Teradata US, Inc.

Sanera Systems, Inc.

Sun Microsystems, Inc.

Brocade Communications Systems, Inc.

Liuxi Yang

Joseph I Chamdani

Jeremy L Branscome

Michael Paul Corwin

Krishnan Meiyyappan

Kapil Laxmikant Surlaker

Ravi Krishnamurthy

Yu Fang

Ulrich Stern

James Shau

Duong Tong

Raj Cherabuddi

Ravindran Krishnamurthy

Storage devices and components, including memory components (e.g., non-volatile memory) can be trained by executable code that facilitates and/or performs reads and/or write requests to one or more storage sub-modules of a storage component (e.g., memory configured on a memory channel) made up of multiple storage components (e.g., DIMMs). The executable code can also train multiple storage components at the same time and/or in parallel.

Training of storage devices in computing systems and environments

Embodiments of the present invention provide hardware-friendly indexing of databases. In particular, forward and reverse indexing are utilized to allow for easy traversal of primary key to foreign key relationships. A novel structure known as a hit list also allows for easy scanning of various indexes in hardware. Group indexing is provided for flexible support of complex group key definition, such as for date range indexing and text indexing. A Replicated Reordered Column (RRC) may also be added to the group index to convert random I/O pattern into sequential I/O of only needed column elements.

Accessing data in a column store database based on hardware compatible indexing and replicated reordered columns

Embodiments of the present invention provide one or more hardware-friendly data structures that enable efficient hardware acceleration of database operations. In particular, the present invention employs a column-store format for the database. In the database, column-groups are stored with implicit row ids (RIDs) and a RID-to-primary key column having both column-store and row-store benefits via column hopping and a heap structure for adding new data. Fixed-width column compression allow for easy hardware database processing directly on the compressed data. A global database virtual address space is utilized that allows for arithmetic derivation of any physical address of the data regardless of its location. A word compression dictionary with token compare and sort index is also provided to allow for efficient hardware-based searching of text. A tuple reconstruction process is provided as well that allows hardware to reconstruct a row by stitching together data from multiple column groups.

Accessing data in column store database based on hardware compatible data structures

Embodiments of the present invention provide a database system that is optimized by using hardware acceleration. The system may be implemented in several variations to accommodate a wide range of queries and database sizes. In some embodiments, the system may comprise a host system that is coupled to one or more hardware accelerator components. The host system may execute software or provide an interface for receiving queries. The host system analyzes and parses these queries into tasks. The host system may then select some of the tasks and translate them into machine code instructions, which are executed by one or more hardware accelerator components. The tasks executed by hardware accelerators are generally those tasks that may be repetitive or processing intensive. Such tasks may include, for example, indexing, searching, sorting, table scanning, record filtering, and the like.

Methods and systems for hardware acceleration of database operations and queries

Embodiments of the present invention provide a hardware accelerator that assists a host database system in processing its queries. The hardware accelerator comprises special purpose processing elements that are capable of receiving database query/operation tasks in the form of machine code database instructions, execute them in hardware without software, and return the query/operation result back to the host system. For example, table and column descriptors are embedded in the machine code database instructions. For ease of installation, the hardware accelerators employ a standard interconnect, such as a PCle or HT interconnect. The processing elements implement a novel dataflow design and Inter Macro-Op Communication (IMC) data structures to execute the machine code database instructions. The hardware accelerator may also comprise a relatively large memory to enhance the hardware execution of the query/operation tasks requested. The hardware accelerator utilizes hardware-friendly memory addressing, which allows for arithmetic derivation of a physical address from a global database virtual address simply based on a row identifier. The hardware accelerator minimizes memory reads/writes by keeping most intermediate results flowing through IMCs in pipelined and parallel fashion. Furthermore, the hardware accelerator may employ task pipelining and pre-fetch pipelining to enhance its performance.

Hardware accelerated reconfigurable processor for accelerating database operations and queries

Accessing data in a column store database based on hardware compatible data structures

Embodiments of the present invention provide a hardware accelerator that assists a host database system in processing its queries. The hardware accelerator comprises special purpose processing elements that are capable of receiving database query/operation tasks in the form of machine code database instructions, execute them in hardware without software, and return the query/operation result back to the host system. For example, table and column descriptors are embedded in the machine code database instructions. For ease of installation, the hardware accelerators employ a standard interconnect, such as a PCIe or HT interconnect. The processing elements implement a novel dataflow design and Inter Macro-Op Communication (IMC) data structures to execute the machine code database instructions. The hardware accelerator may also comprise a relatively large memory to enhance the hardware execution of the query/operation tasks requested. The hardware accelerator utilizes hardware-friendly memory addressing, which allows for arithmetic derivation of a physical address from a global database virtual address simply based on a row identifier. The hardware accelerator minimizes memory reads/writes by keeping most intermediate results flowing through IMCs in pipelined and parallel fashion. Furthermore, the hardware accelerator may employ task pipelining and pre-fetch pipelining to enhance its performance.

Generating a check matrix includes defining a generator function operable to yield check bits associated with a word. A set of primitive elements is calculated from the generator function. A set of check matrix columns is generated, where each check matrix column includes a matrix having a subset of the set of primitive elements. A check matrix is generated from a subset of the set of check matrix columns, where the check matrix yields a syndrome that comprises an error pattern for the word. The check matrix is reported.

Error correction for data communication

Generating a check matrix includes defining a set of column vectors. A matrix operable to have a plurality of entries is initiated. Each entry has a submatrix that includes a function of a subset of the set of column vectors. The following is repeated until a last entry of the matrix is reached. Subsets of the set of column vectors are generated from the set of column vectors, and an entry is generated from each subset. A weight associated with each entry is calculated, and an entry having a minimum weight is selected. The selected entry is added to the matrix, and the subset of column vectors associated with the selected entry is removed from the set of column vectors. The matrix is reported.

Generating a check matrix for error correction

Transmitting data includes receiving a serial sequence of code words. Each code word includes a word and check bits, where the word includes a sequence of word symbols, and the check bits includes a sequence of check bit symbols. The following is repeated until a last word symbol of a last code word is reached: selecting a next code word, and inserting a next word symbol of the selected code word into a vector. The following is repeated until a last check bit symbol of the last code word is reached: selecting a next code word, and inserting a next check bit symbol of the selected code word into the vector. The vector is transmitted.

Transmitting data in a communication network

A memory control unit has been developed. The control unit includes a command &ldquo;spin wheel&rdquo; which schedules the order of read and write commands to the memory. It also includes a read &ldquo;spin wheel&rdquo; which ensures proper timing of the read commands and a write &ldquo;spin wheel&rdquo; which ensures proper timing of the write commands.

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Sunnyvale, CA, United States of America

Liuxi Yang