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 audienceThis paper provides both theoretical and experimental evidence for the existence of an Energy/Frequency Convexity Rule, which relates energy consumption and CPU frequency on mobile devices. We monitored a typical smartphone running a speci c computing-intensive kernel of multiple nested loops written in C using a high-resolution power gauge. Data gathered during a week-long acquisition campaign suggest that energy consumed per input element is strongly correlated with CPU frequency, and, more interestingly, the curve exhibits a clear minimum over a 0.2 GHz to 1.6 GHz window. We provide and motivate an analytical model for this behavior, which ts well with the data. Our work should be of clear interest to researchers focusing on energy usage and minimization for mobile devices, and provide new insights for optimization opportunities

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

Abstract-We introduce and experimentally validate a new macro-level model of the CPU temperature/power relationship within nanometer-scale application processors or system-onchips. By adopting a holistic view, this model is able to take into account many of the physical effects that occur within such systems. Together with two algorithms described in the paper, our results can be used, for instance by engineers designing power or thermal management units, to cancel the temperatureinduced bias on power measurements. This will help them gather temperature-neutral power data while running multiple instance of their benchmarks. Also power requirements and system failure rates can be decreased by controlling the CPU's thermal behavior.Even though it is usually assumed that the temperature/power relationship is exponentially related, there is however a lack of publicly available physical temperature/power measurements to back up this assumption, something our paper corrects. Via measurements on two pertinent platforms sporting nanometerscale application processors, we show that the power/temperature relationship is indeed very likely exponential over a 20• C to 85• C temperature range. Our data suggest that, for application processors operating between 20• C and 50• C, a quadratic model is still accurate and a linear approximation is acceptable.