8 results about "Cache locality" patented technology

This application provides an IP address network partitioning method, apparatus, device, and storage medium, belonging to the field of asset management technology. The method includes: converting a target IP address into an integrated IP address block, wherein the target IP address belongs to either IPv4 or IPv6; determining the host bit width based on a variable-length subnet mask, and logically right-shifting the tail address block within the IP address block according to the host bit width to obtain a bit value; comparing the bit value of the tail address block with a reference bit value of the tail address block in a preset network partitioning rule to determine whether the target IP address matches the network partitioning rule. This method eliminates code redundancy and branch prediction failures. It significantly improves cache locality and instruction pipeline efficiency, thereby enhancing the adaptability of the solution.

In one implementation, a computer implemented method for predicting new information based on a previous information may include creating a draft cache and a target cache within a memory of the computer wherein the draft cache and the target cache have a fixed length. The method may include storing tokens representing the information into the caches, the storing may start at a leftmost cache location. The method may also include forming a mask and an index or pointer for each cache. As new entries are formed, they are added to the caches, and the masks and pointers are updated to include to the entries that are added.

Apparatuses, systems, and techniques to cause information to be invalidated in a second cache location after information is stored in a first cache location. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause information to be invalidated in a second cache location after information is stored in a first cache location.

The application provides a regular path query system and method based on in-memory computing, the system comprises a host end; an in-memory computing end containing a plurality of in-memory computing modules working in parallel; the system divides a graph according to nodes based on the degree characteristics of the nodes, wherein high-degree nodes with a degree exceeding a threshold value and their adjacency relations are allocated to the host end for storage and processing, and low-degree nodes with a degree not exceeding the threshold value and their adjacency relations are allocated to the in-memory computing end for storage and processing. The application offloads random access tasks of low-degree nodes to a PIM end, fully utilizes the high-bandwidth characteristics of the PIM, and at the same time, retains high-degree nodes on the host end, utilizes the cache locality of the CPU, and thus improves the query efficiency.

The application discloses an edge network digital twin application migration and evolution method based on non-stationary online learning and belongs to the technical field of communication network resource management and edge computing. The application is suitable for digital twin application deployment and service request processing under end-edge-cloud cooperation. The method is based on non-stationary online learning, dynamically migrates and continuously evolves the digital twin application in the system to adapt to the mobility of physical entities and the continuous change of states. With the aim of maximizing the long-term average weighted service quality of the digital twin application under the premise of meeting the system cost constraint, the method comprises adopting a non-stationary slot machine learning assisted online joint optimization algorithm to determine the service priority of each type of service request in the digital twin application, the migration position of the digital twin application, the experience knowledge type for personalized service capability improvement, and the ability record fusion type and cache position for diversified service capability maintenance.

The application discloses a shortest instruction distance scheduling method and system of a smart contract virtual machine. The method comprises: static analysis and preprocessing, constructing an instruction dependency graph and calculating comprehensive weights of dependency edges; runtime dynamic scheduling, scheduling and synchronizing instructions according to dynamic priorities through a lock-free ready queue, a resource bitmap and a ring bus; multi-thread cooperation and load balancing, performing instruction-level work stealing among processor cores based on dynamic thresholds. The calculation of comprehensive weights and dynamic priorities takes minimizing global distance cost as an optimization target, and is used for improving cache locality and instruction-level parallelism of instruction execution. The system comprises a static analysis module, a runtime scheduling engine and a load balancer module. The application takes minimizing the execution distance of dependent instructions as a core optimization target, effectively improves instruction-level parallelism and cache locality, and thus significantly improves the execution performance and resource utilization of the smart contract virtual machine in a high-concurrency scenario.

A system and method for enhancing reasoning in neural network-based generative models through software-defined Virtual Neurons—active processing units with configurable activation functions and weighted connections—instantiated during inference without modifying pretrained parameters. Virtual Neurons maintain state vectors in dedicated in- memory data structures managed by a cognitive overlay layer, distinct from external storage and context-token buffers used by prior art. Organization follows hierarchical tiers scaled by α* = (γCs / βCf)^(1 / (β-γ)); for γ ≈ 0.8, β ≈ 1.5, α* ≈ √2, providing optimal path length and cache locality. Cognitively-isolated sandboxes with data-structure-level barriers enable Virtual Double hypothesis exploration by cloning internal cognitive state graphs (not token sequences), with O(1) rollback. A multilayer introspective feedback system evaluates internal cognitive structure (not output scores) and triggers structural modifications. Combined interaction produces runtime adaptation, speculative branching, and self-correction not achievable by components alone.

The application provides a kind of stereoscopic warehouse inventory management method and system, it is related to the technical field of inventory management, method includes: obtaining predicted order data and the task sequence of stacker, according to storage location and preset cache location, calculate the reference time difference that target goods can save from cache location compared with from storage location; according to the task sequence, calculate the subsequent task delay time caused by transporting target goods from storage location to cache location, recorded as opportunity time loss;Based on prediction confidence, reference time difference and opportunity time loss, calculate the comprehensive expected time benefit;When comprehensive expected time benefit is greater than or equal to preset time threshold, target goods are transported from storage location to cache location.The application can improve the efficiency of stereoscopic warehouse out-of-stock goods to some extent by using the above method.