Abstract: A fine-grained scalable time-versioning support for large-scale property graph databases includes receiving a request, wherein the request includes an entity identifier (ID) and reference time information, and searching entries of a first table using the entity ID as a first key. Matching an entry is performed corresponding to the first key in the first table, determining the entry is valid based at least in part on comparing the reference time information of the request with stored time information for the entry, and responsive to the determination, creating a second key, wherein the second key concatenates an ID associated with the entity ID with the reference time information of the request. Searching a second table is performed having entries based at least in part on the second key, and responsive to searching the second table, retrieving data associated with the second key.

Abstract: Various embodiments execute a program with improved cache efficiency. In one embodiment, a first subset of operations of a program is performed on a plurality of objects stored in one or more data structures. The first subset of operations has a regular memory access pattern. After each operation in the first subset of operations has been performed, results of the operation are stored in one of the plurality of queues. Each queue in the plurality of queues is associated with a different cacheable region of a memory. A second subset of operations in the program is performed utilizing at least one queue in the plurality of queues. The second subset of operations utilizes results of the operations in the first subset of operations stored in the queue. The second subset of operations has an irregular memory access pattern that is regularized by localizing memory locations accessed by the second subset of operations to the cacheable region of memory associated with the at least one queue.

Abstract: Techniques for determining connected components of a graph via incremental graph analysis algorithms are provided. In one example, a computer-implemented method comprises analyzing, by a system operatively coupled to a processor, a first differential value representing an initial incremental difference of elements between selected initial elements of an initial vector and selected input elements of an input vector associated with a graph. The method further comprises recurringly analyzing, by the system, a second differential value representing a subsequent incremental difference of elements between selected updated elements of an updated initial vector and selected additional elements of another input vector associated with the graph until the second differential value is zero.

Abstract: Embodiments include techniques for fine-grained scalable time-versioning support for large-scale property graph databases comprising receiving a request, wherein the request includes an entity identifier (ID) and reference time information, and searching entries of a first table using the entity ID as a first key. Techniques also include matching an entry corresponding to the first key in the first table, determining the entry is valid based at least in part on comparing the reference time information of the request with stored time information for the entry, and responsive to the determination, creating a second key, wherein the second key concatenates an ID associated with the entity ID with the reference time information of the request. Techniques also include searching a second table having one or more entries based at least in part on the second key, and responsive to searching the second table, retrieving data associated with the second key.

Abstract: Techniques for determining connected components of a graph via incremental graph analysis algorithms are provided. In one example, a computer-implemented method comprises analyzing, by a system operatively coupled to a processor, a first differential value representing an initial incremental difference of elements between selected initial elements of an initial vector and selected input elements of an input vector associated with a graph. The method further comprises recurringly analyzing, by the system, a second differential value representing a subsequent incremental difference of elements between selected updated elements of an updated initial vector and selected additional elements of another input vector associated with the graph until the second differential value is zero.

Abstract: Various embodiments execute a program with improved cache efficiency. In one embodiment, a first subset of operations of a program is performed on a plurality of objects stored in one or more data structures. The first subset of operations has a regular memory access pattern. After each operation in the first subset of operations has been performed, results of the operation are stored in one of the plurality of queues. Each queue in the plurality of queues is associated with a different cacheable region of a memory. A second subset of operations in the program is performed utilizing at least one queue in the plurality of queues. The second subset of operations utilizes results of the operations in the first subset of operations stored in the queue. The second subset of operations has an irregular memory access pattern that is regularized by localizing memory locations accessed by the second subset of operations to the cacheable region of memory associated with the at least one queue.

Abstract: A graph processing system includes a graph API (Application Programming Interface), as executed by a processor on a computer, and that includes a plurality of graph operators to create graphs and to execute graph analytic applications on the created graphs, the graph operators supporting a creation and manipulation of multi-dimensional properties of graphs. A run-time system is executed by the processor and implements routines that dynamically adjust a plurality of representations and algorithms to execute sequences of operations on graph data. A library is accessible to the run-time system and stores a specification of calling signatures for the graph operators such that the graph operators can be called from any of various computer programming languages such that top-level algorithms received in an input graph application can be understood in the graph processing system when received in any of the various computer programming languages.

Abstract: A graph processing system includes a graph API (Application Programming Interface), as executed by a processor on a computer, and that includes a plurality of graph operators to create graphs and to execute graph analytic applications on the created graphs, the graph operators supporting a creation and manipulation of multi-dimensional properties of graphs. A run-time system is executed by the processor and implements routines that dynamically adjust a plurality of representations and algorithms to execute sequences of operations on graph data. A library is accessible to the run-time system and stores a specification of calling signatures for the graph operators such that the graph operators can be called from any of various computer programming languages such that top-level algorithms received in an input graph application can be understood in the graph processing system when received in any of the various computer programming languages.

Abstract: A graph processing system includes a graph API (Application Program Interface), as executed on a processor of a computer and as capable of implementing any of a plurality of graph operators to express computations of input graph analytics applications. A run-time system, executed by the processor, implements graph operators specified by each graph API function and deploys the implemented graph operators to a selected computing system. A library contains multiple implementations for each graph API function, each implementation predetermined as being optimal for a specific set of conditions met by a graph being processed, for functional capabilities of a specific computing system on which the graph is being processed, and for resources available on that specific computing system.

Abstract: A graph processing system includes a graph API (Application Programming Interface), as executed by a processor on a computer, and that includes a plurality of graph operators to create graphs and to execute graph analytic applications on the created graphs, the graph operators supporting a creation and manipulation of multi-dimensional properties of graphs. A run-time system is executed by the processor and implements routines that dynamically adjust a plurality of representations and algorithms to execute sequences of operations on graph data. A library is accessible to the run-time system and stores a specification of calling signatures for the graph operators such that the graph operators can be called from any of various computer programming languages such that top-level algorithms received in an input graph application can be understood in the graph processing system when received in any of the various computer programming languages.

Abstract: A graph processing system includes a graph API (Application Program Interface), as executed on a processor of a computer and as capable of implementing any of a plurality of graph operators to express computations of input graph analytics applications. A run-time system, executed by the processor, implements graph operators specified by each graph API function and deploys the implemented graph operators to a selected computing system. A library contains multiple implementations for each graph API function, each implementation predetermined as being optimal for a specific set of conditions met by a graph being processed, for functional capabilities of a specific computing system on which the graph is being processed, and for resources available on that specific computing system.

Abstract: A graph processing system includes a graph API (Application Programming Interface), as executed by a processor on a computer, and that includes a plurality of graph operators to create graphs and to execute graph analytic applications on the created graphs, the graph operators supporting a creation and manipulation of multi-dimensional properties of graphs. A run-time system is executed by the processor and implements routines that dynamically adjust a plurality of representations and algorithms to execute sequences of operations on graph data. A library is accessible to the run-time system and stores a specification of calling signatures for the graph operators such that the graph operators can be called from any of various computer programming languages such that top-level algorithms received in an input graph application can be understood in the graph processing system when received in any of the various computer programming languages.

Abstract: A graph processing system includes a graph API (Application Program Interface), as executed on a processor of a computer and as capable of implementing any of a plurality of graph operators to express computations of input graph analytics applications. A run-time system, executed by the processor, implements graph operators specified by each graph API function and deploys the implemented graph operators to a selected computing system. A library contains multiple implementations for each graph API function, each implementation predetermined as being optimal for a specific set of conditions met by a graph being processed, for functional capabilities of a specific computing system on which the graph is being processed, and for resources available on that specific computing system.