Numerical simulations of surface convection in a late M-dwarf

Based on detailed 2D and 3D numerical radiation-hydrodynamics (RHD) simulations of time-dependent compressible convection, we have studied the dynamics and thermal structure of the convective surface layers of a prototypical late-type M-dwarf (Teff~2800K log(g)=5.0, solar chemical composition). The RHD models predict stellar granulation qualitatively similar to the familiar solar pattern. Quantitatively, the granular cells show a convective turn-over time scale of ~100s, and a horizontal scale of 80km; the relative intensity contrast of the granular pattern amounts to 1.1%, and root-mean-square vertical velocities reach 240m/s at maximum. Deviations from radiative equilibrium in the higher, formally convectively stable atmospheric layers are found to be insignificant allowing a reliable modeling of the atmosphere with 1D standard model atmospheres. A mixing-length parameter of alpha=2.1 provides the best representation of the average thermal structure of the RHD model atmosphere while alternative values are found when fitting the asymptotic entropy encountered in deeper layers of the stellar envelope alpha=1.5, or when matching the vertical velocity field alpha=3.5. The close correspondence between RHD and standard model atmospheres implies that presently existing discrepancies between observed and predicted stellar colors in the M-dwarf regime cannot be traced back to an inadequate treatment of convection in the 1D standard models. The RHD models predict a modest extension of the convectively mixed region beyond the formal Schwarzschild stability boundary which provides hints for the distribution of dust grains in cooler (brown dwarf) atmospheres.Comment: 19 pages, 16 figures, accepted for publication in A&

ATCOM: Automatically tuned collective communication system for SMP clusters.

Conventional implementations of collective communications are based on point-to-point communications, and their optimizations have been focused on efficiency of those communication algorithms. However, point-to-point communications are not the optimal choice for modern computing clusters of SMPs due to their two-level communication structure. In recent years, a few research efforts have investigated efficient collective communications for SMP clusters. This dissertation is focused on platform-independent algorithms and implementations in this area;There are two main approaches to implementing efficient collective communications for clusters of SMPs: using shared memory operations for intra-node communications, and over-lapping inter-node/intra-node communications. The former fully utilizes the hardware based shared memory of an SMP, and the latter takes advantage of the inherent hierarchy of the communications within a cluster of SMPs. Previous studies focused on clusters of SMP from certain vendors. However, the previously proposed methods are not portable to other systems. Because the performance optimization issue is very complicated and the developing process is very time consuming, it is highly desired to have self-tuning, platform-independent implementations. As proven in this dissertation, such an implementation can significantly outperform the other point-to-point based portable implementations and some platform-specific implementations;The dissertation describes in detail the architecture of the platform-independent implementation. There are four system components: shared memory-based collective communications, overlapping mechanisms for inter-node and intra-node communications, a prediction-based tuning module and a micro-benchmark based tuning module. Each component is carefully designed with the goal of automatic tuning in mind

Elucidating the druggability of the human proteome with eFindSite

© 2019, Springer Nature Switzerland AG. Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and resources. Inability to properly measure druggability has accounted for a significant portion of failures in drug discovery. This problem is only further exacerbated by the large sample space of proteins involved in human diseases. With these barriers, the druggability space within the human proteome remains unexplored and has made it difficult to develop drugs for numerous diseases. Hence, we present a new feature developed in eFindSite that employs supervised machine learning to predict the druggability of a given protein. Benchmarking calculations against the Non-Redundant data set of Druggable and Less Druggable binding sites demonstrate that an AUC for druggability prediction with eFindSite is as high as 0.88. With eFindSite, we elucidated the human druggability space to be 10,191 proteins. Considering the disease space from the Open Targets Platform and excluding already known targets from the predicted data set reveal 2731 potentially novel therapeutic targets. eFindSite is freely available as a stand-alone software at https://github.com/michal-brylinski/efindsite

Predictive analysis and optimisation of pipelined wavefront applications using reusable analytic models

