LALP: a language to program custom FPGA-based acceleration engines

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly important in embedded and high-performance computing systems. They allow performance levels close to the ones obtained with Application-Specific Integrated Circuits, while still keeping design and implementation flexibility. However, to efficiently program FPGAs, one needs the expertise of hardware developers in order to master hardware description languages (HDLs) such as VHDL or Verilog. Attempts to furnish a high-level compilation flow (e.g., from C programs) still have to address open issues before broader efficient results can be obtained. Bearing in mind an FPGA available resources, it has been developed LALP (Language for Aggressive Loop Pipelining), a novel language to program FPGA-based accelerators, and its compilation framework, including mapping capabilities. The main ideas behind LALP are to provide a higher abstraction level than HDLs, to exploit the intrinsic parallelism of hardware resources, and to allow the programmer to control execution stages whenever the compiler techniques are unable to generate efficient implementations. Those features are particularly useful to implement loop pipelining, a well regarded technique used to accelerate computations in several application domains. This paper describes LALP, and shows how it can be used to achieve high-performance computing solutions.

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

A new damage model for composite laminates

Aircraft composite structures must have high stiffness and strength with low weight, which can guarantee the increase of the pay-load for airplanes without losing airworthiness. However, the mechanical behavior of composite laminates is very complex due the inherent anisotropy and heterogeneity. Many researchers have developed different failure progressive analyses and damage models in order to predict the complex failure mechanisms. This work presents a damage model and progressive failure analysis that requires simple experimental tests and that achieves good accuracy. Firstly, the paper explains damage initiation and propagation criteria and a procedure to identify the material parameters. In the second stage, the model was implemented as a UMAT (User Material Subroutine), which is linked to finite element software, ABAQUS (TM), in order to predict the composite structures behavior. Afterwards, some case studies, mainly off-axis coupons under tensile or compression loads, with different types of stacking sequence were analyzed using the proposed material model. Finally, the computational results were compared to the experimental results, verifying the capability of the damage model in order to predict the composite structure behavior. (C) 2011 Elsevier Ltd. All rights reserved.

Determination of the activation energies of beef tallow and crude glycerin combustion using thermogravimetry

The present study deals with the determination of the activation energy for the thermal decomposition of two renewable fuels crude glycerin and beef tallow. The activation energies were investigated by using a thermogravimetric analyzer (TGA) in the temperature range of 25-600 degrees C in atmosphere of synthetic air. The TG curves of the thermal decomposition process of both samples were divided into several phases and the second, called PH2, was chosen for the kinetic study because it is associated with the combustion ignition. Differential Thermal Analysis (DTA) showed an endothermic event at the PH2 region for the crude glycerin corresponding to devolatilization, while for beef tallow, this step presented an exothermic event, called LTO (low-temperature oxidation), which is correlated with devolatilization followed by combustion. For the entire PH2, activation energy values for crude glycerin were between 90 kJ mol(-1) and 42 kJ mol(-1), while for the beef tallow they ranged from 50 kJ mol(-1) to 113 kJ mol (1). The activation energy values obtained at the pre-ignition stage - conversion between 0 and 0.45 - showed that the crude glycerin with higher values requires an additional energetic support at the start of combustion processes and the beef tallow ignites more easily, presenting lower values. According to the Wolfer's equation, a direct relation between the activation energy and the ignition delay is established and the results of this study provides useful data for the development and design of new combustion chambers and engines when non-traditional fuels are used as feedstock. (C) 2012 Elsevier Ltd. All rights reserved.

Nitrous oxide emissions from gasohol, ethanol and CNG light duty vehicles

A sample of 21 light duty vehicles powered by Otto cycle engines were tested on a chassis dynamometer to measure the exhaust emissions of nitrous oxide (N2O). The tests were performed at the Vehicle Emission Laboratory of CETESB (Environmental Company of the State of Sao Paulo) using the US-FTP-75 (Federal Test Procedure) driving cycle. The sample tested included passenger cars running on three types of fuels used in Brazil: gasohol, ethanol and CNG. The measurement of N2O was made using two methods: Non Dispersive InfraRed (NDIR) analyzer and Fourier Transform InfraRed spectroscopy (FTIR). Measurements of regulated pollutants were also made in order to establish correlations between N2O and NOx. The average N2O emission factors obtained by the NDIR method was 78 +/- 41 mg.km(-1) for vehicles running with gasohol, 73 +/- 45 mg.km(-1) for ethanol vehicles and 171 +/- 69 mg.km(-1) for CNG vehicles. Seventeen results using the FTIR method were also obtained. For gasohol vehicles the results showed a good agreement between the two methods, with an average emission factor of 68 +/- 41 mg.km(-1). The FTIR measurement results of N2O for ethanol and CNG vehicles were much lower than those obtained by the NDIR method. The emission factors were 17 +/- 10 mg.km(-1) and 33 +/- 17 mg.km(-1), respectively, possibly because of the interference of water vapor (present at a higher concentration in the exhaust gases of these vehicles) on measurements by the NDIR method.

MOOGLE: a metamodel-based model search engine

Models are becoming increasingly important in the software development process. As a consequence, the number of models being used is increasing, and so is the need for efficient mechanisms to search them. Various existing search engines could be used for this purpose, but they lack features to properly search models, mainly because they are strongly focused on text-based search. This paper presents Moogle, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed. The paper also presents the results of an evaluation of Moogle, which showed that the metamodel information improves the accuracy of the search.