Software Composition: 10th International Conference, SC by Perla Velasco Elizondo, Mbe Koua Christophe Ndjatchi

By Perla Velasco Elizondo, Mbe Koua Christophe Ndjatchi (auth.), Sven Apel, Ethan Jackson (eds.)

The publication constitutes the refereed court cases of the tenth foreign convention on software program Composition, SC 2011, held in Zurich, Switzerland, in June/July 2011, co-located with instruments 2011 Federated Conferences.The 10 revised complete papers and a couple of brief papers have been rigorously reviewed and chosen from 32 preliminary submissions for inclusion within the publication. The papers mirror all present examine in software program composition and are prepared in topical sections on composition and interfaces, points and lines, and applications.

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In: Proc. ACM SIGPLAN Symp. on Princ. and Pract. of Parallel Programming, pp. 277–288. ACM, New York (2005) 25. : An adaptive algorithm selection framework for reduction parallelization. IEEE Trans. Par. Distr. Syst. 17, 1084–1096 (2006) 26. : A framework for performance-aware composition of explicitly parallel components. , et al. ) ParCo-2007: J¨ulich/Aachen Parallel Computing: Architectures, Algorithms and Applications. Advances in Parallel Computing Series, vol. 15, pp. 227–234. IOS Press, Amsterdam (2008) 27.

In our example, (pre-)learning time was still acceptable in range of a few seconds. The memory required to encode the SVM classifier is 544 bytes (based on the LIBSVM library implementation). This is the highest memory overhead and even 75% larger than Dispatch Table memory consumption. 2 Decision Accuracy of the Approaches In this section, we compare the accuracy of the different decision approaches. As we know the right decision for each actual context (processors available and problem size) only for the sample points measured in the training phase, we can assess accuracy only approximatively: (1) by comparing the decisions of the different approaches at these sample points, and (2) by comparing their decisions with the decisions of the table approach as the baseline.

It uses castToPtr to create a Ptr() from the write set entry and stores this pointer in the writeSet using the putWS function. Pointers are created using the Stable Pointers API [18]: castToPtr first creates a stable pointer for the An Implementation of Composable Memory Transactions in Haskell 39 read set entry and then castStablePtrToPtr is used to create a Ptr (). The problem of using stable pointers is that they are not garbage collected and have to be manually freed. When a value is updated in the write set, the old stable pointer must be freed and after a transaction commits, the stable pointers in its write set must also be freed.

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