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Efficient Computations on faultprone BSP machines
, 1997
"... In this paper general simulations of algorithms designed for fully operational BSP machines on BSP machines with faulty or unavailable processors, are developed. The failstop model is considered for the fault occurrences, that is, if a processor fails or becomes unavailable, it remains so until th ..."
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Cited by 3 (1 self)
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In this paper general simulations of algorithms designed for fully operational BSP machines on BSP machines with faulty or unavailable processors, are developed. The failstop model is considered for the fault occurrences, that is, if a processor fails or becomes unavailable, it remains so until the end of the computation. The faults are random, in the sense that a processor may fail independently with probability at most a, where a is a constant.
Efficient Parallel Computing with Memory Faults
"... . In this paper we show two results on PRAM with constant fraction of memory faults. First we show how to preprocess (i.e. connect a constant fraction of processors into a binary tree) a faulty EREW PRAM with n= log n processors and O(n) memory cells in O(log n) time. The preprocessing is a basic st ..."
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Cited by 2 (1 self)
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. In this paper we show two results on PRAM with constant fraction of memory faults. First we show how to preprocess (i.e. connect a constant fraction of processors into a binary tree) a faulty EREW PRAM with n= log n processors and O(n) memory cells in O(log n) time. The preprocessing is a basic step of simulations from [7,9,17]. Our algorithm, together with the results from [17], gives a first fully workoptimal randomized simulations of EREW on EREW with faults with logarithmic overhead. In the second part of this paper, we consider the CRCW PRAM with memory faults. We show that (after O(log n)time preprocessing) any algorithm for O(n)processor PRAM can be simulated with optimal work in O(log n) time on CRCW with memory faults. The simulation improves the result of [7]. All simulations assume static faults, i.e. that the errors are determined before the computation starts and that no new errors occur during the computation. 1 Introduction The increasing complexity of multiproc...
DISTRIBUTED ALGORITHMS TO PERFORM INDEPENDENT TASKS IN NETWORKS WITH PROCESSOR FAULTS
, 2001
"... ii iii Acknowledgement Thanks are due to my thesis supervisor Bogdan Chlebus for his guidance and inspiration. This dissertation contains results obtained as a joint work with Bogdan Chlebus, Leszek G,asieniec, Andrzej Lingas and Alex Shvartsman. I thank them all for a rewarding and fruitful coopera ..."
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ii iii Acknowledgement Thanks are due to my thesis supervisor Bogdan Chlebus for his guidance and inspiration. This dissertation contains results obtained as a joint work with Bogdan Chlebus, Leszek G,asieniec, Andrzej Lingas and Alex Shvartsman. I thank them all for a rewarding and fruitful cooperation.
Selected Communication Problems In Networks With Faults
, 1999
"... Contents 1 Introduction 7 1.1 Overview of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Technical preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Probabilistic preliminaries . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Token transfer 11 2.1 Pre ..."
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Contents 1 Introduction 7 1.1 Overview of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Technical preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Probabilistic preliminaries . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Token transfer 11 2.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Link failures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 Link and node failures . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 Token dispersal 27 3.1 Overview of algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 Link failures (q = 0) . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 Node failures (p = 0) . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4 Link and node failures<F13.5
Fast Deterministic Computations on a Faulty PRAM
"... We develop a method to simulate deterministically the operational Parallel Random Access Machine (PRAM) on a PRAM prone to processor and memory failures. It is shown that an nprocessor PRAM with a bounded fraction of faulty processors and memory cells can simulate deterministically the fully opera ..."
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We develop a method to simulate deterministically the operational Parallel Random Access Machine (PRAM) on a PRAM prone to processor and memory failures. It is shown that an nprocessor PRAM with a bounded fraction of faulty processors and memory cells can simulate deterministically the fully operational PRAM with O(log n) slowdown, after preprocessing performed in time O(log² n).
FaultTolerant Shared Memory Simulations (Extended Abstract)
, 1996
"... We consider the problem of simulating a PRAM on a faulty distributed memory machine (DMM). We focus on dynamic faults, i.e. each processor or memory module independently fails during the simulation of a PRAM step with fixed probability and remains faulty for the rest of the simulation. We build upon ..."
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We consider the problem of simulating a PRAM on a faulty distributed memory machine (DMM). We focus on dynamic faults, i.e. each processor or memory module independently fails during the simulation of a PRAM step with fixed probability and remains faulty for the rest of the simulation. We build upon randomized hashingbased simulations on nonfaulty DMMs from [14], which achieve delay O(log log n), with high probability. We design and analyze routines for handling faults occurring during the simulation. Based on these routines we present simulations on faulty DMMs with the same delay O(log log n) as in the nonfaulty case, provided that the failure probability of processors and modules is small enough to guarantee an expected linear number of processors and modules to ...