Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring a second problem matrix of a second problem to a first problem matrix of a first problem as paired matrices and shifting the second problem matrix by N+1 and combining the first problem matrix and the mirrored second problem matrix into one matrix of (N+1)×N, where the first problem shared memory comprises regular intervals, where the second problem shared memory is continuous, and where the GPU performs batched dense Cholesky decomposition with the one matrix from the combining to accelerate the Cholesky decomposition.

Abstract: Mechanisms, in a system comprising a host system and at least one accelerator device, for performing a concept analysis operation are provided. The host system extracts a set of one or more concepts from an information source and provides the set of one or more concepts to the accelerator device. The host system also provides at least one matrix representation data structure representing a graph of concepts and relationships between concepts in a corpus. The accelerator device executes the concept analysis operation internal to the accelerator device to generate an output vector identifying concepts in the corpus, identified in the at least one matrix representation data structure, related to the set of one or more concepts extracted from the information source. The accelerator device outputs the output vector to the host system which utilizes the output vector to respond to a request submitted to the host system associated with the information source.

Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring a second problem matrix of a second problem to a first problem matrix of a first problem as paired matrices and shifting the second problem matrix by N+1 and combining the first problem matrix and the mirrored second problem matrix into one matrix of (N+1)×N by merging the first problem matrix and the mirrored second problem matrix. The first problem matrix and the second problem matrix are symmetric and positive definite matrices.

Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring a second problem matrix of a second problem to a first problem matrix of a first problem as paired matrices and shifting the second problem by N+1, combining the first problem matrix and the mirrored second problem matrix into one matrix of (N+1)×N, and reading the fixed size data length of the one square matrix with a fixed data interval for both the first problem and the second problem.

Abstract: Mechanisms, in a system comprising a host system and at least one accelerator device, for performing a concept analysis operation are provided. The host system extracts a set of one or more concepts from an information source and provides the set of one or more concepts to the accelerator device. The host system also provides at least one matrix representation data structure representing a graph of concepts and relationships between concepts in a corpus. The accelerator device executes the concept analysis operation internal to the accelerator device to generate an output vector identifying concepts in the corpus, identified in the at least one matrix representation data structure, related to the set of one or more concepts extracted from the information source. The accelerator device outputs the output vector to the host system which utilizes the output vector to respond to a request submitted to the host system associated with the information source.

Abstract: Mechanisms are provided for performing a matrix operation. A processor of a data processing system is configured to perform cluster-based matrix reordering of an input matrix. An input matrix, which comprises nodes associated with elements of the matrix, is received. The nodes are clustered into clusters based on numbers of connections with other nodes within and between the clusters, and the clusters are ordered by minimizing a total length of cross cluster connections between nodes of the clusters, to thereby generate a reordered matrix. A lookup table is generated identifying new locations of nodes of the input matrix, in the reordered matrix. A matrix operation is then performed based on the reordered matrix and the lookup table.

Abstract: An iterative graph algorithm accelerating method, system, and computer program product, include recording an order of access nodes in a memory layout, reordering the access nodes in the memory layout in accordance with the recorded order, and updating edge information of the reordered access nodes.

Abstract: An iterative graph algorithm accelerating method, system, and computer program product, include recording an order of access nodes in a memory layout, reordering the access nodes in the memory layout in accordance with the recorded order, and updating edge information of the reordered access nodes.

Abstract: A first quicksort is performed in parallel across pairs of partitions of a dataset assigned to respective ones of available processors, including swapping elements of a first partition of a given one of the pairs that are larger than a pivot with elements of a second partition of the given pair that are smaller than the pivot. A second quicksort is performed in parallel across those partitions having elements left unsorted by the first quicksort, and first misplaced elements from a first side of the dataset corresponding to the first partition are swapped with second misplaced elements from a second side of the dataset corresponding to the second partition to produce a first dataset having elements equal to or lower than the pivot and a second dataset having elements equal to or higher than the pivot.

Abstract: Methods and systems for sorting a dataset include partitioning the dataset into 2npartitions, where n is a number of available processors. A first quicksort is performed in parallel across pairs of partitions based on a pivot using a plurality of processors. A second quicksort is performed in parallel on unsorted elements within each partition based on the pivot, where the unsorted elements were left unsorted by the first quicksort. Misplaced elements from a left side of the dataset are swapped with misplaced elements from a right side of the dataset to produce a left dataset that has elements equal to or lower than the pivot and a right dataset that has elements equal to or higher than the pivot.

Abstract: An iterative graph algorithm accelerating method, system, and computer program product, include recording an order of access nodes in a memory layout, reordering the access nodes in the memory layout in accordance with the recorded order, and updating edge information of the reordered access nodes.

