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Tridiagonalization of a dense symmetric matrix on multiple GPUs and its application to symmetric eigenvalue problems
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Outgoing claims: the papers this work cites, builds on, or argues with.
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A proposal for a set of parallel basic linear algebra subprograms
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A Communication-Avoiding Thick-Restart Lanczos Method on a Distributed-Memory System
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Optimizing Memory-Bound SYMV Kernel on GPU Hardware Accelerators
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A High Performance SYMV Kernel on a Fermi-core GPU
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A divide and conquer method for the symmetric tridiagonal eigenproblem
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ABINIT: First-principles approach to material and nanosystem properties
inferredcites →https://doi.org/10.1016/j.jocs.2011.05.002openalex-snapshot
Developing algorithms and software for the parallel solution of the symmetric eigenvalue problem
inferredcites →https://doi.org/10.1016/j.laa.2003.12.028openalex-snapshot
Multiple representations to compute orthogonal eigenvectors of symmetric tridiagonal matrices
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Towards dense linear algebra for hybrid GPU accelerated manycore systems
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Accelerating the reduction to upper Hessenberg, tridiagonal, and bidiagonal forms through hybrid GPU-based computing
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Parallel solution of partial symmetric eigenvalue problems from electronic structure calculations
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One-sided Dense Matrix Factorizations on a Multicore with Multiple GPU Accelerators*
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Using PLAPACK: Parallel linear algebra package
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Electronic structure basic theory and practical methods
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Solving Schrödinger’s equation around a desired energy: Application to silicon quantum dots
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Computational challenges of large-scale, long-time, first-principles molecular dynamics
inferredcites →https://doi.org/10.1109/ipdpsw.2013.265openalex-snapshot
Tridiagonalization of a Symmetric Dense Matrix on a GPU Cluster
inferredcites →https://doi.org/10.1109/mcse.2011.83openalex-snapshot
Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community
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Divide and Conquer on Hybrid GPU-Accelerated Multicore Systems
inferredcites →https://doi.org/10.1137/s0895479898334605openalex-snapshot
Thick-Restart Lanczos Method for Large Symmetric Eigenvalue Problems
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Adaptive Projection Subspace Dimension for the Thick-Restart Lanczos Method
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Optimizing symmetric dense matrix-vector multiplication on GPUs
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Basic Linear Algebra Subprograms for Fortran Usage
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An Improved Magma Gemm For Fermi Graphics Processing Units
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Conjugate-gradient eigenvalue solvers in computing electronic properties of nanostructure architectures
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nu-TRLan User Guide Version 1.0: A High-Performance Software Package for Large-Scale Harmitian Eigenvalue Problems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Accelerating Numerical Dense Linear Algebra Calculations with GPUs
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High Performance Pseudo-analytical Simulation of Multi-Object Adaptive Optics over Multi-GPU Systems
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Leading Edge Hybrid Multi-GPU Algorithms for Generalized Eigenproblems in Electronic Structure Calculations
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Eigen-G: GPU-Based Eigenvalue Solver for Real-Symmetric Dense Matrices
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On the effect of using rCUDA to provide CUDA acceleration to Xen virtual machines
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Optimization of Generalized Eigensolver for Dense Symmetric Matrices on AMD GPU
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On the support of inter-node P2P GPU memory copies in rCUDA
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On Accelerating Concurrent PCA Computations for Financial Risk Applications
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Extending rCUDA with Support for P2P Memory Copies between Remote GPUs
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On Implementing Sparse Matrix Multi-vector Multiplication on GPUs
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MAGMA embedded: Towards a dense linear algebra library for energy efficient extreme computing
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A Comparative Performance Analysis of Remote GPU Virtualization over Three Generations of GPUs
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Tridiagonalization of a Symmetric Dense Matrix on a GPU Cluster
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Reducing the performance gap of remote GPU virtualization with InfiniBand Connect-IB
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Analysis and Design Techniques towards High-Performance and Energy-Efficient Dense Linear Solvers on GPUs
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Toward a scalable multi-GPU eigensolver via compute-intensive kernels and efficient communication
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Proceedings of the 16th Annual Middleware Conference
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