High Performance Fortran (HPF) was developed to support data parallel programming for single-instruction multiple-data (SIMD) and multiple-instruction multiple-data (MIMD) machines with distributed memory. The programmer is provided a familiar uniform logical address space and specifies the data distribution by directives. The compiler then exploits these directives to allocate arrays in the local memories, to assign computations to elementary processors, and to migrate data between processors when required. We show here that linear algebra is a powerful framework to encode HPF directives and to synthesize distributed code with space-efficient array allocation, tight loop bounds, and vectorized communications forINDEPENDENTloops. The generated code includes traditional optimizations such as guard elimination, message vectorization and aggregation, and overlap analysis. The systematic use of an affine framework makes it possible to prove the compilation scheme correct.
International audienceModular static analyzers use procedure abstractions, a.k.a. summarizations, to ensure that their execution time increases linearly with the size of analyzed programs. A similar abstraction mechanism is also used within a procedure to perform a bottom-up analysis. For instance, a sequence of instructions is abstracted by combining the abstractions of its components, or a loop is abstracted using the abstraction of its loop body: fixed point iterations for a loop can be replaced by a direct computation of the transitive closure of the loop body abstraction. More specifically, our abstraction mechanism uses affine constraints, i.e. polyhedra, to specify pre- and post-conditions as well as state transformers. We present an algorithm to compute the transitive closure of such a state transformer, and we illustrate its performance on various examples. Our algorithm is simple, based on discrete differentiation and integration: it is very different from the usual abstract interpretation fixed point computation based on widening. Experiments are carried out using previously published examples. We obtain the same results directly, without using any heuristic
Automatic emotion recognition constitutes one of the great challenges providing new tools for more objective and quicker diagnosis, communication and research. Quick and accurate emotion recognition may increase possibilities of computers, robots, and integrated environments to recognize human emotions, and response accordingly to them a social rules. The purpose of this paper is to investigate the possibility of automated emotion representation, recognition and prediction its state-of-the-art and main directions for further research. We focus on the impact of emotion analysis and state of the arts of multimodal emotion detection. We present existing works, possibilities and existing methods to analyze emotion in text, sound, image, video and physiological signals. We also emphasize the most important features for all available emotion recognition modes. Finally, we present the available platform and outlines the existing projects, which deal with multimodal emotion analysis.
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