Pipelined wavefront computations are an ubiquitous class of high performance parallel algorithms used for the solution of many scientific and engineering applications. In order to aid the design and optimisation of these applications, and to ensure that during procurement platforms are chosen best suited to these codes, there has been considerable research in analysing and evaluating their operational performance. Wavefront codes exhibit complex computation, communication, synchronisation patterns, and as a result there exist a large variety of such codes and possible optimisations. The problem is compounded by each new generation of high performance computing system, which has often introduced a previously unexplored architectural trait, requiring previous performance models to be rewritten and reevaluated. In this thesis, we address the performance modelling and optimisation of this class of application, as a whole. This differs from previous studies in which bespoke models are applied to specific applications. The analytic performance models are generalised and reusable, and we demonstrate their application to the predictive analysis and optimisation of pipelined wavefront computations running on modern high performance computing systems. The performance model is based on the LogGP parameterisation, and uses a small number of input parameters to specify the particular behaviour of most wavefront codes. The new parameters and model equations capture the key structural and behavioural differences among different wavefront application codes, providing a succinct summary of the operations for each application and insights into alternative wavefront application design. The models are applied to three industry-strength wavefront codes and are validated on several systems including a Cray XT3/XT4 and an InfiniBand commodity cluster. Model predictions show high quantitative accuracy (less than 20% error) for all high performance configurations and excellent qualitative accuracy. The thesis presents applications, projections and insights for optimisations using the model, which show the utility of reusable analytic models for performance engineering of high performance computing codes. In particular, we demonstrate the use of the model for: (1) evaluating application configuration and resulting performance; (2) evaluating hardware platform issues including platform sizing, configuration; (3) exploring hardware platform design alternatives and system procurement and, (4) considering possible code and algorithmic optimisations

Assessing the environmental risks associated with contaminated sites

A risk assessment strategy considering the impact of chemicals on the whole ecosystem has been developed in order to create a sound and useful method for quantifying and comparing global risks posed by the main different hazardous chemicals found in the environment. This index, called the Environmental Risk Index for a Complete Assessment (ERICA), merges in a single number the environmental assessment, the human health risk assessment and the uncertainty caused by missing or unreliable data. ERICA uses a scoring system with parameters for the main characteristics of the pollutants. The main advantage is that it preserves a simple approach by condensing in this single value an analysis of the risk for the area under observation. The availability and reliability of the data is an important part of the work done to build the index. Experimental and predictive data were compared to evaluate the reliability. Data were derived both from literature sources (experimental models mainly) and predictive models. ERICA can be considered a diagnostic and prognostic tool for environmental contaminants in critical and potentially dangerous sites, such as incinerators, landfills and industrial areas or in broader geographical areas. The application of the proposed integrated index provides a preliminary quantitative analysis of possible environmental alerts due to the presence of one or more pollutants in the investigated site. This thesis presents the method and the equations behind the index and a first case study based on the Italian legislation and a pilot study on a location on the Italian seacoast

Toward optimised skeletons for heterogeneous parallel architecture with performance cost model

High performance architectures are increasingly heterogeneous with shared and distributed memory components, and accelerators like GPUs. Programming such architectures is complicated and performance portability is a major issue as the architectures evolve. This thesis explores the potential for algorithmic skeletons integrating a dynamically parametrised static cost model, to deliver portable performance for mostly regular data parallel programs on heterogeneous archi- tectures. The rst contribution of this thesis is to address the challenges of program- ming heterogeneous architectures by providing two skeleton-based programming libraries: i.e. HWSkel for heterogeneous multicore clusters and GPU-HWSkel that enables GPUs to be exploited as general purpose multi-processor devices. Both libraries provide heterogeneous data parallel algorithmic skeletons including hMap, hMapAll, hReduce, hMapReduce, and hMapReduceAll. The second contribution is the development of cost models for workload dis- tribution. First, we construct an architectural cost model (CM1) to optimise overall processing time for HWSkel heterogeneous skeletons on a heterogeneous system composed of networks of arbitrary numbers of nodes, each with an ar- bitrary number of cores sharing arbitrary amounts of memory. The cost model characterises the components of the architecture by the number of cores, clock speed, and crucially the size of the L2 cache. Second, we extend the HWSkel cost model (CM1) to account for GPU performance. The extended cost model (CM2) is used in the GPU-HWSkel library to automatically nd a good distribution for both a single heterogeneous multicore/GPU node, and clusters of heteroge- neous multicore/GPU nodes. Experiments are carried out on three heterogeneous multicore clusters, four heterogeneous multicore/GPU clusters, and three single heterogeneous multicore/GPU nodes. The results of experimental evaluations for four data parallel benchmarks, i.e. sumEuler, Image matching, Fibonacci, and Matrix Multiplication, show that our combined heterogeneous skeletons and cost models can make good use of resources in heterogeneous systems. Moreover using cores together with a GPU in the same host can deliver good performance either on a single node or on multiple node architectures

