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Showing 1 to 4 of 4 for “"sparse matrix computations"”.
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Designing Hardware Accelerators for Solving Sparse Linear Systems
Solving sparse linear systems is a key primitive that sits at the heart of many important numeric algorithms. Because of this primitive’s importance, algorithm designers have spent many decades optimizing linear solvers for high performance hardware. However, despite their efforts, existing …
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A Reconfigurable, Distributed-Memory Accelerator for Sparse Applications
Iterative sparse matrix computations lie at the heart of many scientific computing and graph analytics algorithms. On conventional systems, their irregular memory accesses and low arithmetic intensity create challenging memory bandwidth bottlenecks. To overcome such bottlenecks, distributed-SRAM …
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An Object-Oriented Algorithmic Laboratory for Ordering Sparse Matrices
… known NP-hard problems that have applications in sparse matrix computations: the envelope/wavefront reduction problem and the fill reduction problem. Envelope/wavefront reducing orderings have a wide range of applications including profile and frontal solvers, incomplete factorization …
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Hypergraph-Based Combinatorial Optimization of Matrix-Vector Multiplication
… thesis, we will describe our work on optimizing matrix-vector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific computing, both involving matrix-vector multiplication, and both are solved using hypergraph models. For both …