An ISA Comparison Between Superscalar and Vector Processors

1999 ◽  
pp. 548-560 ◽  
Author(s):  
Francisca Quintana ◽  
Roger Espasa ◽  
Mateo Valero
Keyword(s):  
Vector Processors

Implementation of digital filtering algorithms using pipelined vector processors

1987 ◽  
Vol 75 (9) ◽  
pp. 1293-1303 ◽  
Author(s):  
Wonyong Sung ◽  
S.K. Mitra
Keyword(s):  
Digital Filtering ◽  
Filtering Algorithms ◽  
Vector Processors

Full-Field Compositional Modeling on Vector Processors

1991 ◽  
Vol 6 (01) ◽  
pp. 107-114 ◽  
Author(s):  
L.C. Young
Keyword(s):  
Full Field ◽  
Compositional Modeling ◽  
Vector Processors

Register Flush-free Runahead Execution for Modern Vector Processors

2021 ◽  
Author(s):  
Hikaru Takayashiki ◽  
Masayuki Sato ◽  
Kazuhiko Komatsu ◽  
Hiroaki Kobayashi
Keyword(s):  
Vector Processors ◽  
Runahead Execution

Throughput analysis of digital partitioning with error-correcting codes for optical matrix–vector processors

1995 ◽  
Vol 34 (29) ◽  
pp. 6744 ◽  
Author(s):  
Scott A. Ellett ◽  
Thomas F. Krile ◽  
John F. Walkup
Keyword(s):  
Error Correcting Codes ◽  
Throughput Analysis ◽  
Vector Processors ◽  
Matrix Vector

scholarly journals Parallel/Vector Integration Methods for Dynamical Astronomy

1999 ◽  
Vol 172 ◽  
pp. 231-241
Author(s):  
Toshio Fukushima
Keyword(s):  
Large Scale ◽  
Acceleration Factor ◽  
Picard Iteration ◽  
Vector Integration ◽  
Vector Mode ◽  
Dynamical Astronomy ◽  
Vector Processors ◽  
Processor Unit ◽  
Symmetric Multistep Methods ◽  
Scalar Mode

AbstractThis paper reviews three recent works on the numerical methods to integrate ordinary differential equations (ODE), which are specially designed for parallel, vector, and/or multi-processor-unit (PU) computers. The first is the Picard-Chebyshev method (Fukushima, 1997a). It obtains a global solution of ODE in the form of Chebyshev polynomial of large (> 1000) degree by applying the Picard iteration repeatedly. The iteration converges for smooth problems and/or perturbed dynamics. The method runs around 100-1000 times faster in the vector mode than in the scalar mode of a certain computer with vector processors (Fukushima, 1997b). The second is a parallelization of a symplectic integrator (Saha et al., 1997). It regards the implicit midpoint rules covering thousands of timesteps as large-scale nonlinear equations and solves them by the fixed-point iteration. The method is applicable to Hamiltonian systems and is expected to lead an acceleration factor of around 50 in parallel computers with more than 1000 PUs. The last is a parallelization of the extrapolation method (Ito and Fukushima, 1997). It performs trial integrations in parallel. Also the trial integrations are further accelerated by balancing computational load among PUs by the technique of folding. The method is all-purpose and achieves an acceleration factor of around 3.5 by using several PUs. Finally, we give a perspective on the parallelization of some implicit integrators which require multiple corrections in solving implicit formulas like the implicit Hermitian integrators (Makino and Aarseth, 1992), (Hut et al., 1995) or the implicit symmetric multistep methods (Fukushima, 1998), (Fukushima, 1999).

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