New insights on the pathomechanism of GNE myopathy: proposing an immune-mediated response

Glycosylation is known to be involved in several biological functions, and defects in the synthesis or attachment of sugars can modulate the course of various malignancies. GNE myopathy (GNEM) is an ultra-rare congenital disorder of glycosylation caused by biallelic mutations in the GNE gene, which encodes for a bifunctional enzyme required for sialic acid biosynthesis. Although, hyposialylation has been assumed as the main cause of this myopathy, new data suggest that GNEM mechanism is far more complicated. To date, there is no approved treatment for GNEM and research on sialylation-increasing therapies is challenged by unknown processes and the absence of biomarkers. In this work we explored cellular and molecular mechanisms that may contribute to this myopathy as a means of identifying alternative therapeutic targets and biomarkers. Previous studies have identified that sialic acid removal alters the expression of some immune agents; this led us to study whether defective sialylation in the GNE knock-out cell model influences immune function. The overexpression of major histocompatibility complex class-I (MHC-I) and cytokine secretion levels in GNEM cell model point towards the involvement of an immune response and suggest that immune players could be good disease biomarkers in diagnostic and clinical development. Furthermore, drug-likeliness of newly synthesized compounds (based on prodrug technology) was computationally analysed, and assays to evaluate the in vitro toxicity and efficacy of the prodrugs were designed and optimized, using N-acetyl-D-mannosamine currently in phase 2 of clinical trials and N-acetyl-D-mannosamine-6-phosphate (parent compound). Although no restoring of the sialic acid content was observed with both compounds, a small recovery of immune parameters suggests the involvement of pathways other than sialylation. Overall, understanding the immune response in GNEM could bring some light into pathophysiology and accelerate the approval of a new therapeutic option.A glicosilação está envolvida em várias funções biológicas. Defeitos na síntese ou fixação de monossacáridos podem modular o desenvolvimento de várias doenças. A miopatia GNE (GNEM) é uma doença congénita da glicosilação ultra-rara causada por mutações bialélicas no gene GNE, que codifica uma enzima bifuncional crucial para a biossíntese de ácido siálico. Embora se assuma a hiposialilação como a principal causa desta miopatia, novas evidências sugerem que o mecanismo de doença é mais complexo. Atualmente, não existe nenhum tratamento aprovado para GNEM e a investigação em torno de terapias que aumentam a sialilação é dificultada por mecanismos desconhecidos e ausência de biomarcadores. Neste trabalho explorámos mecanismos que podem contribuir para a GNEM no sentido de identificar alvos terapêuticos alternativos e biomarcadores. Estudos anteriores identificaram que a remoção de ácido siálico altera a expressão de alguns agentes imunes, o que nos levou a estudar se a função imune está alterada no modelo celular com o gene GNE knock-out. A sobre-expressão do complexo principal de histocompatibilidade classe I (MHC-I) e os níveis de secreção de citocinas observados no modelo celular de GNEM apontam para o envolvimento de uma resposta imune e sugerem que moléculas imunes podem ser bons biomarcadores no diagnóstico e desenvolvimento clínico. Além disso, as propriedades “drug-like” de novos compostos sintetizados (pró-fármacos) foram analisadas computacionalmente, e ensaios in vitro para avaliar a toxicidade e a eficácia dos pró- fármacos foram otimizados, usando N-acetil-D-manosamina atualmente na fase 2 de ensaios clínicos e N-acetil-D-manosamina-6-fosfato (princípio ativo). Embora nenhuma recuperação da sialilação tenha sido observada com os compostos, houve uma pequena recuperação dos parâmetros imunológicos, sugerindo o envolvimento de outras vias além da sialilação. Em jeito de conclusão, entender a resposta imune na GNEM pode trazer mais entendimento da sua fisiopatologia e acelerar a aprovação de novas e melhores opções terapêuticas