps: employing the feature sequence data as the input, calculating the density value and color value of each sampling point through the forward propagation of a neural network, carrying out the volume rendering integral operation according to the ray tracing principle in the direction of a ray, and obtaining the feature sequence data; judging a final color output result of the current pixel point; according to an error value between the color output result and a real image, updating a network parameter weight through a back propagation algorithm, and if the error value is greater than a convergence threshold, continuing to iterate the training process to adjust a feature coding strategy to obtain an optimized neural radiation field model parameter; and after the rendering performance configuration parameters are obtained, optimizing a storage allocation strategy of feature data through a memory pool management mechanism, and if the current memory occupancy rate exceeds a safety threshold, starting a data compression algorithm to reduce the storage space requirement, and obtaining a real-time rendering output result. According to the invention, high-quality real-time rendering of the dynamic scene is realized.

The invention discloses a context-aware-based gap containerization TLS compatible method, which comprises the following steps of: distinguishing a simple TLS variable and a complex TLS variable in a source gap system, and establishing a TLS variable source feature; the method comprises the following steps: constructing a dynamic telescopic TLS memory pool matched with a variable access permission in a target system, expanding a kernel state TLS segment descriptor, constructing a bidirectional mapping table of musl TLS offset and a memory pool address, and optimizing and constructing a clustering structure aligned with a CPU cache line for a hotspot variable to improve the access efficiency; tLS variable access tracks are recorded through a dynamic binary translation engine, TLS segment descriptors are updated, the descriptors are read during thread switching to recover TLS context and refresh cache to guarantee consistency, address and offset conversion is completed according to target system alignment requirements by combining source features and a bidirectional mapping table for TLS variables of nested structures, pointers and array types, and the TLS variables of the nesting structures, the pointers and the array types are aligned. And completing dynamic conversion execution of the source system application program in the target system.

The invention provides a 3DGS model real-time processing method and device based on an unreal engine, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: writing 3DGS data into a partitioned memory pool structure; writing data in the partitioned memory pool structure into a memory buffer area, performing coordinate system alignment processing according to a world coordinate system of an unreal engine, and performing spatial partitioning on the aligned processing data according to a point cloud density and a region density difference coefficient; converting the data in each spatial block into a point cloud data structure compatible with the Niagara particle system, and completing data format conversion in a graphics processor by using a calculation shader to obtain second processing data; and based on the constructed multi-level resource pool management strategy and a runtime memory monitoring mechanism, performing rendering processing on the second processing data according to a track prediction result of the camera, and generating a rendering result under an unreal engine. The processing efficiency of three-dimensional modeling in the unreal engine can be improved.

The embodiment of the invention provides a data migration method, an access processing method, a data migration system and electronic equipment. The data migration method is applied to a switch, the switch communicates with a plurality of memory devices in a memory pool based on an interconnection protocol, and each memory device is provided with a plurality of memory areas. When data migration needs to be carried out on a first memory area in a memory pool, a switch sequentially migrates a plurality of data pages obtained by dividing data in the first memory area into available second memory areas according to pages, and in the migration process, a page bitmap determined for the first memory region is utilized to record migration states of the plurality of data pages. By adopting the scheme, the problem of large-area fault of the server caused by the memory problem can be effectively solved, the page-level granularity is adopted to migrate the data in the first memory region page by page in the migration process, the access of the server to the first memory region is continuously processed in the migration process, and the influence on the performance of the server is reduced.

The invention discloses an auditing data security management and control system based on big data, and relates to the field of data management and control, and the system comprises a distributed crawler which collects auditing logs, operation records and network traffic in real time. And the stream batch integrated engine performs metadata alignment and timestamp synchronization on heterogeneous data to generate a unified audit event stream. The intelligent desensitization module carries out gradient desensitization on the sensitive fields and constructs reversible blood relationship mapping. According to the hierarchical storage, data are stored in an encrypted memory pool, an SSD and a cold standby cluster according to data sensitivity and access frequency, and consistent hash load balancing is achieved. Multi-modal safety monitoring analyzes data in real time through a three-level assembly line. And the hierarchical response module writes a permission chain according to the risk trigger action and the key audit event hash value, and constructs a tamper-proof evidence chain. The method has the advantages that the data security, privacy protection and efficient emergency response are ensured through intelligent desensitization, hierarchical storage and security analysis of the audit data, and the non-tampering property of the data is ensured by utilizing the block chain technology.

