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: 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.

Abstract: Problems are resolved in a business process that includes application programs that run on an Information Technology (IT) infrastructure having IT components. A symptom is generated that identifies a problem in the business process. Selected application programs and/or IT components in the IT infrastructure that may cause the problem in the business process are identified, based on the symptom. A respective situation for a respective selected application program and/or IT component is obtained. The respective situation is one of a set of component-independent predefined situation categories that is associated with a respective selected application program and/or IT component. The respective situations provide the status of the selected application programs and/or IT components in a common situation format that includes the associated one of the component-independent predefined situation categories.

Abstract: Problems are resolved in a business process that includes application programs that run on an Information Technology (IT) infrastructure having IT components. A symptom is generated that identifies a problem in the business process. Selected application programs and/or IT components in the IT infrastructure that may cause the problem in the business process are identified, based on the symptom. A respective situation for a respective selected application program and/or IT component is obtained. The respective situation is one of a set of component-independent predefined situation categories that is associated with a respective selected application program and/or IT component. The respective situations provide the status of the selected application programs and/or IT components in a common situation format that includes the associated one of the component-independent predefined situation categories.

Abstract: A computer system progressively stores and transmits compressed clustered multi-resolution polygonal models. The computer uses a data structure that represents a clustered multi-resolution polygonal model in n-dimensional space. The data structure has a connectivity record which encodes the connectivity information of the highest level of detail. The data structure also has a clustering record which encodes how the vertices of each level of detal are clustered to obtain the vertices of the next lower level of detail. The clustering record is organized in decreasing order of level of detail. The data structure also has a data record with information describing the vertex positions of the levels of detail, and optionally the corresponding properties. The fields of the data record are organized in increasing order of level of detail.