1,004 research outputs found
Parallel Stochastic Simulators in System Biology: The Evolution of the Species
No full textThe stochastic simulation of biological systems is an increasingly popular technique in Bioinformatics. It is often an enlightening technique, especially for multi-stable systems which dynamics can be hardly captured with ordinary differential equations. To be effective, stochastic simulations should be supported by powerful statistical analysis tools. The simulation-analysis workflow may however result in being computationally expensive, thus compromising the interactivity required in model tuning. In this work we advocate the high-level design of simulators for stochastic systems as a vehicle for building efficient and portable parallel simulators. In particular, the Calculus of Wrapped Components (CWC) simulator, which is designed according to the FastFlow's pattern-based approach, is presented and discussed in this work. FastFlow has been extended to support also clusters of multi-cores with minimal coding effort, assessing the portability of the approach
On Designing Multicore-Aware Simulators for Biological Systems
No full textThe stochastic simulation of biological systems is an increasingly popular technique in bioinformatics. It often is an enlightening technique, which may however result in being computational expensive. We discuss the main opportunities to speed it up on multi-core platforms, which pose new challenges for parallelisation techniques. These opportunities are developed in two general families of solutions involving both the single simulation and a bulk of independent simulations (either replicas of derived from parameter sweep). Proposed solutions are tested on the parallelisation of the CWC simulator (Calculus of Wrapped Compartments) that is carried out according to proposed solutions by way of the Fast Flow programming framework making possible fast development and efficient execution on multi-cores
Simulation techniques for the calculus of wrapped compartments
No full textAbstractThe modelling and analysis of biological systems has deep roots in Mathematics, specifically in the field of Ordinary Differential Equations (ODEs). Alternative approaches based on formal calculi, often derived from process algebras or term rewriting systems, provide a quite complementary way to analyse the behaviour of biological systems. These calculi allow to cope in a natural way with notions like compartments and membranes, which are not easy (sometimes impossible) to handle with purely numerical approaches, and are often based on stochastic simulation methods. Recently, it has also become evident that stochastic effects in regulatory networks play a crucial role in the analysis of such systems. Actually, in many situations it is necessary to use stochastic models. For example when the system to be described is based on the interaction of few molecules, when we are at the presence of a chemical instability, or when we want to simulate the functioning of a pool of entities whose compartmentalised structure evolves dynamically. In contrast, stable metabolic networks, involving a large number of reagents, for which the computational cost of a stochastic simulation becomes an insurmountable obstacle, are efficiently modelled with ODEs. In this paper we define a hybrid simulation method, combining the stochastic approach with ODEs, for systems described in the Calculus of Wrapped Compartments (CWC), a calculus on which we can express the compartmentalisation of a biological system whose evolution is defined by a set of rewrite rules
Hybrid Calculus of Wrapped Compartments
No full textThe modelling and analysis of biological systems has deep roots in Mathematics, specifically in the field of ordinary differential equations (ODEs). Alternative approaches based on formal calculi, often derived from process algebras or term rewriting systems, provide a quite complementary way to analyze the behaviour of biological systems. These calculi allow to cope in a natural way with notions like compartments and membranes, which are not easy (sometimes impossible) to handle with purely numerical approaches, and are often based on stochastic simulation methods. Recently, it has also become evident that stochastic effects in regulatory networks play a crucial role in the analysis of such systems. Actually, in many situations it is necessary to use stochastic models. For example when the system to be described is based on the interaction of few molecules, when we are at the presence of a chemical instability, or when we want to simulate the functioning of a pool of entities whose compartmentalised structure evolves dynamically. In contrast, stable metabolic networks, involving a large number of reagents, for which the computational cost of a stochastic simulation becomes an insurmountable obstacle, are efficiently modelled with ODEs. In this paper we define a hybrid simulation method, combining the stochastic approach with ODEs, for systems described in CWC, a calculus on which we can express the compartmentalisation of a biological system whose evolution is defined by a set of rewrite rules
CWC parallel simulator with on-line statistics
