Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring matrices to form paired matrices solving the paired matrices simultaneously.

Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring matrices to form paired matrices solving the paired matrices simultaneously.

Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring matrices to form paired matrices solving the paired matrices simultaneously.

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: 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 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 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: 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: 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: 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 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: 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 efficient method for partitioning, for example, FPGA devices is described which optimizes the number of devices required to implement a design. The method involves generating a hierarchical graph of a feasible bipartition of the cells of the design. Feasible pairs are merged, followed by flattening of the hierarchical graph. The number of I/O pins of the new partition is then reduced, upon which a hierarchical graph is derived. A perturbed partition is then generated, followed by restoration of feasibility.

Abstract: An efficient method for partitioning, for example, FPGA devices is described which optimizes the number of devices required to implement a design. The method involves generating a hierarchical graph of a feasible bipartition of the cells of the design. Feasible pairs are merged, followed by flattening of the hierarchical graph. The number of I/O pins of the new partition is then reduced, upon which a hierarchical graph is derived. A perturbed partition is then generated, followed by restoration of feasibility.