Applying reactive models to column experiments to assess the hydrogeochemistry of seawater intrusion: optimising ACUAINTRUSION and selecting cation exchange coefficients with PHREEQC

Three sets of laboratory column experimental results concerning the hydrogeochemistry of seawater intrusion have been modelled using two codes: ACUAINTRUSION (Chemical Engineering Department, University of Alicante) and PHREEQC (U.S.G.S.). These reactive models utilise the hydrodynamic parameters determined using the ACUAINTRUSION TRANSPORT software and fit the chloride breakthrough curves perfectly. The ACUAINTRUSION code was improved, and the instabilities were studied relative to the discretisation. The relative square errors were obtained using different combinations of the spatial and temporal steps: the global error for the total experimental data and the partial error for each element. Good simulations for the three experiments were obtained using the ACUAINTRUSION software with slight variations in the selectivity coefficients for both sediments determined in batch experiments with fresh water. The cation exchange parameters included in ACUAINTRUSION are those reported by the Gapon convention with modified exponents for the Ca/Mg exchange. PHREEQC simulations performed using the Gains-Thomas convention were unsatisfactory, with the exchange coefficients from the database of PHREEQC (or range), but those determined with fresh water – natural sediment allowed only an approximation to be obtained. For the treated sediment, the adjusted exchange coefficients were determined to improve the simulation and are vastly different from those from the database of PHREEQC or batch experiment values; however, these values fall in an order similar to the others determined under dynamic conditions. Different cation concentrations were simulated using two different software packages; this disparity could be attributed to the defined selectivity coefficients that affect the gypsum equilibrium. Consequently, different calculated sulphate concentrations are obtained using each type of software; a smaller mismatch was predicted using ACUAINTRUSION. In general, the presented simulations by ACUAINTRUSION and PHREEQC produced similar results, making predictions consistent with the experimental data. However, the simulated results are not identical to the experimental data; sulphate (total S) is overpredicted by both models, most likely due to such factors as the kinetics of gypsum, the possible variations in the exchange coefficients due to salinity and the neglect of other processes.

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.

Fault tolerant embedded systems design by multi-objective optimization

The design of fault tolerant systems is gaining importance in large domains of embedded applications where design constrains are as important as reliability. New software techniques, based on selective application of redundancy, have shown remarkable fault coverage with reduced costs and overheads. However, the large number of different solutions provided by these techniques, and the costly process to assess their reliability, make the design space exploration a very difficult and time-consuming task. This paper proposes the integration of a multi-objective optimization tool with a software hardening environment to perform an automatic design space exploration in the search for the best trade-offs between reliability, cost, and performance. The first tool is commanded by a genetic algorithm which can simultaneously fulfill many design goals thanks to the use of the NSGA-II multi-objective algorithm. The second is a compiler-based infrastructure that automatically produces selective protected (hardened) versions of the software and generates accurate overhead reports and fault coverage estimations. The advantages of our proposal are illustrated by means of a complex and detailed case study involving a typical embedded application, the AES (Advanced Encryption Standard).

RetinaStudio: A bioinspired framework to encode visual information

The retina is a very complex neural structure, which performs spatial, temporal, and chromatic processing on visual information and converts it into a compact ‘digital’ format composed of neural impulses. This paper presents a new compiler-based framework able to describe, simulate and validate custom retina models. The framework is compatible with the most usual neural recording and analysis tools, taking advantage of the interoperability with these kinds of applications. Furthermore it is possible to compile the code to generate accelerated versions of the visual processing models compatible with COTS microprocessors, FPGAs or GPUs. The whole system represents an ongoing work to design and develop a functional visual neuroprosthesis. Several case studies are described to assess the effectiveness and usefulness of the framework.

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.