Adaptive Memetic Particle Swarm Optimization with Variable Local Search Pool Size

We propose an adaptive Memetic Particle Swarm Optimization algorithm where local search is selected from a pool of different algorithms. The choice of local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of local search. Our study investigates whether the pool size affects the memetic algorithm’s performance, as well as the possible benefit of using the adaptive strategy against a baseline static one. For this purpose, we employed the memetic algorithms framework provided in the recent MEMPSODE optimization software, and tested the proposed algorithms on the Benchmarking Black Box Optimization (BBOB 2012) test suite. The obtained results lead to a series of useful conclusions

Scheduling Dynamic OpenMP Applications over Multicore Architectures

International audienceApproaching the theoretical performance of hierarchical multicore machines requires a very careful distribution of threads and data among the underlying non-uniform architecture in order to minimize cache misses and NUMA penalties. While it is acknowledged that OpenMP can enhance the quality of thread scheduling on such architectures in a portable way, by transmitting precious information about the affinities between threads and data to the underlying runtime system, most OpenMP runtime systems are actually unable to efficiently support highly irregular, massively parallel applications on NUMA machines. In this paper, we present a thread scheduling policy suited to the execution of OpenMP programs featuring irregular and massive nested parallelism over hierarchical architectures. Our policy enforces a distribution of threads that maximizes the proximity of threads belonging to the same parallel section, and uses a NUMA-aware work stealing strategy when load balancing is needed. It has been developed as a plug-in to the ForestGOMP OpenMP platform. We demonstrate the efficiency of our approach with a highly irregular recursive OpenMP program resulting from the generic parallelization of a surface reconstruction application. We achieve a speedup of 14 on a 16-core machine with no application-level optimization

More green and less blue water in the Alps during warmer summers

Climate change can reduce surface-water supply by enhancing evapotranspiration in forested mountains, especially during heatwaves. We investigate this ‘drought paradox’ for the European Alps using a 1,212-station database and hyper-resolution ecohydrological simulations to quantify blue (runoff) and green (evapotranspiration) water fluxes. During the 2003 heatwave, evapotranspiration in large areas over the Alps was above average despite low precipitation, amplifying the runoff deficit by 32% in the most runoff-productive areas (1,300–3,000 m above sea level). A 3 °C air temperature increase could enhance annual evapotranspiration by up to 100 mm (45 mm on average), which would reduce annual runoff at a rate similar to a 3% precipitation decrease. This suggests that green-water feedbacks—which are often poorly represented in large-scale model simulations—pose an additional threat to water resources, especially in dry summers. Despite uncertainty in the validation of the hyper-resolution ecohydrological modelling with observations, this approach permits more realistic predictions of mountain region water availability

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics

Nested OpenMP Parallelization of a Hierarchical Data Clustering Algorithm

International audienceThis paper presents a high performance parallel implementation of a hierarchical data clustering algorithm. The OpenMP programming model, either enhanced with our lightweight runtime support or through its tasking model, deals with the high irregularity of the algorithm and allows for efficient exploitation of the inherent loop-level nested parallelism. Thorough experimental evaluation demonstrates the performance scalability of our parallelization and the effective utilization of computational resources, which results in a clustering approach able to provide high quality clustering of very large datasets

Portable Runtime Support and Exploitation of Nested Parallelism in OpenMP

In this paper, we present an alternative implementation of the NANOS OpenMP runtime library (NthLib) that targets portability and efficient support of multiple levels of parallelism. We have implemented the runtime libraries of available open-source OpenMP compilers on top of NthLib, reducing thus their overheads and providing them with inherent support for nested parallelism. In addition, we present an experimental implementation of the workqueuing model and the parallelization of a data clustering algorithm using OpenMP directives. The asymmetry and non-determinism of this algorithm necessitate the exploitation of its nested loop-level parallelism. The experimental results on a SMP server with four processors demonstrate our efficient OpenMP runtime support