Abstract: The present invention discloses a method for executing an SQL operator on compressed data chunk. The method comprising the step of: receiving SQL operator, accessing compressed data chunk blocks, receive e full set of derivatives of the compression scheme, check compression rules based on the compression scheme and relevant operator for approving SQL operation on compressed data and in case of approval applying respective SQL operator on relevant compressed data chunks.
Abstract: According to some embodiments is disclosed a method for controlling and scheduling operation of at least one SQL operator on data chunk. The method comprising the step of: receiving SQL query, accessing data chunk blocks, receive meta data statistics and SQL query, analyzing the query selectivity, result size and Frequency moments calculation during the query execution run-time and choosing the right device to execute the each operator of the query according to analysis and predict results size.
Abstract: The present invention provides a method for compressing genome sequences readers using GPU processing unit. The method comprising the steps of: identifying position of each given genome reader characters string in the sequence of a reference genome, determining alignment of each reader string within the reference genome, comparing each reader characters string to corresponding reference genome sequence based on determined alignment, filtering characters in each reader by GPU processor by eliminating similar characters and extracting only characters differences in association to their position in the genome sequence and recording filtered data of each reader in association to its alignment in genome reference at the genome compressed database.
Type:
Grant
Filed:
February 2, 2016
Date of Patent:
May 5, 2020
Assignee:
SQREAM TECHNOLOGIES LTD
Inventors:
Dotan Shdema, Raziel Shoshani, Or Cohen, Ori Netzer
Abstract: The present invention discloses a method for real time execution of SQL queries on data stream using HWA units. The method comprising the step of: receiving data stream and injecting directly in to the one more HWA units, receiving SQL query and identifying SQL query type, statistically real time analyzing multiple data streams and calculating statistics coefficients and characteristics of data stream, creating metadata based on statistical analysis in case the calculated statistics coefficients and characteristics obey predefined rules and using created metadata for SQL execution based on SQL identified SQL type in case the calculated statistics coefficients and characteristics obey predefined rules. The steps of statistical data analysis and creation of meta data are performed by the HWA.
Abstract: The invention relates to a system for parallel execution of database queries over one or more Central Processing Units (CPUs), and one or more Multi Core Processor, (MCPs), the system comprises (a) a query analyzer for dividing the query to plurality of sub-queries, and for computing and assigning to each sub-query a target address of either a CPU of an MCP; (b) a query compiler for creating an Abstract Syntax Tree (AST) and OpenCL primitives only for those sub-queries that are targeted to an MCP, and for conveying both the remaining sub-queries, and the AST and the OpenCL code to a virtual machine, and (A) a Virtual Machine (VM) which comprises: a task bank, a buffers; a scheduler. The virtual machine combines said sub-query results by the CPUs and said primitive results by said MCPs to a final query result.
Abstract: The invention relates to a system for parallel execution of database queries over one or more Central Processing Units (CPUs), and one or more Multi Core Processor, (MCPs), the system comprises (a) a query analyzer for dividing the query to plurality of sub- queries, and for computing and assigning to each sub-query a target address of either a CPU of an MCP; (b) a query compiler for creating an Abstract Syntax Tree (AST) and OpenCL primitives only for those sub-queries that are targeted to an MCP, and for conveying both the remaining sub-queries, and the AST and the OpenCL code to a virtual machine, and (A) a Virtual Machine (VM) which comprises: a task bank, a buffers; a scheduler. The virtual machine combines said sub-query results by the CPUs and said primitive results by said MCPs to a final query result.
Abstract: The present invention discloses a method for optimizing the throughput of hardware accelerators (HWAs) in a computerized abstraction system, by utilizing the maximal data input bandwidth to the said HWAs. The method is comprised of the following steps: dynamically obtaining the quantities and properties of HWAs and storage units within the computerized abstraction system dynamically allocating cache memory space per each of the HWAs, according to the said obtained quantities and properties, to minimize the time required for reading data from storage instances to the said HWA dynamically allocating spoolers per each of the HWAs, according to the said obtained quantities and properties, to buffer the input data and ensure a continuous flow of input data, in the target HWA's maximal input bandwidth.
Type:
Grant
Filed:
December 20, 2016
Date of Patent:
January 29, 2019
Assignee:
SQREAM TECHNOLOGIES LTD.
Inventors:
Ori Brostovsky, Omid Vahdaty, Eli Klatis, Tal Zelig, Jake Wheat, Razi Shoshani
Abstract: A method for pre-processing and processing query operation on multiple data chunk on vector enabled architecture. The method includes receiving a user query having at least one a data item, accessing data chunk blocks having an enhanced data structure representation. The enhanced data structure representation includes data recursive presentation of data chunk boundaries and bloom filter bitmask of data chunks. The method further includes searching simultaneously at multiple data chunk blocks utilizing the recursive presentation of data chunk boundaries using a HardWare Accelerator (HWA), identifying data item address by comparing a calculated Bloom filter bitmask of the requested data item to a calculated bitmask of the respective data chunks simultaneously by using multiple HWAs, and executing query on respective data chunks.
Abstract: A method for applying bloom filter on a large data set consisting of key-value pairs, using at least one processor includes: partitioning large data-set of key-value pairs into data chunks; determining Bloom filter Vector and number of segments in the vector for each data chunk; Encoding all keys of a given Chunk into a Bloom filter vector; Determining the segment-id of a given key using H (0) hash function; Encoding Key into a Bloom filter segment with the determined segment-id, using a K-bit array produced by H1, . . . Hk functions; and Packing of segments into extent data structures where each extent includes segments of different chunks, but with the same segment-id wherein a single extent filters multiple chunks, depending on a packing factor (the number of segments packed into a single extent).