The invention discloses an operating system containerization kernel compatibility method based on shadow structure mapping, which comprises the following steps of: constructing a multi-dimensional mapping rule base by analyzing the difference of data structures between a container system and a standard system, and creating a shadow structure comprising a data area, a metadata area and an expansion area when the container system runs on a host system; the host system allocates a shadow memory space and a memory pool, establishes mapping between a shadow structure and a physical memory page and bidirectional mapping between the shadow structure and a host structure, intercepts system call of a container application, obtains a target structure and allocates a shadow structure and a host structure memory, and sends the shadow structure and the host structure memory to the host system; according to the method, conversion from the target structure to the host structure is completed according to the multi-dimensional mapping rule base, the host machine completes calling through the host structure, then the calling result is reversely converted into the target structure and returned to the container application, and seamless compatibility of the container system and the host system is achieved under the condition that a user mode program of the container system is not modified.

The invention discloses a resource management method and device, equipment and a medium, and relates to the technical field of storage, the resource management method comprises the following steps: carrying out performance test and data analysis on different storage architectures according to an I / O path of a distributed storage system and an asynchronous programming model, and determining processor static parameters under the different storage architectures according to the results of the performance test and the data analysis; the method comprises the following steps: collecting performance indexes of a distributed storage system through an asynchronous programming model, predicting load change conditions of the system according to the performance indexes of the distributed storage system, and dynamically adjusting processor resources according to the load change conditions of the system; managing a large-page memory pool and a cache according to the large-page memory management model and the business model type of the distributed storage system; and optimizing a memory application and release mechanism in the storage service of the distributed storage system. Through dynamic scheduling of processor resources and a large-page memory pool data sharing mechanism, balanced allocation of storage and calculation resources is realized, and the system performance and the resource utilization rate are improved.

The invention discloses a method and a system for tracking and detecting an occluded target in an electric power scene based on thunder-vision integrated multi-mode spatio-temporal adversarial learning, and relates to the field of intelligent inspection of electric power equipment, and the method comprises the steps: inputting a structured point cloud matrix and a visual image into a double-branch feature extraction model, respectively extracting geometric features and semantic features by using a double-branch feature extraction model; inputting the bimodal fusion features into a dynamic weight adaptive fusion mechanism based on an adversarial training strategy to obtain a fusion weight coefficient of the target in a short-time shielding state in the power scene; and a double-buffer memory pool is constructed, and a three-level progressive trajectory prediction compensation mechanism is combined to compensate a target trajectory position in a long-time shielding state in a power scene. According to the method, the shielding state quantitative model is constructed, the fusion weight distribution proportion can be effectively and dynamically adjusted in real time according to the visual sensor, and when it is detected that the target equipment enters the shielding state, the visual data weight is automatically reduced to the safety threshold value.

A real-time target detection method and system based on RTSP flow and NPU collaborative optimization belong to the technical field of computer vision and artificial intelligence, and are characterized by comprising the following steps: adopting dynamic memory optimization management of a hybrid pipeline architecture, and performing single-time continuous copying through a CPU (Central Processing Unit); through deep integration of innovative technologies such as DMA direct transmission NPU continuous memory pool management, dynamic batch processing scheduling, hybrid assembly line processing, intelligent equipment load balancing, parallel preprocessing optimization and vectorization post-processing, RTSP flow collaborative optimization and the like, the NPU utilization rate is improved to 85% or above, the overall average FPS is improved by 200% or above, the assembly line parallelism degree achieves three times of performance gain, and the production efficiency is greatly improved. The data transmission delay is reduced by 80%, the system stability is remarkably improved, performance degradation is avoided after long-time operation, and the method is suitable for various scenes such as edge calculation and cloud reasoning.

The invention provides a dialogue interaction processing method, device and equipment, a computer readable storage medium and a computer program product. The method can be applied to the field of financial science and technology. The method comprises the steps of obtaining current dialogue information input by a user, and extracting current semantic features of the current dialogue information; searching historical semantic features with the highest relevancy with the current semantic features from a dialogue memory pool, and taking the historical semantic features as target historical semantic features; wherein historical semantic features of all historical dialogue information in the current session are stored in the dialogue memory pool; the current semantic features and the target historical semantic features are spliced and then input into an intention recognition model, and the current intention of the user is obtained; predicting a subsequent intention of the user according to the current intention; and dialogue response information is generated according to the subsequent intention, and the dialogue response information is fed back to the user. According to the method, the dialogue response message can better meet the intention demand of the user, so that the user interaction experience is improved.

The invention discloses a CXL protocol extension method, device and system based on the Ethernet. The method comprises the following steps of: carrying out long-distance expansion interconnection on a CXL (Compute Express Link) protocol by using Ethernet as a physical transmission medium; by bearing the CXL protocol on the Ethernet, the range of memory pooling is expanded from the rack level to the whole data center (kilometer level), and the potential of the CXL technology is thoroughly released; through protocol translation, label management and flow control realized by pure hardware, an operating system kernel and a software protocol stack are bypassed, so that the end-to-end memory access delay is far lower than that of any software-based network storage scheme, and a reliable request-response tracking and timeout retransmission mechanism is realized through a hardware label manager. By distinguishing the control flow and the data flow, differentiated QoS guarantee is provided for different types of memory flows, and the stability of the system under high load is ensured.

