Abstract
This paper presents an approach to the shared task HaSpeeDe within Evalita 2018. We followed a standard machine learning procedure with training, validation, and testing phases. We considered word embedding as features and deep learning for classification. We tested the effect of merging two datasets in the classification of messages from Facebook and Twitter. We concluded that using data for training and testing from the same social network was a requirement to achieve a good performance. Moreover, adding data from a different social network allowed to improve the results, indicating that more generalized models can be an advantage.

Abstract

Abstract
Usually, Aspect-Oriented Programming (AOP) languages are an extension of a specific target programming language (e.g., Aspect J for JAVA and Aspect C++ for C++). Although providing AOP support with target language extensions may ease the adoption of an approach, it may impose constraints related with constructs and semantics. Furthermore, by tightly coupling the AOP language to the target language the reuse potential of many aspects, especially the ones regarding non-functional requirements, is lost. LARA is a domain-specific language inspired by AOP concepts, having the specification of source-to-source transformations as one of its main goals. LARA has been designed to be, as much as possible, independent of the target language and to provide constructs and semantics that ease the definition of concerns, especially related to non-functional requirements. In this paper, we propose techniques to overcome some of the challenges presented by a multilanguage approach to AOP of cross-cutting concerns focused on non-functional requirements and applied through the use of a weaving process. The techniques mainly focus on providing well-defined library interfaces that can have concrete implementations for each supported target language. The developer uses an agnostic interface and the weaver provides a specific implementation for the target language. We evaluate our approach using 8 concerns with varying levels of language agnosticism that support 4 target languages (C, C++, JAVA and MATLAB) and show that the proposed techniques contribute to more concise LARA aspects, high reuse of aspects, and to significant effort reductions when developing weavers for new imperative, object-oriented programming languages.

Abstract
Writing mixed-precision kernels allows to achieve higher throughput together with outputs whose precision remain within given limits. The recent introduction of native half-precision arithmetic capabilities in several GPUs, such as NVIDIA P100 and AMD Vega 10, contributes to make precision-tuning even more relevant as of late. However, it is not trivial to manually find which variables are to be represented as half-precision instead of single- or double-precision. Although the use of half-precision arithmetic can speed up kernel execution considerably, it can also result in providing non-usable kernel outputs, whenever the wrong variables are declared using the half-precision data-type. In this paper we present an automatic approach for precision tuning. Given an OpenCL kernel with a set of inputs declared by a user (i.e., the person responsible for programming and/or tuning the kernel), our approach is capable of deriving the mixed-precision versions of the kernel that are better improve upon the original with respect to a given metric (e.g., time-to-solution, energy-to-solution). We allow the user to declare and/or select a metric to measure and to filter solutions based on the quality of the output. We implement a proof-of-concept of our approach using an aspect-oriented programming language called LARA. It is capable of generating mixed-precision kernels that result in considerably higher performance when compared with the original single-precision floating-point versions, while generating outputs that can be acceptable in some scenarios.

Abstract
Automatic parallelization of sequential code has become increasingly relevant in multicore programming. In particular, loop parallelization continues to be a promising optimization technique for scienti.c applications, and can provide considerable speedups for program execution. Furthermore, if we can verify that there are no true data dependencies between loop iterations, they can be easily parallelized. This paper describes Clava AutoPar, a library for the Clava weaver that performs automatic and symbolic parallelization of C code. The library is composed of two main parts, parallel loop detection and source-to-source code parallelization. The system is entirely automatic and attempts to statically detect parallel loops for a given input program, without any user intervention or profiling information. We obtained a geometric mean speedup of 1.5 for a set of programs from the C version of the NAS benchmark, and experimental results suggest that the performance obtained with Clava AutoPar is comparable or better than other similar research and commercial tools.

Abstract
In the context of compiler optimizations, tuning of parameters and selection of algorithms, runtime adaptivity and autotuning are becoming increasingly important, especially due to the complexity of applications, workloads, computing devices and execution environments. For identifying and specifying adaptivity, different phases are required: analysis of program hotspots and adaptivity opportunities, code restructuring, and programming of adaptivity strategies. These phases usually require different tools and modications to the source code that may result in difficult to maintain and error prone code. This paper presents a flexible approach to support the different phases when developing adaptive applications. The approach is based on a single domain-specific language (DSL), able to specify and evaluate multiple strategies and to maintain a separation of concerns. We describe the requirements and the design of the DSL, an accompanying API, and of a Java-to-Java compiler that implements the approach. In addition, we present and evaluate the use of the approach to specify runtime adaptivity strategies in the context of Java programs, especially when considering runtime autotuning of optimization parameters and runtime selection of algorithms. Although simple, the case studies shown truly demonstrate the main advantages of the approach in terms of the programming model and of the performance impact.