Abstract: The present disclosure describes an analytical data management system (ADMS) that serves critical dashboards, applications, and internal users. This ADMS has high scalability, and availability through replication and failover, high user query load, and large data volumes. The ADMS provides continuous ingestion and high performance querying with tunable freshness. It further advances the idea of disaggregation by decoupling its architectural components: ingestion, indexing, and querying. As a result, the impact of a slow down in indexing on the query performance is minimized by either trading off data freshness or incurring higher costs.

Abstract: A transformation on raw data is applied to produce transformed data, where the transformation includes at least one selected from among a summary of the raw data or a transform of the raw data between different domains. In response to a query to access data, the query is processed using the transformed data.

Abstract: According to an example, an instruction to run a kernel of an application on an apparatus having a first processing unit integrated with a second processing unit may be received. In addition, an application profile for the application at a runtime of the application kernel on the second processing unit may be created, in which the application profile identifies an affinity of the application kernel to be run on either the first processing unit or the second processing unit, and identifies a characterization of an input data set of the application. The application profile may also be stored in a data store.

Abstract: The communication between integrated graphics processing units (GPUs) is disclosed. A first integrated GPU of a first computing device obtains a tuple pertaining to data to be transmitted to a second integrated GPU of a second computing device. The tuple comprises at least a length of the data. The first integrated GPU allocates a virtual address space to the data based on the length of the data, where the virtual address space has a plurality of virtual addresses. Further, a mapping table of a mapping between the plurality of virtual addresses and a plurality of bus addresses is provided by the first integrated GPU to a communication module of the first computing device to transmit the data, where the plurality of bus addresses indicate physical locations of the data.

Abstract: Examples disclosed herein relate to distributed computing in R. Some examples disclosed herein may include identifying a distributed multivariate apply (dmapply) operation and an invocation of a distributed computing backend and determining a function referenced in the dmapply operation. A distributed backend driver associated with the invoked distributed computing backend may translate the determined function to a function native to an R application programming interface (API) of the invoked distributed computing backend and may provide the translated function to the invoked distributed computing backend to perform the translated function on a distributed data set referenced in the dmapply operation.

Abstract: Examples disclosed herein relate to distributed computing in R. Some examples disclosed herein may include identifying a distributed multivariate apply (dmapply) operation and an invocation of a distributed computing backend and determining a function referenced in the dmapply operation. A distributed backend driver associated with the invoked distributed computing backend may translate the determined function to a function native to an R application programming interface (API) of the invoked distributed computing backend and may provide the translated function to the invoked distributed computing backend to perform the translated function on a distributed data set referenced in the dmapply operation.

Abstract: Method, systems, and computer-readable storage devices for updating a prediction model are described. In one aspect, a statistical analysis group assignment may be received. The statistical analysis group assignment may group partition-level worker node and a first set of partition-level worker nodes as a statistical analysis group. A statistical analysis phase may then be executed where a group-level decision tree is generated from statistical data and other statistical data received from the first set of partition-level worker nodes. A decision tree analysis phase may then be executed, where a step decision tree may be generated based on a selection from the group-level tree and other group-level trees received from other statistical analysis groups. The prediction model may be caused to be updated using the step decision tree.

Abstract: A method, system, and computer-readable storage device for transferring data from a database management system to a data analytics system is described herein. In one aspect, a database management node storing a data partition of a database table that is distributed across a plurality of database management nodes of a database management system receives a data transfer request from a data analytics node of a data analytics node cluster. The data transfer request may include a data transfer policy for splitting the data partition onto the data analytics node cluster. A data split may be identified from the data partition. The data split may be a subset of the data partition. The data split is communicated to the data analytics node according to the data transfer policy.

Abstract: A transformation on raw data is applied to produce transformed data, where the transformation includes at least one selected from among a summary of the raw data or a transform of the raw data between different domains. In response to a query to access data, the query is processed using the transformed data.

Abstract: According to an example, an instruction to run a kernel of an application on an apparatus having a first processing unit integrated with a second processing unit may be received. In addition, an application profile for the application at a runtime of the application kernel on the second processing unit may be created, in which the application profile identifies an affinity of the application kernel to be run on either the first processing unit or the second processing unit, and identifies a characterization of an input data set of the application. The application profile may also be stored in a data store.

