Download Algorithms and Architectures for Parallel Processing: 11th by David Abramson (auth.), Yang Xiang, Alfredo Cuzzocrea, PDF

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

This quantity set LNCS 7016 and LNCS 7017 constitutes the refereed complaints of the eleventh overseas convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011.
The first quantity offers 24 revised normal 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 various dimensions of parallel algorithms and architectures, encompassing primary theoretical techniques, useful experimental effects, and advertisement parts and structures and concentrate on vast parts of parallel and disbursed computing, i.e., architectures, algorithms and networks, and structures and applications.

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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 , . . , bk ) ∈ Zn×k Output: LLL-reduced lattice basis B (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) APPROX BASIS(B , B) while (i ≤ k) do SCALARPRODUCTS(R, µ) ORTHOGONALIZATION(R, µ) f = µ-UPDATE(µ) SIZEREDUCTION(B, f ) if (Fc = false ∧ Fr = true) then RECOMPUTE Rij () Fr = false if (Fc = true) then i = max(i − 1, 2) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) Fc = false else i =i while ((i > 1) ∧ (yRi−1,i−1 > Si−1 )) do SWAP(bi , bi−1 ) SWAP(bi , bi−1 ) i =i−1 if (i = i ) then if (i = 1) then R11 = b1 i = 2 else i =i+1 The structure of the parallel aSE-LLL (see Algorithm 2) is very similar to the structure of the sequential aSE-LLL (see Algorithm 1).

To further explore the independence of the performance on the size of the aggregation tree, we report the average communication overhead per node in Figure 3b. As shown in this figure, MAI outperforms SHIA in terms of the average amount of communications. And MAI exhibits a little variance when n ranges from 50 to 250. The communication overhead is closely related to the network topology. In the simulations, the nodes are randomly distributed in the area. That’s why the overhead increases with the increase of the network size, but still fluctuates at some points.

However, Helgrind+ is not able to detect this kind of bug, since each single access to a shared variable is properly protected by locks. The new approach detects the race and passes in test 2. Test 3 represents another arithmetic function for vector normal1 normalize () { ization (Figure 6). It has a more 2 lock (& m ); complicated dependency graph, be- 3 f l o a t len = sqrt ( a * a + b * b ); cause it uses the sqrt() function. 4 a = a / len ; b = b / len ; Still, all correlations are detected.

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