Suchergebnisse - supernodal factorization algorithm

A Parallel Direct Finite Element Solver Empowered by Machine Learning for Solving Large-Scale Electromagnetic Problems von Wang, Junjie, Zuo, Sheng, Zhao, Xunwang, Tian, Min, Lin, Zhongchao, Zhang, Yu

“… Our proposed solver features adaptive fill-reducing ordering and highly assembled lazy offloading (HALO) factorization …”
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A Supernodal Cholesky Factorization Algorithm for Shared-Memory Multiprocessors von Ng, Esmond, Peyton, Barry W.

“… The new algorithm is a straightforward parallelization of the left-looking supernodal sparse Cholesky factorization algorithm …”
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On Parallel Sparse LU Factorization: Supernodal Left-Looking Multi-Stage Algorithm von Karasenko, Ivan I., Gumalevskii, Roman V., Rakitskiy, Anton A.

“… Such methods are based on matrix decomposition into the product of triangular factors. Computation of factors (factorization …”
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Application of supernodal sparse factorization and inversion to the estimation of (co)variance components by residual maximum likelihood von Masuda, Y, Baba, T, Suzuki, M

“… ), which are often required in residual maximum likelihood (REML). Supernodal left‐looking and inverse multifrontal algorithms were employed for sparse factorization and inversion, respectively …”
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Accelerating sparse Cholesky factorization on GPUs von Rennich, Steven C., Stosic, Darko, Davis, Timothy A.

“… •Sparse direct factorization is a critical component of scientific computing•GPU acceleration of it is difficult due to algorithmic irregularity and PCIe perf …”
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FSCHOL: An OpenCL-based HPC Framework for Accelerating Sparse Cholesky Factorization on FPGAs von Tavakoli, Erfan Bank, Riera, Michael, Quraishi, Masudul Hassan, Ren, Fengbo

“… The proposed FSCHOL framework consists of an FPGA kernel implementing a throughput-optimized hardware architecture for accelerating the supernodal multifrontal algorithm for sparse Cholesky …”
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On the Efficiency of Supernodal Factorization in Interior-Point Method Using CPU-GPU Collaboration von Shah, Usman Ali, Yousaf, Suhail, Ahmad, Iftikhar, Ahmad, Muhammad Ovais

“… Despite its efficiency, PDIPM remains a compute-intensive algorithm. Fortunately, graphics processing units (GPUs …”
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Supernodal sparse Cholesky factorization on graphics processing units von Zou, Dan, Dou, Yong, Guo, Song, Li, Rongchun, Deng, Lin

“… SUMMARY Sparse Cholesky factorization is the most computationally intensive component in solving large sparse linear systems and is the core algorithm of numerous scientific computing applications …”
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A new hybrid GPU-CPU sparse LDLT factorization algorithm with GPU and CPU factorizing concurrently von Liu, Yunmou, Du, Hui, Li, Zhuogen, Chen, Pu

“… This paper proposes a new task assignment scheme for sparse LDLT factorization on a hybrid GPU-CPU platform, and correspondingly an efficient supernodal algorithm, GPU Node and CPU Pipelining (GNCP …”
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Supernodal symbolic Cholesky factorization on a local-memory multiprocessor von Ng, Esmond

“… By exploiting the supernodal structure in the Cholesky factor, the performance of a previous parallel symbolic factorization algorithm is improved …”
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S4 : A free electromagnetic solver for layered periodic structures von Liu, Victor, Fan, Shanhui

“… We describe S4, a free implementation of the Fourier modal method (FMM), which has also been commonly referred to as rigorous coupled wave analysis (RCWA), for …”
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Accelerating Sparse LU Factorization with Density-Aware Adaptive Matrix Multiplication for Circuit Simulation von Wang, Tengcheng, Li, Wenhao, Pei, Haojie, Sun, Yuying, Jin, Zhou, Liu, Weifeng

“… Sparse LU factorization is considered to be one of the most time-consuming components in circuit simulation, particularly when dealing with circuits of considerable size in the advanced process era …”
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A Supernodal Approach to Sparse Partial Pivoting von Demmel, James W., Eisenstat, Stanley C., Gilbert, John R., Li, Xiaoye S., Liu, Joseph W. H.

“… We investigate several ways to improve the performance of sparse LU factorization with partial pivoting, as used to solve unsymmetric linear systems …”
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IMF: An Incomplete Multifrontal $LU$-Factorization for Element-Structured Sparse Linear Systems von Vannieuwenhoven, Nick, Meerbergen, Karl

“… We propose an incomplete multifrontal $LU$-factorization (IMF) preconditioner that extends supernodal multifrontal methods to incomplete factorizations …”
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A Multithreaded Algorithm for Sparse Cholesky Factorization on Hybrid Multicore Architectures von Tang, Meng, Gadou, Mohamed, Ranka, Sanjay

“… We present a multithreaded method for supernodal sparse Cholesky factorization on a hybrid multicore platform consisting of a multicore CPU and GPU …”
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A supernodal block factorized sparse approximate inverse for non-symmetric linear systems von Ferronato, Massimiliano, Pini, Giorgio

“… that the supernodal Block FSAI preconditioner outperforms the native algorithm and exhibits a much more stable behavior with respect to the variation of the user-specified parameters …”
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Optimized sparse Cholesky factorization on hybrid multicore architectures von Tang, Meng, Gadou, Mohamed, Rennich, Steven, Davis, Timothy A., Ranka, Sanjay

“… We present techniques for supernodal sparse Cholesky factorization on a hybrid multicore platform consisting of a multicore CPU and GPU …”
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An Asynchronous Parallel Supernodal Algorithm for Sparse Gaussian Elimination von Demmel, James W., Gilbert, John R., Li, Xiaoye S.

“… Although Gaussian elimination with partial pivoting is a robust algorithm to solve unsymmetric sparse linear systems of equations, it is difficult to implement efficiently on parallel machines …”
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Accelerating Sparse Cholesky Factorization on GPUs von Rennich, Steven C., Stosic, Darko, Davis, Timothy A.

“… to fully utilize this resource. In this paper we describe a supernodal Cholesky factorization algorithm which permits improved utilization of the GPU when factoring sparse matrices …”
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Supernodal Sparse Cholesky Factorization on Distributed-Memory Multiprocessors von Eswar, Kalluri, Sadayappan, P., Huang, Chua-Huang, Visvanathan, V.

“… This paper discusses the use of supernodes in the design of algorithms for sparse Cholesky factorization on distributed-memory multiprocessors …”
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