Abstract: An embodiment provides a system and method for transaction commitment and replication. The method includes receiving a minitransaction from a client node at one or more memory nodes, wherein each memory node includes a number of replicas. The minitransaction is a type of transaction which atomically executes any combination of reading, comparing, and writing to any of a number of memory locations. The method also includes determining, for a leader of the replicas within a memory node, whether the leader is able to commit the minitransaction and stabilizing state changes of the minitransaction within a transaction log using a consensus procedure to update the replicas. The method further includes committing the minitransaction if, at each memory node, a quorum of the replicas is able to stabilize the minitransaction, or aborting the minitransaction otherwise.

Abstract: The communication between integrated graphics processing units (CPUs) is disclosed. A first integrated GPU of a first computing device obtains a tuple pertaining to data to be transmitted to a second integrated CPU of a second computing device. The tuple comprises at least a length of the data. The first integrated GPU allocates a virtual address space to the data based on the length of the data, where the virtual address space has a plurality of virtual addresses. Further, a mapping table of a mapping between the plurality of virtual addresses and a plurality of bus addresses is provided by the first integrated GPU to a communication module of the first computing device to transmit the data, where the plurality of bus addresses indicate physical locations of the data.

Abstract: Examples for performing processing tasks using an auxiliary processing unit are described. In an example, a computing system may include a processor to perform a plurality of processing tasks for each of a plurality of applications hosted by the computing system. An auxiliary processing task may be determined for an active application from the plurality of applications. The auxiliary processing tasks may be from among the plurality of processing tasks performed for the active application. Further, the processing code corresponding to the auxiliary processing task may be provided to the auxiliary processing unit of the computing system. The auxiliary processing unit may execute the processing code to perform corresponding auxiliary processing tasks and share a processing load with the processor.

Abstract: A sparse array is partitioned into first partitions and a second array is partitioned into second partitions based on an invariant relationship between the sparse array and the second array. The sparse array and the second array are associated with a computation involving the sparse array and the second array. The first partitions and the corresponding second partitions are distributed to workers. A different first partition and corresponding second partition is distributed to each of the workers. Third partitions of the sparse array and corresponding fourth partitions of the second array are determined based on the invariant relationship and measurements of load are received from each of the workers. At least one of the first partitions and the corresponding second partition is different from one of the third partitions and the corresponding fourth partition. The at least one of the first partitions and the corresponding second partition that is different is redistributed among the workers.

Abstract: A method, system, and computer-readable storage device for transferring data from a database management system to a data analytics system is described herein. In one aspect, a database management node storing a data partition of a database table that is distributed across a plurality of database management nodes of a database management system receives a data transfer request from a data analytics node of a data analytics node cluster. The data transfer request may include a data transfer policy for splitting the data partition onto the data analytics node cluster. A data split may be identified from the data partition. The data split may be a subset of the data partition. The data split is communicated to the data analytics node according to the data transfer policy.

Abstract: Log analysis can include transferring compiled log analysis code, executing log analysis code, and performing a log analysis on the executed log analysis code.

Abstract: A technique includes receiving a user input in an array-oriented database. The user input indicates a database operation and processing a plurality of chunks of data stored by the database to perform the operation. The processing in dudes selectively distributing the processing of the plurality of chunks between a first group of at least one central processing unit and a second group of at least one co-processor.

Abstract: A technique includes distributing a plurality of tasks among a plurality of worker nodes to perform a processing operation on an array. Each task is associated with a set of a least one data block of the array, and an order of the tasks is defined by an array-based programming language. Distribution of the tasks includes, for at least one of the worker nodes, selectively reordering the order defined by the array-based programming language to regulate an amount of data transferred to the worker node.

Abstract: A plurality of array partitions are defined for use by a set of tasks of the program run-time. The array partitions can be determined from one or more arrays that are utilized by the program at run-time. Each of the plurality of computing devices are assigned to perform one or more tasks in the set of tasks. By assigning each of the plurality of computing devices to perform one or more tasks, an objective to reduce data transfer amongst the plurality of computing devices can be implemented.

Abstract: A plurality of array partitions are defined for use by a set of tasks of the program run-time. The array partitions can be determined from one or more arrays that are utilized by the program at run-time. Each of the plurality of computing devices are assigned to perform one or more tasks in the set of tasks. By assigning each of the plurality of computing devices to perform one or more tasks, an objective to reduce data transfer amongst the plurality of computing devices can be implemented.