Multi-objective adaptive evolutionary strategy for tuning compilations

Tuning compilations is the process of adjusting the values of a compiler options to improve some features of the final application. In this paper, a strategy based on the use of a genetic algorithm and a multi-objective scheme is proposed to deal with this task. Unlike previous works, we try to take advantage of the knowledge of this domain to provide a problem-specific genetic operation that improves both the speed of convergence and the quality of the results. The evaluation of the strategy is carried out by means of a case of study aimed to improve the performance of the well-known web server Apache. Experimental results show that a 7.5% of overall improvement can be achieved. Furthermore, the adaptive approach has shown an ability to markedly speed-up the convergence of the original strategy.

Heterofunctional Supports in Enzyme Immobilization: From Traditional Immobilization Protocols to Opportunities in Tuning Enzyme Properties

A heterofunctional support for enzyme immobilization may be defined as that which possesses several distinct functionalities on its surface able to interact with a protein. We will focus on those supports in which a final covalent attachment between the enzyme and the support is achieved. Heterofunctionality sometimes has been featured in very old immobilization techniques, even though in many instances it has been overlooked, giving rise to some misunderstandings. In this respect, glutaraldehyde-activated supports are the oldest multifunctional supports. Their matrix has primary amino groups, the hydrophobic glutaraldehyde chain, and can covalently react with the primary amino groups of the enzyme. Thus, immobilization may start (first event of the immobilization) via different causes and may involve different positions of the enzyme surface depending on the activation degree and immobilization conditions. Other “classical” heterofunctional supports are epoxy commercial supports consisting of reactive covalent epoxy groups on a hydrophobic matrix. Immobilization is performed at high ionic strength to permit protein adsorption, so that covalent attachment may take place at a later stage. Starting from these old immobilization techniques, tailor-made heterofunctional supports have been designed to permit a stricter control of the enzyme immobilization process. The requirement is to find conditions where the main covalent reactive moieties may have very low reactivity toward the enzyme. In this Review we will discuss the suitable properties of the groups able to give the covalent attachment (intending a multipoint covalent attachment), and the groups able to produce the first enzyme adsorption on the support. Prospects, limitations, and likely pathways for the evolution (e.g., coupling of site-directed mutagenesis and thiol heterofunctional supports of enzyme immobilization on heterofunctional supports) will be discussed in this Review.

Tuning compilations by multi-objective optimization: Application to Apache web server

Modern compilers present a great and ever increasing number of options which can modify the features and behavior of a compiled program. Many of these options are often wasted due to the required comprehensive knowledge about both the underlying architecture and the internal processes of the compiler. In this context, it is usual, not having a single design goal but a more complex set of objectives. In addition, the dependencies between different goals are difficult to be a priori inferred. This paper proposes a strategy for tuning the compilation of any given application. This is accomplished by using an automatic variation of the compilation options by means of multi-objective optimization and evolutionary computation commanded by the NSGA-II algorithm. This allows finding compilation options that simultaneously optimize different objectives. The advantages of our proposal are illustrated by means of a case study based on the well-known Apache web server. Our strategy has demonstrated an ability to find improvements up to 7.5% and up to 27% in context switches and L2 cache misses, respectively, and also discovers the most important bottlenecks involved in the application performance.