39,565 research outputs found
Simplified Distributed Programming with Micro Objects
Developing large-scale distributed applications can be a daunting task.
object-based environments have attempted to alleviate problems by providing
distributed objects that look like local objects. We advocate that this
approach has actually only made matters worse, as the developer needs to be
aware of many intricate internal details in order to adequately handle partial
failures. The result is an increase of application complexity. We present an
alternative in which distribution transparency is lessened in favor of clearer
semantics. In particular, we argue that a developer should always be offered
the unambiguous semantics of local objects, and that distribution comes from
copying those objects to where they are needed. We claim that it is often
sufficient to provide only small, immutable objects, along with facilities to
group objects into clusters.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499
FRIENDS - A flexible architecture for implementing fault tolerant and secure distributed applications
FRIENDS is a software-based architecture for implementing fault-tolerant and, to some extent, secure applications. This architecture is composed of sub-systems and libraries of metaobjects. Transparency and separation of concerns is provided not only to the application programmer but also to the programmers implementing metaobjects for fault tolerance, secure communication and distribution. Common services required for implementing metaobjects are provided by the sub-systems. Metaobjects are implemented using object-oriented techniques and can be reused and customised according to the application needs, the operational environment and its related fault assumptions. Flexibility is increased by a recursive use of metaobjects. Examples and experiments are also described
Implementing fault tolerant applications using reflective object-oriented programming
Abstract: Shows how reflection and object-oriented programming can be used to ease the implementation of classical fault tolerance mechanisms in distributed applications. When the underlying runtime system does not provide fault tolerance transparently, classical approaches to implementing fault tolerance mechanisms often imply mixing functional programming with non-functional programming (e.g. error processing mechanisms). The use of reflection improves the transparency of fault tolerance mechanisms to the programmer and more generally provides a clearer separation between functional and non-functional programming. The implementations of some classical replication techniques using a reflective approach are presented in detail and illustrated by several examples, which have been prototyped on a network of Unix workstations. Lessons learnt from our experiments are drawn and future work is discussed
Engineering simulations for cancer systems biology
Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions
Modelling and simulation framework for reactive transport of organic contaminants in bed-sediments using a pure java object - oriented paradigm
Numerical modelling and simulation of organic contaminant reactive transport in the environment is being increasingly
relied upon for a wide range of tasks associated with risk-based decision-making, such as prediction of contaminant
profiles, optimisation of remediation methods, and monitoring of changes resulting from an implemented remediation
scheme. The lack of integration of multiple mechanistic models to a single modelling framework, however, has
prevented the field of reactive transport modelling in bed-sediments from developing a cohesive understanding of
contaminant fate and behaviour in the aquatic sediment environment. This paper will investigate the problems involved
in the model integration process, discuss modelling and software development approaches, and present preliminary
results from use of CORETRANS, a predictive modelling framework that simulates 1-dimensional organic contaminant
reaction and transport in bed-sediments
Characterizing Deep-Learning I/O Workloads in TensorFlow
The performance of Deep-Learning (DL) computing frameworks rely on the
performance of data ingestion and checkpointing. In fact, during the training,
a considerable high number of relatively small files are first loaded and
pre-processed on CPUs and then moved to accelerator for computation. In
addition, checkpointing and restart operations are carried out to allow DL
computing frameworks to restart quickly from a checkpoint. Because of this, I/O
affects the performance of DL applications. In this work, we characterize the
I/O performance and scaling of TensorFlow, an open-source programming framework
developed by Google and specifically designed for solving DL problems. To
measure TensorFlow I/O performance, we first design a micro-benchmark to
measure TensorFlow reads, and then use a TensorFlow mini-application based on
AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow.
To improve the checkpointing performance, we design and implement a burst
buffer. We find that increasing the number of threads increases TensorFlow
bandwidth by a maximum of 2.3x and 7.8x on our benchmark environments. The use
of the tensorFlow prefetcher results in a complete overlap of computation on
accelerator and input pipeline on CPU eliminating the effective cost of I/O on
the overall performance. The use of a burst buffer to checkpoint to a fast
small capacity storage and copy asynchronously the checkpoints to a slower
large capacity storage resulted in a performance improvement of 2.6x with
respect to checkpointing directly to slower storage on our benchmark
environment.Comment: Accepted for publication at pdsw-DISCS 201
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