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183 results about "Processor node" patented technology

Processor nodes. The processor nodes drive all functions in the storage system. Each node consists of a Power® server that contains POWER7® or POWER7+™ processors and memory.

High speed methods for maintaining a summary of thread activity for multiprocessor computer systems

A high-speed method for maintaining a summary of thread activity reduces the number of remote-memory operations for an n processor, multiple node computer system from n2 to (2n−1) operations. The method uses a hierarchical summary of-thread-activity data structure that includes structures such as first and second level bit masks. The first level bit mask is accessible to all nodes and contains a bit per node, the bit indicating whether the corresponding node contains a processor that has not yet passed through a quiescent state. The second level bit mask is local to each node and contains a bit per processor per node, the bit indicating whether the corresponding processor has not yet passed through a quiescent state. The method includes determining from a data structure on the processor's node (such as a second level bitmask) if the processor has passed through a quiescent state. If so, it is then determined from the data structure if all other processors on its node have passed through a quiescent state. If so, it is then indicated in a data structure accessible to all nodes (such as the first level bitmask) that all processors on the processor's node have passed through a quiescent state. The local generation number can also be stored in the data structure accessible to all nodes. If a processor determines from this data structure that the processor is the last processor to pass through a quiescent state, the processor updates the data structure for storing a number of the current generation stored in the memory of each node.
Owner:SEQUENT COMPUTER SYSTEMS

Dynamic memory affinity reallocation after partition migration

A method of dynamically reallocating memory affinity in a virtual machine after migrating the virtual machine from a source computer system to a destination computer system migrates processor states and resources used by the virtual machine from the source computer system to the destination computer system. The method maps memory of the virtual machine to processor nodes of the destination computer system. The method deletes memory mappings in processor hardware, such as translation lookaside buffers and effective-to-real address tables, for the virtual machine on the destination computer system. The method starts the virtual machine on the destination computer system in virtual real memory mode. A hypervisor running on the destination computer system receives a page fault and virtual address of a page for said virtual machine from a processor of the destination computer system and determines if the page is in local memory of the processor. If the hypervisor determines the page to be in the local memory of the processor, the hypervisor returning a physical address mapping for the page to the processor. If the hypervisor determines the page not to be in the local memory of the processor, the hypervisor moves the page to local memory of the processor and returns a physical address mapping for said page to the processor.
Owner:IBM CORP

SAR (Synthetic Aperture Radar) image segmentation method based on parallel sparse spectral clustering

InactiveCN101853491ASolve the problem of excessive calculationOvercome limitationsImage enhancementScene recognitionDecompositionSynthetic aperture radar
The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on parallel sparse spectral clustering, relating to the technical field of image processing and mainly solving the problem of limitation of segmentation application of large-scale SAR images in the traditional spectral clustering technology. The SAR image segmentation method comprises the steps of: 1, extracting features of an SAR image to be segmented; 2, configuring an MATLAB (matrix laboratory) parallel computing environment; 3, allocating tasks all to processor nodes and computing partitioned sparse similar matrixes; 4, collecting computing results by a parallel task dispatcher and merging into an integral sparse similar matrix; 5, resolving a Laplacian matrix and carrying out feature decomposition; 6, carrying out K-means clustering on a feature vector matrix subjected to normalization; and 7, outputting a segmentation result of the SAR image. The invention can effectively overcome the bottleneck problem in computation and storage space of the traditional spectral clustering technology, has remarkable segmentation effect on large-scale SAR images, and is suitable for SAR image target detection and target identification.
Owner:XIDIAN UNIV
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