Improvements in performance modeling and identification of computational regimes within software libraries is a critical first step in developing software libraries that are truly agile with respect to the application as well as to the hardware. It is shown here that Pareto ranking, a concept from multi-objective optimization, can be an effective tool for mining large performance datasets. The approach is illustrated using software performance data gathered using both the public domain LAPACK library and an asynchronous communication library based on IBM LAPI active message library. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract Automatic peptide identification from collision‐induced dissociation tandem mass spectrometry data using optimization techniques is made difficult by large plateaus in the fitness landscapes of scoring functions, by the fuzzy nature of constraints from noisy data and by the existence of diverse but equally justifiable probabilistic models of peak matching. Here, two different scoring functions are combined into a parallel multi‐objective optimization framework. It is shown how multi‐objective optimization can be used to empirically test for independence between distinct scoring functions. The loss of selection pressure during the evolution of a population of putative peptide sequences by a Pareto‐driven genetic algorithm is addressed by alternating between two definitions of fitness according to a numerical threshold. Copyright © 2005 John Wiley & Sons, Ltd.

Summary form only given. Automatic de novo peptide identification from collision-induced dissociation tandem mass spectrometry data is made difficult by large plateaus in the fitness landscapes of scoring functions and the fuzzy nature of the constraints that is due to noise in the data. A framework is presented for combining different peptide identification methods within a parallel genetic algorithm. The distinctive feature of our approach, based on Pareto ranking, is that it can accommodate constraints and possibly conflicting scoring functions. We have also shown how population structure can significantly improve the wall clock time of a parallel peptide identification genetic algorithm while at the same time maintaining some exchange of information across local populations.

An asynchronous communication library for accessing and managing dynamic hash tables over a network of Symmetric Multiprocessors (SMP) is presented. A blocking factor is shown experimentally to reduce the variance of the wall clock time. It is also shown that remote accesses to a distributed hash table can be as effective and scalable as the one-sided operations of the low-level communication middleware on an IBM SP.

The cost of pivoting in L U factorization has be­ come non-negligible on MIMD computers due to the acceleration of floating point ari thmetic and compar­ atively slow communications. This study addresses the L U factorization of matrices stored by row. The importance of effici ently broadcasting pivot data is stressed. Multicasting is found preferable in this re­ spect to broadcasting along minimum spanning trees. Threshold pivoting is shown to effectively reduce the number of messages while preserving a good load bal­ ance.