StratusPM: An Analytical Performance Model for Cloud Applications

2016 ◽  
Author(s):  
Mohammad Hamdaqa ◽  
Ladan Tahvildari
Keyword(s):  
Performance Model ◽  
Analytical Performance ◽  
Cloud Applications

An analytical performance model of robotic storage libraries

1996 ◽  
Vol 27-28 (1) ◽  
pp. 231-251
Author(s):  
T Johnson
Keyword(s):  
Performance Model ◽  
Analytical Performance

scholarly journals Analytical performance model for FPGA-based reconfigurable computing

2015 ◽  
Vol 39 (8) ◽  
pp. 796-806 ◽  
Author(s):  
Hossein Mehri ◽  
Bijan Alizadeh
Keyword(s):  
Reconfigurable Computing ◽  
Performance Model ◽  
Analytical Performance

Scaling Up Parallel Computation of Tiled QR Factorizations by a Distributed Scheduling Runtime System and Analytical Modeling

2018 ◽  
Vol 28 (01) ◽  
pp. 1850004 ◽  
Author(s):  
Weijian Zheng ◽  
Fengguang Song ◽  
Lan Lin ◽  
Zizhong Chen
Keyword(s):  
Dynamic Scheduling ◽  
Parallel Implementation ◽  
Optimal Number ◽  
Performance Model ◽  
Analytical Performance ◽  
Distributed Scheduling ◽  
Qr Factorization ◽  
Runtime Systems ◽  
Runtime System ◽  
Factorization Algorithms

Implementing parallel software for QR factorizations to achieve scalable performance on massively parallel manycore systems requires a comprehensive design that includes algorithm redesign, efficient runtime systems, synchronization and communication reduction, and analytical performance modeling. This paper presents a piece of tiled communication-avoiding QR factorization software that is able to scale efficiently for matrices with general dimensions. We design a tiled communication-avoiding QR factorization algorithm and implement it with a fully distributed dynamic scheduling runtime system to minimize both synchronization and communication. The whole class of communication-avoiding QR factorization algorithms uses an important parameter of D (i.e., the number of domains), whose best solution is still unknown so far and requires manual tuning and empirical searching to find it. To that end, we introduce a simplified analytical performance model to determine an optimal number of domains D[Formula: see text]. The experimental results show that our new parallel implementation is faster than a state-of-the-art multicore-based numerical library by up to 30%, and faster than ScaLAPACK by up to 30 times with thousands of CPU cores. Furthermore, using the new analytical model to predict an optimal number of domains is as competitive as exhaustive searching, and exhibits an average performance difference of 1%.

scholarly journals An Analytical Performance Model for the Spidergon NoC

2007 ◽  
Author(s):  
Mahmoud Moadeli ◽  
Ali Shahrabi ◽  
Wim Vanderbauwhede ◽  
Mohamed Ould-Khaoua
Keyword(s):  
Performance Model ◽  
Analytical Performance

Analytical performance model for mobile network operator cloud

2015 ◽  
Vol 71 (12) ◽  
pp. 4555-4577 ◽  
Author(s):  
Hassan Raei ◽  
Nasser Yazdani
Keyword(s):  
Mobile Network ◽  
Performance Model ◽  
Analytical Performance ◽  
Network Operator ◽  
Mobile Network Operator

scholarly journals A Comprehensive Analytical Performance Model of DRAM Caches

2015 ◽  
Author(s):  
Nagendra Gulur ◽  
Mahesh Mehendale ◽  
Ramaswamy Govindarajan
Keyword(s):  
Performance Model ◽  
Analytical Performance

A comprehensive analytical performance model for disk devices under random workloads

2002 ◽  
Vol 14 (1) ◽  
pp. 140-155 ◽  
Author(s):  
P. Triantafillou ◽  
S. Christodoulakis ◽  
C.A. Georgiadis
Keyword(s):  
Performance Model ◽  
Analytical Performance

V/STOL shaft propulsion system analytical performance model

1977 ◽  
Author(s):  
R. SULKOSKE ◽  
R. TUCKER ◽  
J. HOLMES
Keyword(s):  
Performance Model ◽  
Propulsion System ◽  
Analytical Performance
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