By Roman Wyrzykowski, Jack Dongarra, Konrad Karczewski, Jerzy Waśniewski
This two-volume-set (LNCS 8384 and 8385) constitutes the refereed complaints of the tenth overseas convention of Parallel Processing and utilized arithmetic, PPAM 2013, held in Warsaw, Poland, in September 2013. The 143 revised complete papers awarded in either volumes have been rigorously reviewed and chosen from quite a few submissions. The papers hide vital fields of parallel/distributed/cloud computing and utilized arithmetic, resembling numerical algorithms and parallel medical computing; parallel non-numerical algorithms; instruments and environments for parallel/distributed/cloud computing; functions of parallel computing; utilized arithmetic, evolutionary computing and metaheuristics.
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Extra resources for Parallel Processing and Applied Mathematics: 10th International Conference, PPAM 2013, Warsaw, Poland, September 8-11, 2013, Revised Selected Papers, Part II
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Eur. J. Oper. Res. 194, 856–872 (2009) 8. : First fit decreasing scheduling on uniform multiprocessors. Discrete Appl. Math. 10, 165–177 (1985) 9. : Mathematical models for time/cost optimization in grid scheduling. 04, School of Computing, University of Leeds (2008). pdf ´ An approximation algorithm for the generalized assign10. : ment problem. Math. Program. 62, 461–474 (1993) 11. : A novel economic-based scheduling heuristic for computational grids. Int. J. High Perform. Comput. Appl. at Abstract.
The processors are managed by a centralized scheduler. A user workload is composed of successive campaigns where each campaign, indexed by i is submitted at a time denoted by tui and is composed of a set of independent and non-preemptive sequential jobs. We consider an on-line problem in which any particular campaign i (and its jobs) is unknown to the scheduler until it is submitted. A campaign is defined as the set Jiu containing the jobs released by a user u in one submission; nui denotes the number of jobs of a campaign and nu the total number of jobs released in all the campaigns of user u.
Pr }, loading them as much as possible by the LPT-strategy; the remaining processors {Pr+1 , . . , Pm } are loaded one by one with unscheduled jobs. The solution of the smallest cost is selected among those constructed for different values of r, 1 ≤ r ≤ m. Algorithm ‘LPT Groups’ (LPT with Groups of Equivalent Processors) 1. Form groups G1 , . . , Gg of processors putting in one group processors of the same relative cost c¯i . Keep group numbering consistent with processor numbering (2) so that the groups with the smallest indices contain the cheapest processors.