Quickshift, Inc.

Interactive Silicon, Inc.

International Business Machines Corporation

Mossman Holdings LLC

Other

Dell USA L P

Peter Dau Geiger

Manuel Joseph Alvarez, Ii

Thomas A Dye

Sanjay Raghunath Deshpande

Praveen S Reddy

Joel Roger Davidson

Lawrence Joseph Powell, Jr

Thomas H Holman, Jr

Jeffrey Holland Gruger

Manuel Alvarez

A method and system for allowing a processor or I/O master to address more system memory than physically exists are described. A Compressed Memory Management Unit (CMMU) may keep least recently used pages compressed, and most recently and/or frequently used pages uncompressed in physical memory. The CMMU translates system addresses into physical addresses, and may manage the compression and/or decompression of data at the physical addresses as required. The CMMU may provide data to be compressed or decompressed to a compression/decompression engine. In some embodiments, the data to be compressed or decompressed may be provided to a plurality of compression/decompression engines that may be configured to operate in parallel. The CMMU may pass the resulting physical address to the system memory controller to access the physical memory. A CMMU may be integrated in a processor, a system memory controller or elsewhere within the system.

System and method for managing compression and decompression of system memory in a computer system

A network device, also referred to as the Compression Enhanced Network Processor (CENP), with embedded parallel (or fast serial) compression and/or decompression capability. The network device may be a network processor based multi-ported switch, bridge, router, hub, or other device. The CENP may provide improved data density, efficiency and bandwidth for each port of a multi-port network switch. In one embodiment, the CENP may comprise a network processor core, a memory management unit, a memory buffer (e.g., an SRAM memory buffer), and a system memory. The CENP may comprise a compression and decompression engine. In one embodiment, the memory management unit comprises the compression and decompression engine, and thus may be referred to as a Compression Enhanced Memory Controller Unit (CEMCU).

Network device with improved storage density and access speed using compression techniques

An integrated memory controller (IMC) including MemoryF/X Technology which includes data compression and decompression engines for improved performance. The memory controller (IMC) of the present invention preferably selectively uses a combination of lossless, lossy, and no compression modes. Data transfers to and from the integrated memory controller of the present invention can thus be in a plurality of formats, these being compressed or normal (non-compressed), compressed lossy or lossless, or compressed with a combination of lossy and lossless. The invention also indicates preferred methods for specific compression and decompression of particular data formats such as digital video, 3D textures and image data using a combination of novel lossy and lossless compression algorithms in block or span addressable formats. To improve latency and reduce performance degradations normally associated with compression and decompression techniques, the MemoryF/X Technology encompasses multiple novel techniques such as: 1) parallel lossless compression/decompression; 2) selectable compression modes such as lossless, lossy or no compression; 3) priority compression mode; 4) data cache techniques; 5) variable compression block sizes; 6) compression reordering; and 7) unique address translation, attribute, and address caches. The parallel compression and decompression algorithm allows high-speed parallel compression and high speed parallel decompression operation. The IMC also preferably uses a special memory allocation and directory technique for reduction of table size and low latency operation. The integrated data compression and decompression capabilities of the IMC remove system bottle-necks and increase performance. This allows lower cost systems due to smaller data storage, reduced bandwidth requirements, reduced power and noise.

Selective lossless, lossy, or no compression of data based on address range, data type, and/or requesting agent

A parallel decompression system and method that decompresses input compressed data in one or more decompression cycles, with a plurality of tokens typically being decompressed in each cycle in parallel. A parallel decompression engine may include an input for receiving compressed data, a history window, and a plurality of decoders for examining and decoding a plurality of tokens from the compressed data in parallel in a series of decompression cycles. Several devices are described that may include the parallel decompression engine, including intelligent devices, network devices, adapters and other network connection devices, consumer devices, set-top boxes, digital-to-analog and analog-to-digital converters, digital data recording, reading and storage devices, optical data recording, reading and storage devices, solid state storage devices, processors, bus bridges, memory modules, and cache controllers.

System and method for performing scalable embedded parallel data decompression

A system and method for managing a functional unit in a system using a data movement engine. An exemplary system may comprise a CPU coupled to a memory controller. The memory controller may include or couple to a data movement engine (DME). The memory controller may in turn couple to a system memory or other device which includes at least one functional unit. The DME may operate to transfer data to/from the system memory and/or the functional unit, as described herein. In one embodiment, the DME may also include multiple DME channels or multiple DME contexts. The DME may operate to direct the functional unit to perform operations on data in the system memory. For example, the DME may read source data from the system memory, the DME may then write the source data to the functional unit, the functional unit may operate on the data to produce modified data, the DME may then read the modified data from the functional unit, and the DME may then write the modified data to a destination in the system memory. Thus the DME may direct the functional unit to perform an operation on data in system memory using four data movement operations. The DME may also perform various other data movement operations in the computer system, e.g., data movement operations that are not involved with operation of the functional unit.

