Is average superlinear speedup possible?

2005 ◽  
pp. 301-312 ◽  
Author(s):  
Ewald Speckenmeyer
Keyword(s):  
Superlinear Speedup

Superlinear Speedup

2011 ◽  
pp. 1955-1955
Author(s):  
Jack Dongarra ◽  
Piotr Luszczek ◽  
Felix Wolf ◽  
Jesper Larsson Träff ◽  
Patrice Quinton ◽  
...  
Keyword(s):  
Superlinear Speedup

Virtualized environments in cloud can have superlinear speedup

2012 ◽  
Author(s):  
Sasko Ristov ◽  
Magdalena Kostoska ◽  
Marjan Gusev ◽  
Kiril Kiroski
Keyword(s):  
Superlinear Speedup

Modelling superlinear speedup on distributed memory multiprocessors

1998 ◽  
pp. 689-692
Author(s):  
V. Bianco ◽  
F.F. Rivera ◽  
D.B. Heras ◽  
M. Amor ◽  
O.G. Plata ◽  
...  
Keyword(s):  
Distributed Memory ◽  
Superlinear Speedup

scholarly journals Comparison between O(n2) and O(n) neighbor search algorithm and its influence on superlinear speedup in parallel discrete element method (DEM) for complex-shaped particles

2018 ◽  
Vol 35 (6) ◽  
pp. 2327-2348 ◽  
Author(s):  
Beichuan Yan ◽  
Richard Regueiro
Keyword(s):  
Discrete Element Method ◽  
Particle Shape ◽  
Search Algorithm ◽  
Discrete Element ◽  
Search Algorithms ◽  
Content Type ◽  
Neighbor Search ◽  
Shape Complexity ◽  
Superlinear Speedup ◽  
Element Method

Purpose This paper aims to present performance comparison between O(n2) and O(n) neighbor search algorithms, studies their effects for different particle shape complexity and computational granularity (CG) and investigates the influence on superlinear speedup of 3D discrete element method (DEM) for complex-shaped particles. In particular, it aims to answer the question: O(n2) or O(n) neighbor search algorithm, which performs better in parallel 3D DEM computational practice? Design/methodology/approach The O(n2) and O(n) neighbor search algorithms are carefully implemented in the code paraEllip3d, which is executed on the Department of Defense supercomputers across five orders of magnitude of simulation scale (2,500; 12,000; 150,000; 1 million and 10 million particles) to evaluate and compare the performance, using both strong and weak scaling measurements. Findings The more complex the particle shapes (from sphere to ellipsoid to poly-ellipsoid), the smaller the neighbor search fraction (NSF); and the lower is the CG, the smaller is the NSF. In both serial and parallel computing of complex-shaped 3D DEM, the O(n2) algorithm is inefficient at coarse CG; however, it executes faster than O(n) algorithm at fine CGs that are mostly used in computational practice to achieve the best performance. This means that O(n2) algorithm outperforms O(n) in parallel 3D DEM generally. Practical implications Taking for granted that O(n) outperforms O(n2) unconditionally, complex-shaped 3D DEM is a misconception commonly encountered in the computational engineering and science literature. Originality/value The paper clarifies that performance of O(n2) and O(n) neighbor search algorithms for complex-shaped 3D DEM is affected by particle shape complexity and CG. In particular, the O(n2) algorithm outperforms the O(n) algorithm in large-scale parallel 3D DEM simulations generally, even though this outperformance is counterintuitive.

PARALLEL REAL-TIME COMPUTATION OF NONLINEAR FEEDBACK FUNCTIONS

2003 ◽  
Vol 13 (01) ◽  
pp. 65-75 ◽  
Author(s):  
SELIM G. AKL
Keyword(s):  
Real Time ◽  
Parallel Computer ◽  
Nonlinear Feedback ◽  
Feedback Function ◽  
Parallel Solution ◽  
Computational Problem ◽  
Superlinear Speedup ◽  
Better Than ◽  
Surprising Observation

This paper focuses on the improvement in the quality of computation provided by parallelism. The problem of interest is that of computing the maximum of a nonlinear feedback function in a real-time environment. We show that the solution obtained in parallel is significantly, provably, and consistently better than a sequential one. It is important to note that our purpose is not to demonstrate merely that a parallel computer can obtain a solution to a computational problem that is of higher quality than one derived sequentially. The latter is an interesting (and often surprising) observation in its own right, but we wish to go further. It is shown here that the improvement in quality due to parallelism can be arbitrarily high. To be specific, the ratio of the parallel solution to the sequential one is typically superlinear in the number of processors used by the parallel computer. This result is akin to superlinear speedup—a phenomenon itself originally thought to be impossible.

Achieving superlinear speedup on a heterogeneous, distributed system

1994 ◽  
Vol 2 (2) ◽  
pp. 57-61 ◽  
Author(s):  
C.R. Mechoso ◽  
J.D. Farrara ◽  
J.A. Spahr
Keyword(s):  
Distributed System ◽  
Superlinear Speedup
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