No full textThe CWC simulator is a simulation tool for the Calculus of Wrapped Compartments (CWC) that is based on Gillespie’s direct method supporting the simulation and the analysis simulating biological phenomena. The CWC simulator is parallel application able to exploit the full power of multicore platforms by way of the fastflow lock-free C++ library. Starting from version 0.6, the CWC simulator includes a powerful parallel engine for computing on-line statistics and mining of simulation outputs
Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern
No full textIn this paper, a highly effective parallel filter for visual data restoration is presented. The filter is designed following a skeletal approach, using a newly proposed stencil-reduce, and has been implemented by way of the FastFlow parallel programming library. As a result of its high-level design, it is possible to run the filter seamlessly on a multicore machine, on multi-GPGPUs, or on both. The design and implementation of the filter are discussed, and an experimental evaluation is presented
Stochastic Calculus of Wrapped Compartments
No full textThe Calculus of Wrapped Compartments (CWC) is a variant of the Calculus of Looping Sequences (CLS). While keeping the same expressiveness, CWC strongly simplifies the development of automatic tools for the analysis of biological systems. The main simplification consists in the removal of the sequencing operator, thus lightening the formal treatment of the patterns to be matched in a term (whose complexity in CLS is strongly affected by the variables matching in the sequences). We define a stochastic semantics for this new calculus. As an application we model the interaction between macrophages and apoptotic neutrophils and a mechanism of gene regulation in E.Coli
NuChart-II: The road to a fast and scalable tool for Hi-C data analysis
No full textRecent advances in molecular biology and bioinformatic techniques have brought about an explosion of information about the spatial organisation of the DNA in the nucleus of a cell. High-throughput molecular biology techniques provide a genome-wide capture of the spatial organisation of chromosomes at unprecedented scales, which permit one to identify physical interactions between genetic elements located throughout a genome. This important information is, however, hampered by the lack of biologist-friendly analysis and visualisation software: these disciplines are literally caught in a flood of data and are now facing many of the scale-out issues that high-performance computing has been addressing for years. Data must be managed, analysed and integrated, with substantial requirements of speed (in terms of execution time), application scalability and data representation. In this work, we present NuChart-II, an efficient and highly optimised tool for genomic data analysis that provides a gene-centric, graph-based representation of genomic information and which proposes an ex-post normalisation technique for Hi-C data. While designing NuChart-II, we addressed several common issues in the parallelisation of memory-bound algorithms for shared-memory systems
PiCo: a novel approach to stream data analytics
No full textIn this paper, we present a new C++ API with a fluent interface called PiCo (Pipeline Composition). PiCo’s programming model aims at making easier the programming of data analytics applications while preserving or enhancing their performance. This is attained through three key design choices: 1) unifying batch and stream data access models, 2) decoupling processing from data layout, and 3) exploiting a stream-oriented, scalable, efficient C++11 runtime system. PiCo proposes a programming model based on pipelines and operators that are polymorphic with respect to data types in the sense that it is possible to re-use the same algorithms and pipelines on different data models (e.g., streams, lists, sets, etc.). Preliminary results show that PiCo can attain better performances in terms of execution times and hugely improve memory utilization when compared to Spark and Flink in both batch and stream processing.Author's copy (postprint) of C. Misale, M. Drocco, G. Tremblay, and M. Aldinucci, "PiCo: a Novel Approach to Stream Data Analytics," in Proc. of Euro-Par Workshops: 1st Intl. Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017), Santiago de Compostela, Spain, 2018. doi:10.1007/978-3-319-75178-8_1
The Loop-of-Stencil-Reduce Paradigm
No full textIn this paper we advocate the Loop-of-stencil-reduce pattern as a way to simplify the parallel programming of heterogeneous platforms (multicore+GPUs). Loop-of-Stencil-reduce is general enough to subsume map, reduce, map-reduce, stencil, stencil-reduce, and, crucially, their usage in a loop. It transparently targets (by using OpenCL) combinations of CPU cores and GPUs, and it makes it possible to simplify the deployment of a single stencil computation kernel on different GPUs. The paper discusses the implementation of Loop-of-stencil-reduce within the FastFlow parallel framework, considering a simple iterative data-parallel application as running example (Game of Life) and a highly effective parallel filter for visual data restoration to assess performance. Thanks to the high-level design of the Loop-of-stencil-reduce, it was possible to run the filter seamlessly on a multicore machine, on multi-GPUs, and on both.</p
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