By David Abramson (auth.), Yang Xiang, Alfredo Cuzzocrea, Michael Hobbs, Wanlei Zhou (eds.)

ISBN-10: 3642246494

ISBN-13: 9783642246494

ISBN-10: 3642246508

ISBN-13: 9783642246500

This quantity set LNCS 7016 and LNCS 7017 constitutes the refereed court cases of the eleventh foreign convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011.

The first quantity provides 24 revised average papers and 17 revised brief papers including the summary of the keynote lecture - all conscientiously reviewed and chosen from eighty five preliminary submissions. The papers conceal the numerous dimensions of parallel algorithms and architectures, encompassing basic theoretical techniques, functional experimental effects, and advertisement parts and structures and concentrate on large parts of parallel and allotted computing, i.e., architectures, algorithms and networks, and structures and applications.

**Read Online or Download Algorithms and Architectures for Parallel Processing: 11th International Conference, ICA3PP, Melbourne, Australia, October 24-26, 2011, Proceedings, Part I PDF**

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**Extra info for Algorithms and Architectures for Parallel Processing: 11th International Conference, ICA3PP, Melbourne, Australia, October 24-26, 2011, Proceedings, Part I**

**Sample text**

5 unlock (& m ); The normalization itself is consis- 6 } tently protected by a single lock, so Fig. 6. Test 3: Vector normalization it is data race free. A similar scenario is represented by test 4, shown in Figure 7: The 1 append ( char * s ) { two shared variables mCont and mLen 2 lock (& m1 ); 3 mCont = f ( mCont , s ); are related through the string s, yet protected by diﬀerent locks. Again, 4 unlock (& m1 ); 5 } a typical race detector cannot de6 reflow ( char * s ) { tect this data race. In contrast, our 7 lock (& m2 ); approach correctly infers the corre8 mLen = g ( mLen , s ); lated set {s, mCont, mLen} and de9 unlock (& m2 ); tects its empty lockset.

Their parallel algorithm relies on a PRAM type model where all threads have instant access to shared memory. The shared memory is used to synchronize the parallel computation eﬀorts and to exchange data in between threads. They were the ﬁrst to ﬁnd a solution that deals with the dependencies in the main while-loop of the aSE-LLL algorithm which up to then made it impossible to eﬃciently parallelize the algorithm. Algorithm 2. Parallel Advanced Schnorr-Euchner LLL Algorithm (simpliﬁed) Input: Lattice basis B = (b1 , .

To complete the adapted lockset algorithm, we can now simply replace Lt with Lu and Cv with Cs (Cs denotes the candidate set of a correlated set s and is computed analogously to Cv ). For the discussion of our locking policy, we assume that a correlated set s is accessed by u1 and u2 in parallel, with Lu1 ∩ Lu2 = ∅. Obviously shared(u1 ) and shared(u2 ) cannot overlap, so that only the following two general cases of interleaving are possible: excl1 (u1 ) excl1 (u2 ) shared(u1 ) shared(u2 ) (a) Either shared(u1 ) shared(u2 ) excl2 (u1 ) excl2 (u2 ) precedes excl1 (u1 ) shared(u1 ) excl1 (u2 ) excl2 (u1 ) shared(u2 ) excl2 (u2 ) (b) Or shared(u1 ) precedes shared(u2 ) 20 A.

### Algorithms and Architectures for Parallel Processing: 11th International Conference, ICA3PP, Melbourne, Australia, October 24-26, 2011, Proceedings, Part I by David Abramson (auth.), Yang Xiang, Alfredo Cuzzocrea, Michael Hobbs, Wanlei Zhou (eds.)

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