A system for streamlined analysis of access sub-networks in a cloud environment is disclosed. The system comprises memory storing access sub-networks in a cloud environment between a plurality of resources and a plurality of users, memory storing user-to-role mappings for roles assigned to the plurality of users, and accumulation logic having access to the access sub-networks and to the user-to-role mappings. The accumulation logic is configured to traverse the access sub-networks to build a number U user-to-resource mappings between the plurality of users and the plurality of resources, and evaluate the U user-to-resource mappings against the user-to-role mappings to accumulate a number R role-to-resource mappings between the roles and the plurality of resources.

Described herein is a graphics processing unit (GPU) configured to receive an instruction having multiple operands, where the instruction is a single instruction multiple data (SIMD) instruction configured to use a bfloat16 (BF16) number format and the BF16 number format is a sixteen-bit floating point format having an eight-bit exponent. The GPU can process the instruction using the multiple operands, where to process the instruction includes to perform a multiply operation, perform an addition to a result of the multiply operation, and apply a rectified linear unit function to a result of the addition.

A technique relates to flash-optimized data layout of a dataset for queries. Selection columns are stored in flash memory according to a selection optimized layout, where the selection optimized layout is configured to optimize predicate matching and data skipping. The selection optimized layout, for each selection column, is formed by storing a selection column dictionary filled with unique data values in a given selection column, where the unique data values are stored in sorted order in the selection column dictionary. Row position designations are stored corresponding to each row position that the unique data values are present within the given selection column, without duplicating storage of any of the unique data values that occur more than once in the given selection column.

Embodiments of the present disclosure provide a system for accelerated data search of a database storage system. The system includes a host device including a database storage engine; and a memory system including a controller and a memory device, which includes a plurality of pages storing multiple records. The controller includes a page processing accelerator configured to: read, from the plurality of pages, multiple pages in response to a filtered read command; filter particular pages among the multiple pages based on a column full search condition, the filtered pages including entries satisfying the column full search condition; and transfer, to the host device, information regarding the filtered pages.

An analytics processing system generates analytics from a collection of unstructured data by. transforming a received source of input data from an unstructured database into a delimiterless form, and iteratively moving portions of the delimiterlesss input data from a solid-state memory to a shared memory adapted for parallel operations with a plurality of GPU cores. The method stores computational data, such as values for matching, in a high speed memory responsive to operations with the shared memory, in which the high-speed memory remains static for the duration of the iterations. A host CPU invokes the plurality of cores for performing the parallel operations on the computational data and the portions of the delimiterless input data, and stores a result in a general memory accessible from a graphical user interface (GUI). The GPU cores parallelize the matching task of the input data from the unstructured database against the match data.

Described herein is a graphics processing unit (GPU) comprising a first processing cluster to perform parallel processing operations, the parallel processing operations including a ray tracing operation and a matrix multiply operation; and a second processing cluster coupled to the first processing cluster, wherein the first processing cluster includes a floating-point unit to perform floating point operations, the floating-point unit is configured to process an instruction using a bfloat16 (BF16) format with a multiplier to multiply second and third source operands while an accumulator adds a first source operand with output from the multiplier.

A computer-implemented method includes generating a query. The query is directed to a database server. The database server allocating a predetermined quantity of computing resources to the query. The method includes identifying a speed threshold. The method includes determining a quantity of running resources. The quantity of running resources is effective for a running speed for the query to exceed the speed threshold. The method is responsive to the quantity of running resources being greater than the predetermined quantity of computing resources. The method includes including an option with the query. The option is for additional computing resources to be allocated to the query. The method is responsive to input selecting the option, by transmitting the query and the additional computing resources to the database server.

Scaling of query processing resources for efficient utilization and performance is implemented for a database service. A query is received via a network endpoint associated with a database managed by a database service. Respective response times predicted for the query using different query processing configurations available to perform the query are determined. Those query processing configurations with response times that exceed a variability threshold determined for the query may be excluded. A remaining query processing configuration may then be selected to perform the query.

A system, apparatus, device, or method to output different iterations of data entities. The method may include establishing a first data entity; establishing a first state for the first data entity. The method may include establishing a second state for the first data entity. The method may include storing the first data entity, the first state, and the second state at a storage device. The method may include retrieving a first iteration of the first data entity exhibiting at least a portion of the first state. The method may include retrieving a second iteration of the first data entity exhibiting at least a portion of the second state. The method may include outputting the first iteration and the second iteration at an output time.

In dynamic data access, a request is received to access data of a core data service view of an in-memory database. It is determined that an aging temperature parameter is specified in an annotation in a core data service view definition. An aging temperature value corresponding to the aging temperature parameter is received as a range restriction. A default access behavior associated with the core data service view definition is overridden. A partition where the aging temperature value lies in a secondary memory is determined. Latest or recent partition in the secondary memory is referred to as a latest partition. Data from the latest partition until the determined partition is accessed in the secondary memory. The accessed data is loaded from the secondary memory to the main memory.