The invention relates to the technical field of memory expansion, and discloses a memory access system and method for a memory pool, the system is connected with PC I e equipment and CXL equipment, the PC I e equipment is used for sending an access request to a host so as to access the CXL memory pool through the host; the CXL equipment is used for sending a read-write command to the CXL switch so as to access the CXL memory pool through the CXL switch, the system comprises a host which is respectively connected with the PC I e equipment and the CXL switch, and the host is used for receiving an access request sent by the PC I e equipment, converting the access request into the read-write command and sending the read-write command to the CXL switch so as to access the CXL memory pool through the CXL switch, cXL equipment and PC I e equipment which are short in memory are expanded through the CXL memory pool, the memory access system of the memory pool can be compatible with the CXL equipment and the PC I e equipment at the same time, unified memory distribution and memory management are provided for the CXL equipment and the PC I e equipment, memory sharing between the CXL equipment and the PC I e equipment is achieved, memories of the CXL equipment and the PC I e equipment are expanded, and therefore the memory access efficiency and the memory utilization rate are improved.

The invention provides a real-time scheduling method for a wireless network based on a large language model, the wireless network comprises a scheduling node and a plurality of user nodes, and the scheduling node is provided with a scheduling agent, a historical memory pool, an reflection agent and a suggestion buffer area. The method comprises the following steps executed in each time slot: step S1, the scheduling node acquires global information of a wireless network under the current time slot and calculates states of all data streams under the current time slot according to a preset calculation rule; and S2, the scheduling agent generates a scheduling decision of the current time slot according to a preset scheduling prompt word by using a large language model configured by the scheduling agent based on the states of all data streams under the current time slot and decision suggestions stored in the suggestion buffer area and by taking a maximized reward as a target. According to the technical scheme of the invention, by introducing the large language model, the problem of insufficient adaptability in a dynamic network environment in the prior art is solved.

The invention discloses a data transmission method and device, equipment, a storage medium and a computer program product, and the method comprises the steps that first equipment responds to first system call of an application process, and divides a first data buffer area into a plurality of sections; the first data buffer area is used for buffering first data written by an application process; allocating a first mbuf and a plurality of second mbufs from the first memory pool based on the plurality of sections; the first mbuf is used for storing message header information corresponding to a first message, and the first message represents a message used for transmitting first data; each second mbuf in the plurality of second mbufs is used for indicating one section; linking one first mbuf and a plurality of second mbufs into a first linked list, and mapping the first linked list into a network card, so that the network card sends a first message to the second equipment; the first linked list is used for the network card to read the first data from the first data buffer area and encapsulate the first data into a first message.

The invention discloses a memory pooling system, a control method thereof, a power-on method, a power-on device and electronic equipment, and relates to the technical field of computers, through a multiplexer dynamic selection mechanism of a memory expansion unit, a first management link of a controller module for managing a memory buffer or a second management link of the controller module for managing a memory card is constructed, and the memory expansion unit is used for storing the memory. An external device to be expanded does not need to participate in memory device management, under the condition that the number of physical pins is limited, large-capacity memory expansion can be achieved only by connecting a memory buffer and a memory card, a memory board forms a pooling resource pool for capacity expansion by configuring the memory card, meanwhile, a multiplexer efficiently multiplexes a limited signal channel of the switching module, and the expansion efficiency is improved. The controller module can intelligently switch management paths according to instruction attributes, and finally the to-be-expanded device breaks through the limitation of physical pins and synchronously obtains the large-capacity and high-bandwidth memory expansion capacity.

A computer-implemented method implemented in a blockchain network is described. Validation nodes receive data regarding a newly mined block comprising a plurality of transactions and send a delete request to a distributed memory pool to delete the plurality of transactions from the distributed memory pool. Nodes storing the distributed memory pool store a plurality of transactions, the plurality of transactions forming at least part of a distributed memory pool of transactions waiting to be mined into a block of a blockchain. The computer-implemented method further comprises receiving a delete request from a validation node of the blockchain network, the delete request identifying one or more transactions which have been included in a newly mined block, the delete request indicating that the one or more transactions should be deleted from the distributed memory pool.

Provided is a system and method for memory pool management, the method including receiving, by a first control circuit of a memory device controller, a first request to create a first logical memory space, the first control circuit being associated with a first memory zone, based on determining that a first memory unit, at a first physical address range of the first memory zone is available, allocating the first memory unit to the first logical memory space, based on determining that a second memory unit of the first memory zone is occupied, sending a memory-donation request to a second control circuit of the memory device controller, the second control circuit being associated with a second memory zone, and based on determining that a third memory unit of the second memory zone is available, allocating the third memory unit to the first logical memory space.