Automatic Loop Kernel Analysis and Performance Modeling With Kerncraft

Analytic performance models are essential for understanding the performance characteristics of loop kernels, which consume a major part of CPU cycles in computational science. Starting from a validated performance model one can infer the relevant hardware bottlenecks and promising optimization opportunities. Unfortunately, analytic performance modeling is often tedious even for experienced developers since it requires in-depth knowledge about the hardware and how it interacts with the software. We present the "Kerncraft" tool, which eases the construction of analytic performance models for streaming kernels and stencil loop nests. Starting from the loop source code, the problem size, and a description of the underlying hardware, Kerncraft can ideally predict the single-core performance and scaling behavior of loops on multicore processors using the Roofline or the Execution-Cache-Memory (ECM) model. We describe the operating principles of Kerncraft with its capabilities and limitations, and we show how it may be used to quickly gain insights by accelerated analytic modeling.Comment: 11 pages, 4 figures, 8 listing

Reynolds-averaged Navier-Stokes simulation of turbulent flow in a circular pipe using OpenFOAM®

A RANS simulation of flow through a pipe is performed and validated against experimental data and previous DNS results. A mesh refinement study is performed to illustrate the near wall mesh size needed to correctly predict mean flow characteristics. In addition, aspects of the model are changed to study their impact on the results as well as the computational requirements. Comparisons are made between a two-dimensional analysis with axisymmetric boundary conditions, a one-eighth axisymmetric model, a one-fourth axisymmetric model, and a full three-dimensional pipe. The two-dimensional model provides the best match to past data; however, it is noted that the model may not be well tuned for a three-dimensional mesh. The simulation is also performed using three different turbulence models and the results of each model are compared. The purpose of the model is to create a tool that can be used for design iterations. While the model does not fully capture the complexities of turbulent flow, it is able to predict the mean flow accurately enough to be useful in a design setting. The goal of this work is to create a foundation upon which further studies of pipe flow with internal obstructions can build. The overall results show the model is able to predict the mean flow well for the validation case. However, the model does not perform well when certain aspects are changed. Increasing the robustness of the model and the determination of more usable boundary conditions remains a subject for future studies

Modelling the 3D physical structure of astrophysical sources with GASS

The era of interferometric observations leads to the need of a more and more precise description of physical structures and dynamics of star-forming regions, from pre-stellar cores to protoplanetary discs. The molecular emission can be traced in multiple physical components such as infalling envelopes, outflows and protoplanetary discs. To compare with the observations, a precise and complex radiative transfer modelling of these regions is needed. We present GASS (Generator of Astrophysical Sources Structure), a code that allows us to generate the three-dimensional (3D) physical structure model of astrophysical sources. From the GASS graphical interface, the user easily creates different components such as spherical envelopes, outflows and discs. The physical properties of these components are modelled thanks to dedicated graphical interfaces that display various figures in order to help the user and facilitate the modelling task. For each component, the code randomly generates points in a 3D grid with a sample probability weighted by the molecular density. The created models can be used as the physical structure input for 3D radiative transfer codes to predict the molecular line or continuum emission. An analysis of the output hyper-spectral cube given by such radiative transfer code can be made directly in GASS using the various post-treatment options implemented, such as calculation of moments or convolution with a beam. This makes GASS well suited to model and analyse both interferometric and single-dish data. This paper is focused on the results given by the association of GASS and LIME, a 3D radiative transfer code, and we show that the complex geometry observed in star-forming regions can be adequately handled by GASS+LIME

Predicting protein function by machine learning on amino acid sequences – a critical evaluation

Copyright @ 2007 Al-Shahib et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins. Results: Here we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier. Conclusion: We conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function