Managing a codec engine for memory compression/decompression operations using a data movement engine

System and method for managing compression and decompression and decompression of system memory in a computer system

A method and system for identifying and configuring device-enhanced memory modules at system startup is described. A driver is described that performs a wakeup procedure at startup to identify installed device-enhanced memory modules, detect memory implementations such as interleaving and striping on memory modules, detect error detection and correction (ECC) implementations, and to configure the identified device-enhanced memory modules to use the detected implementations. The method may include several phases including, but not limited to, a start block phase, an ECC configuration phase, an ECC check phase, an interleave detect and configuration phase, a buffer check phase, and a final configuration phase. One or more of the phases may be performed at system startup and/or during normal system operation. Methods for shutting down and providing a sleep mode for device-enhanced memory modules are also described.

System and method for recognizing and configuring devices embedded on memory modules

Embodiments of a compression/decompression (codec) system may include a plurality of data compression engines each implementing a different data compression algorithm. A codec system may be designed for the reduction of data bandwidth and storage requirements and for compressing/decompressing data. Uncompressed data may be compressed using a plurality of compression engines in parallel, with each engine compressing the data using a different lossless data compression algorithm. At least one of the data compression engines may implement a parallel lossless data compression algorithm designed to process stream data at more than a single byte or symbol at one time. The plurality of different versions of compressed data generated by the different compression algorithms may be examined to determine an optimal version of the compressed data according to one or more predetermined criteria. A codec system may be integrated in a processor, a system memory controller or elsewhere within a system.

System and method for generating optimally compressed data from a plurality of data compression/decompression engines implementing different data compression algorithms

An memory module including parallel data compression and decompression engines for improved performance. The memory module includes MemoryF/X Technology. To improve latency and reduce performance degradations normally associated with compression and decompression techniques, the MemoryF/X Technology encompasses multiple novel techniques such as: 1) parallel lossless compression/decompression; 2) selectable compression modes such as lossless, lossy or no compression; 3) priority compression mode; 4) data cache techniques; 5) variable compression block sizes; 6) compression reordering; and 7) unique address translation, attribute, and address caches. The parallel compression and decompression algorithm allows high-speed parallel compression and high-speed parallel decompression operation. The memory module-integrated data compression and decompression capabilities remove system bottlenecks and increase performance. This allows lower cost systems due to smaller data storage, reduced bandwidth requirements, reduced power and noise.

Memory module including scalable embedded parallel data compression and decompression engines

A parallel decompression system and method which decompresses input compressed data in one or more decompression cycles, with a plurality of tokens typically being decompressed in each cycle in parallel. A parallel decompression engine may include an input for receiving compressed data, a history window, and a plurality of decoders for examining and decoding a plurality of tokens from the compressed data in parallel in a series of decompression cycles. A token may represent one or more compressed symbols or one uncompressed symbol. The parallel decompression engine may also include preliminary select generation logic for generating a plurality of preliminary selects in parallel. A preliminary select may point to an uncompressed symbol in the history window, an uncompressed symbol from a token in the current decompression cycle, or a symbol being decompressed in the current decompression cycle. The parallel decompression engine may also include final select generation logic for resolving preliminary selects and generating a plurality of final selects in parallel. Each of the plurality of final selects points either to an uncompressed symbol in the history window or to an uncompressed symbol from a token in the current decompression cycle. The parallel decompression engine may also include uncompressed data output logic for generating the uncompressed data from the uncompressed symbols pointed to by the plurality of final selects, and for storing the symbols decompressed in this cycle in the history window. The decompression engine may also include an output for outputting the uncompressed data produced in the decompression cycles. The decompression engine may be divided into a series of stages. The decoders may be included in a first stage. The preliminary select generation logic may be included in a second stage. The final select generation logic may be included in a third stage. The output logic may be included in a fourth stage.

