Abstract
This paper describes initial experiences with semi-automated performance tuning of a linear solver in LS-DYNA, a large, widely-used engineering application. Through a collection of tools supporting empirical optimization, we alleviate the burden of performance tuning for mapping today's complex software to increasingly complex hardware platforms. We describe a tool that automatically isolates code segments for the purposes of performance tuning, code generation issues, and we present a collection of automatically-generated performance results for specific performance-oriented parameters.

Abstract
In this paper we derive an upper bound for the average-case generalization error of the mixture of experts modular neural network, based on an average-case generalization error bound for an isolated neural network. By doing this we also generalize a previous bound for this architecture that was restricted to special problems. We also present a correction factor for the original average generalization error, that was empirically obtained, that yields more accurate error bounds for the 6 data sets used in the experiments. These experiments illustrate the validity of the derived error bound for the mixture of experts modular neural network and show how it can be used in practice.

Abstract
Diurnal and seasonal changes in the leaf water potential (T), stomatal conductance (g,), net CO2 assimilation rate (P-N), transpiration rate (E), internal CO2 concentration (Q), and intrinsic water use efficiency (P-N/g(s),) were studied in grapevines (Vitis vinifera L. cv. Touriga Nacional) growing in low, moderate, and severe summer stress at Vila Real (VR), Pinh (a) over tildeo (PI), and Almendra (AL) experimental sites, respectively. In VR and PI site the limitation to photosynthesis was caused more by stomatal limitations, while in AL mesophyll limitations were also responsible for the summer decline in P-N.

Abstract
This paper presents some interesting concepts of static dataflow machines that can be used by reconfigurable computing architectures. We introduce some data-driven reconfigurable arrays and summarize techniques to map imperative software programs to those architectures, some of them being focus of current research work. In particular, we briefly present a novel technique for pipelining loops. Experiments with the technique confirm important improvements over the use of conventional loop pipelining. Hence, the technique proves to be an efficient approach to map loops to coarse-grained reconfigurable architectures employing a static dataflow computational model.

Abstract
Loop unrolling plays an important role in compilation for Reconfigurable Processing Units (RPUs) as it exposes operator parallelism and enables other transformations (e.g., scalar replacement). Deciding when and where to apply loop unrolling, either fully or partially, leads to large design space exploration problems. In order to cope with these vast spaces, researchers have explored the application of design estimation techniques. Using estimation, tools can conduct early evaluation of the impact and interplay of transformations in both the required resources and expected performance. In this paper we present some of the current approaches and issues related to estimation of the loop unrolling impact when targeting RPUs.

Abstract
This paper introduces lambda<^>, a simply typed lambda calculus supporting inductive types and recursive function definitions with termination ensured by types. The system is shown to enjoy subject reduction, strong normalisation of typable terms and to be stronger than a related system in which termination is ensured by a syntactic guard condition. The system can, at will, be extended to support coinductive types and corecursive function definitions also.