207,544 research outputs found
Real-time and fault tolerance in distributed control software
Closed loop control systems typically contain multitude of spatially distributed sensors and actuators operated simultaneously. So those systems are parallel and distributed in their essence. But mapping this parallelism onto the given distributed hardware architecture, brings in some additional requirements: safe multithreading, optimal process allocation, real-time scheduling of bus and network resources. Nowadays, fault tolerance methods and fast even online reconfiguration are becoming increasingly important. All those often conflicting requirements, make design and implementation of real-time distributed control systems an extremely difficult task, that requires substantial knowledge in several areas of control and computer science. Although many design methods have been proposed so far, none of them had succeeded to cover all important aspects of the problem at hand. [1] Continuous increase of production in embedded market, makes a simple and natural design methodology for real-time systems needed more then ever
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
FADI: a fault-tolerant environment for open distributed computing
FADI is a complete programming environment that serves the reliable execution of distributed application programs. FADI encompasses all aspects of modern fault-tolerant distributed computing. The built-in user-transparent error detection mechanism covers processor node crashes and hardware transient failures. The mechanism also integrates user-assisted error checks into the system failure model. The nucleus non-blocking checkpointing mechanism combined with a novel selective message logging technique delivers an efficient, low-overhead backup and recovery mechanism for distributed processes. FADI also provides means for remote automatic process allocation on the distributed system nodes
MPICH-G2: A Grid-Enabled Implementation of the Message Passing Interface
Application development for distributed computing "Grids" can benefit from
tools that variously hide or enable application-level management of critical
aspects of the heterogeneous environment. As part of an investigation of these
issues, we have developed MPICH-G2, a Grid-enabled implementation of the
Message Passing Interface (MPI) that allows a user to run MPI programs across
multiple computers, at the same or different sites, using the same commands
that would be used on a parallel computer. This library extends the Argonne
MPICH implementation of MPI to use services provided by the Globus Toolkit for
authentication, authorization, resource allocation, executable staging, and
I/O, as well as for process creation, monitoring, and control. Various
performance-critical operations, including startup and collective operations,
are configured to exploit network topology information. The library also
exploits MPI constructs for performance management; for example, the MPI
communicator construct is used for application-level discovery of, and
adaptation to, both network topology and network quality-of-service mechanisms.
We describe the MPICH-G2 design and implementation, present performance
results, and review application experiences, including record-setting
distributed simulations.Comment: 20 pages, 8 figure
TensorFlow Doing HPC
TensorFlow is a popular emerging open-source programming framework supporting
the execution of distributed applications on heterogeneous hardware. While
TensorFlow has been initially designed for developing Machine Learning (ML)
applications, in fact TensorFlow aims at supporting the development of a much
broader range of application kinds that are outside the ML domain and can
possibly include HPC applications. However, very few experiments have been
conducted to evaluate TensorFlow performance when running HPC workloads on
supercomputers. This work addresses this lack by designing four traditional HPC
benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG)
solver and Fast Fourier Transform (FFT). We analyze their performance on two
supercomputers with accelerators and evaluate the potential of TensorFlow for
developing HPC applications. Our tests show that TensorFlow can fully take
advantage of high performance networks and accelerators on supercomputers.
Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical
communication bandwidth on our testing platform. We find an approximately 2x,
1.7x and 1.8x performance improvement when increasing the number of GPUs from
two to four in the matrix-matrix multiply, CG and FFT applications
respectively. All our performance results demonstrate that TensorFlow has high
potential of emerging also as HPC programming framework for heterogeneous
supercomputers.Comment: Accepted for publication at The Ninth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES'19
An approach to rollback recovery of collaborating mobile agents
Fault-tolerance is one of the main problems that must be resolved to improve the adoption of the agents' computing paradigm. In this paper, we analyse the execution model of agent platforms and the significance of the faults affecting their constituent components on the reliable execution of agent-based applications, in order to develop a pragmatic framework for agent systems fault-tolerance. The developed framework deploys a communication-pairs independent check pointing strategy to offer a low-cost, application-transparent model for reliable agent- based computing that covers all possible faults that might invalidate reliable agent execution, migration and communication and maintains the exactly-one execution property
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