Embodiments of a compression/decompression (codec) system may include a plurality of parallel data compression and/or parallel data decompression engines designed for the reduction of data bandwidth and storage requirements and for compressing/decompressing data. The plurality of compression/decompression engines may each implement a parallel lossless data compression/decompression algorithm. The codec system may split incoming uncompressed or compressed data up among the plurality of compression/decompression engines. Each of the plurality of compression/decompression engines may compress or decompress a particular part of the data. The codec system may then merge the portions of compressed or uncompressed data output from the plurality of compression/decompression engines. The codec system may implement a method for performing parallel data compression and/or decompression designed to process stream data at more than a single byte or symbol at one time. A codec system may be integrated in a processor, a system memory controller or elsewhere within a system.

Parallel compression and decompression system and method having multiple parallel compression and decompression engines

A distributed system structure for a large-way, symmetric multiprocessor system using a bus-based cache-coherence protocol is provided. The distributed system structure contains an address switch, multiple memory subsystems, and multiple master devices, either processors, I/O agents, or coherent memory adapters, organized into a set of nodes supported by a node controller. The node controller receives commands from a master device, communicates with a master device as another master device or as a slave device, and queues commands received from a master device. Due to pin limitations that may be caused by large buses, e.g. buses that support a high number of data pins, the node controller may be implemented such that the functionality for its address paths and data paths are implemented in physically separate components, chips, or circuitry, such as a node data controller or a node address controller. In this case, commands may be sent from the node address controller to the node data controller to control the flow of data through a node.

Method and system for controlling data transfers with physical separation of data functionality from address and control functionality in a distributed multi-bus multiprocessor system

A graphics controller which performs display list-based video refresh operations that enable objects with independent frame rates to be efficiently assembled is disclosed. The graphics controller maintains a virtual display refresh list (VDRL) comprising a plurality of pointers to scan line segments in memory. The graphics controller also creates, maintains, and deletes draw display lists (DDLs) that comprise pointers to object display list subroutines (ODLs) that independently draw objects in memory. The ODLs may allocated one or more buffers in memory into which different frames of the objects are drawn. When an ODL has completed executing, the corresponding pointer in the DDL may be updated to point to the buffer location in memory that stores the newly completed object frame. The VDRL is maintained independently (and may be doubled-buffered) and is updated using the DDLs. Motion estimation may be performed by the graphics controller using the different frames of objects that are drawn into memory by the ODLs. The different object frames may also be animated by the graphics controller once they are drawn into memory. The object frames stored in memory may be compressed to conserve memory.

Video controller system with object display lists

An ASIC device embedded into the memory subsystem of a computing device used to accelerate the transfer of active memory pages for usage by the system CPU from either compressed memory cache buffer or the addition of a compressed disk subsystem for improved system cost and performance. The Compression Enhanced Dual In-line Memory Module of the present invention uses parallel lossless compression and decompression engines embedded into the ASIC device for improved system memory page density and I/O subsystem data bandwidth. In addition, the operating system software optimizes page transfers between compressed disk partitions, compressed cache memory and inactive/active page memory within the computer system. The disclosure also indicates preferred methods for initialization, recognition and operation of the ASIC device transparently within industry standard memory interfaces and subsystems. The system can interface to present operating system software and applications, which enable optimal usage for the compressed paging system memory environment. The integrated parallel data compression and decompression capabilities of the compactor ASIC mounted on industry standard memory modules, along with the software drivers and filters of the present invention keep recently used pages compressed in the system memory. Additional performance is gained by the transfer of compressed pages between the system memory and the disk and network subsystems. In addition, the present invention may reduce the amount of data transferred between distributed computers across the LAN or WAN by the transmission of compressed page data between remote systems or distributed databases.

PARALLEL COMPRESSION/DECOMPRESSION SYSTEM AND METHOD FOR IMPLEMENTATION OF IN-MEMORY COMPRESSED CACHE IMPROVING STORAGE DENSITY AND ACCESS SPEED FOR INDUSTRY STANDARD MEMORY SUBSYSTEMS AND IN-LINE MEMORY MODULES

A spanning based method for rendering and display of 3D graphical data on a display device. The method first parses the geometry data, generates independent vertex-sorted geometric primitives (e.g., triangles) and then performs setup on the geometric primitives. The method then computes horizontal segments that make up each triangle, performs a Y sort of the triangles for each span line, and performs an X sort of triangle segments and vertices for each span line. The method then performs a Z rules determination for each span line to discard or reject hidden segments. The method then constructs the 3-D VDRL list for each span line comprising pointers which reference viewed triangle spans. During execution, the 3-D VDRL is read and interpreted to generate pixel data. The pixel data includes the viewed triangle spans and may include texture data or other data referenced by the VDRL.

Growing community of inventors

Austin, TX, United States of America

Peter Dau Geiger