Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption

This paper presents the interesting observation that by performing fewer of the optimizations available in a standard compiler optimization level such as -O2, while preserving their original ordering, significant savings can be achieved in both execution time and energy consumption. This observation has been validated on two embedded processors, namely the ARM Cortex-M0 and the ARM Cortex-M3, using two different versions of the LLVM compilation framework; v3.8 and v5.0. Experimental evaluation with 71 embedded benchmarks demonstrated performance gains for at least half of the benchmarks for both processors. An average execution time reduction of 2.4% and 5.3% was achieved across all the benchmarks for the Cortex-M0 and Cortex-M3 processors, respectively, with execution time improvements ranging from 1% up to 90% over the -O2. The savings that can be achieved are in the same range as what can be achieved by the state-of-the-art compilation approaches that use iterative compilation or machine learning to select flags or to determine phase orderings that result in more efficient code. In contrast to these time consuming and expensive to apply techniques, our approach only needs to test a limited number of optimization configurations, less than 64, to obtain similar or even better savings. Furthermore, our approach can support multi-criteria optimization as it targets execution time, energy consumption and code size at the same time.Comment: 15 pages, 3 figures, 71 benchmarks used for evaluatio

Feedback driven adaptive combinatorial testing

The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice many such behaviors are not actually tested because of masking effects – failures that perturb execution so as to prevent some behaviors from being exercised. In this work we present a feedback-driven, adaptive, combinatorial testing approach aimed at detecting and working around masking effects. At each iteration we detect potential masking effects, heuristically isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We empirically assess the effectiveness of the proposed approach on two large widely used open source software systems. Our results suggest that masking effects do exist and that our approach provides a promising and efficient way to work around them

Training in hypoxia fails to further enhance endurance performance and lactate clearance in well-trained men and impairs glucose metabolism during prolonged exercise.

The aim of this study was to investigate the synergistic effects of endurance training and hypoxia on endurance performance in normoxic and hypoxic conditions (approximately 3000 m above sea level) as well as on lactate and glucose metabolism during prolonged exercise. For this purpose, 14 well-trained cyclists performed 12 training sessions in conditions of normobaric hypoxia (HYP group, n = 7) or normoxia (NOR group, n = 7) over 4 weeks. Before and after training, lactate and glucose turnover rates were measured by infusion of exogenous lactate and stable isotope tracers. Endurance performance was assessed during incremental tests performed in normoxia and hypoxia and a 40 km time trial performed in normoxia. After training, performance was similarly and significantly improved in the NOR and HYP groups (training, P < 0.001) in normoxic conditions. No further effect of hypoxic training was found on markers of endurance performance in hypoxia (training x hypoxia interaction, n.s.). In addition, training and hypoxia had no significant effect on lactate turnover rate. In contrast, there was a significant interaction of training and hypoxia (P < 0.05) on glucose metabolism, as follows: plasma insulin and glucose concentrations were significantly increased; glucose metabolic clearance rate was decreased; and the insulin to glucagon ratio was increased after training in the HYP group. In conclusion, our results show that, compared with training in normoxia, training in hypoxia has no further effect on endurance performance in both normoxic and hypoxic conditions or on lactate metabolic clearance rate. Additionally, these findings suggest that training in hypoxia impairs blood glucose regulation in endurance-trained subjects during exercise