Abstract: Techniques for transforming input operands to reduce overhead for implementing addition operations in hardware are provided. In one aspect, a method for transforming input operands of an adder includes the steps of: receiving a bit array of the input operands; replacing a duplicate signal (e.g., a signal that occurs twice) for a given bit k in the bit array with a single signal at bit k+1; reducing a number of occurrences of the signal on adjacent bits of the input operand, wherein by way of the replacing and reducing a transformed bit array is formed; and providing the transformed bit array to the adder.

Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring a second problem matrix of a second problem to a first problem matrix of a first problem as paired matrices and shifting the second problem by N+1, combining the first problem matrix and the mirrored second problem matrix into one matrix of (N+1)×N, and reading the fixed size data length of the one square matrix with a fixed data interval for both the first problem and the second problem.

Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU) including at least a first problem and a second problem, include mirroring a second problem matrix of the second problem to a first problem matrix of the first problem, combining the first problem matrix and the mirrored second problem matrix into a single problem matrix, and allocating data read to a thread and to the first problem and the second problem, respectively.

Abstract: An information processing system, computer readable storage medium, and method for accelerated radix sort processing of data elements in an array in memory. The information processing system stores an array of data elements in a buffer memory in an application specific integrated circuit radix sort accelerator. The array has a head end and a tail end. The system radix sort processing, with a head processor, data elements starting at the head end of the array and progressively advancing radix sort processing data elements toward the tail end of the array. The system radix sort processing, with a tail processor, data elements starting at the tail end of the array and progressively advancing radix sort processing data elements toward the head end of the array, the tail processor radix sort processing data elements in the array contemporaneously with the head processor radix sort processing data elements in the array.

Abstract: Apparatuses and Methods for sorting a data set. A data storage is divided into a plurality of buckets that is each associated with a respective key value. A plurality of stripes is identified in each bucket. A plurality of data stripe sets is defined that has one stripe within each respective bucket. A first and a second in-place partial bucket radix sort are performed on data items contained within the first and second data stripe sets, respectively, using an initial radix. Incorrectly sorted data items in the first bucket are grouped by a first processor and incorrectly sorted data items in the second bucket are grouped by a second processor into a respective incorrect data item group within each bucket. A radix sort is then performed using the initial radix on the items within the respective incorrect data item group. A first level sorted output is produced.

Abstract: An information processing system, computer readable storage medium, and method for accelerated radix sort processing of data elements in an array in memory. The information processing system stores an array of data elements in a buffer memory in an application specific integrated circuit radix sort accelerator. The array has a head end and a tail end. The system radix sort processing, with a head processor, data elements starting at the head end of the array and progressively advancing radix sort processing data elements toward the tail end of the array. The system radix sort processing, with a tail processor, data elements starting at the tail end of the array and progressively advancing radix sort processing data elements toward the head end of the array, the tail processor radix sort processing data elements in the array contemporaneously with the head processor radix sort processing data elements in the array.

Abstract: Systems and methods for sorting a data set stored on an external device. A plurality of smaller radix sizes are determined, based on a first radix size and performance characteristics of an external data storage device, whose sizes add up to a first radix size for an in-place radix sort. The smaller radix sizes reduce a total time to perform the in-place radix sort. Each level of a multiple level in-place radix sort is performed with the smaller radix sizes. Each level of the sort includes dividing the data set into N buckets; dividing the buffer into N buckets; and iteratively loading a respective segment in each bucket of the data set into a respective bucket of the buffer, performing an in-place radix sort on the data in the buffer, and returning sorted buffer data to the data set on the external storage device.

Abstract: An information processing system, computer readable storage medium, and method for accelerated radix sort processing of data elements in an array in memory. The information processing system stores an array of data elements in a buffer memory in an application specific integrated circuit radix sort accelerator. The array has a head end and a tail end. The system radix sort processing, with a head processor, data elements starting at the head end of the array and progressively advancing radix sort processing data elements toward the tail end of the array. The system radix sort processing, with a tail processor, data elements starting at the tail end of the array and progressively advancing radix sort processing data elements toward the head end of the array, the tail processor radix sort processing data elements in the array contemporaneously with the head processor radix sort processing data elements in the array.

Abstract: Methods for sorting a data set. A data storage is divided into a plurality of buckets that is each associated with a respective key value. A plurality of stripes is identified in each bucket. At least one data stripe set is defined that has one stripe within each respective bucket. An in-place partial bucket radix sort is performed on data items contained within one data stripe set with a first processor using an initial radix. Incorrectly sorted data items are then grouped in each bucket into a respective incorrect data item group within each bucket. A radix sort is then performed using the initial radix on the items within the respective incorrect data item group. A first level sorted output is produced.