We present a free web application for the calculation of the buried volume (% V Bur ) of NHC ligands. The web application provides a graphic and user-friendly interface to the SambVca program, developed for the calculation of % V Bur values not only of NHC ligands but also of other classic organometallic ligands such as, for example, phosphanes and cyclopentadienyl-based ligands. To provide a reliable pro-

Molecular dynamics simulations of liquid ethylene glycol described by the OPLS-AA force field were performed to gain insight into its hydrogen-bond structure. We use the population correlation function as a statistical measure for the hydrogen-bond lifetime. In an attempt to understand the complicated hydrogen-bonding, we developed new molecular visualization tools within the Vish Visualization shell and used it to visualize the life of each individual hydrogen-bond. With this tool hydrogen-bond formation and breaking as well as clustering and chain formation in hydrogen-bonded liquids can be observed directly. Liquid ethylene glycol at room temperature does not show significant clustering or chain building. The hydrogen-bonds break often due to the rotational and vibrational motions of the molecules leading to an H-bond half-life time of approximately 1.5 ps. However, most of the H-bonds are reformed again so that after 50 ps only 40% of these H-bonds are irreversibly broken due to diffusional motion. This hydrogen-bond half-life time due to diffusional motion is 80.3 ps. The work was preceded by a careful check of various OPLS-based force fields used in the literature. It was found that they lead to quite different angular and H-bond distributions.

Kofler, K.; Grasso, I.; Cosenza, B.; Fahringer, T.: An automatic input-sensitive approach for heterogeneous task partitioning. ABSTRACTUnleashing the full potential of heterogeneous systems, consisting of multi-core CPUs and GPUs, is a challenging task due to the difference in processing capabilities, memory availability, and communication latencies of different computational resources.In this paper we propose a novel approach that automatically optimizes task partitioning for different (input) problem sizes and different heterogeneous multi-core architectures. We use the Insieme source-to-source compiler to translate a single-device OpenCL program into a multi-device OpenCL program. The Insieme Runtime System then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach based on Artificial Neural Networks (ANN) that incorporates static program features as well as dynamic, input sensitive features. Principal component analysis have been used to further improve the task partitioning. Our approach has been evaluated over a suite of 23 programs and respectively achieves a performance improvement of 22% and 25% compared to an execution of the benchmarks on a single CPU and a single GPU which is equal to 87.5% of the optimal performance.

We focus on agent-based simulations where a large number of agents move in the space, obeying to some simple rules. Since such kind of simulations are computational intensive, it is challenging, for such a contest, to let the number of agents to grow and to increase the quality of the simulation. A fascinating way to answer to this need is by exploiting parallel architectures.In this paper, we present a novel distributed load balancing schema for a parallel implementation of such simulations. The purpose of such schema is to achieve an high scalability. Our approach to load balancing is designed to be lightweight and totally distributed: the calculations for the balancing take place at each computational step, and influences the successive step.To the best of our knowledge, our approach is the first distributed load balancing schema in this context.We present both the design and the implementation that allowed us to perform a number of experiments, with up-to 1, 000, 000 agents. Tests show that, in spite of the fact that the load balancing algorithm is local, the workload distribution is balanced while the communication overhead is negligible.

Dynamic voltage and frequency scaling (DVFS) is an important solution to balance performance and energy consumption, and hardware vendors provide management libraries that allow the programmer to change both memory and core frequencies. The possibility to manually set these frequencies is a great opportunity for application tuning, which can focus on the best applicationdependent setting. However, this task is not straightforward because of the large set of possible configurations and because of the multi-objective nature of the problem, which minimizes energy consumption and maximizes performance. This paper proposes a method to predict the best core and memory frequency configurations on GPUs for an input OpenCL kernel. Our modeling approach, based on machine learning, first predicts speedup and normalized energy over the default frequency configuration. Then, it combines the two models into a multi-objective one that predicts a Pareto-set of frequency configurations. The approach uses static code features, is built on a set of carefully designed microbenchmarks, and can predict the best frequency settings of a new kernel without executing it. Test results show that our modeling approach is very accurate on predicting extrema points and Pareto set for ten out of twelve test benchmarks, and discover frequency configurations that dominate the default configuration in either energy or performance. CCS CONCEPTS • Computer systems organization → Parallel architectures; • Hardware → Power and energy.