4,257 research outputs found
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the rst six months. The project aim is to scale the Erlang's radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the e ectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
Using Java for distributed computing in the Gaia satellite data processing
In recent years Java has matured to a stable easy-to-use language with the
flexibility of an interpreter (for reflection etc.) but the performance and
type checking of a compiled language. When we started using Java for
astronomical applications around 1999 they were the first of their kind in
astronomy. Now a great deal of astronomy software is written in Java as are
many business applications.
We discuss the current environment and trends concerning the language and
present an actual example of scientific use of Java for high-performance
distributed computing: ESA's mission Gaia. The Gaia scanning satellite will
perform a galactic census of about 1000 million objects in our galaxy. The Gaia
community has chosen to write its processing software in Java. We explore the
manifold reasons for choosing Java for this large science collaboration.
Gaia processing is numerically complex but highly distributable, some parts
being embarrassingly parallel. We describe the Gaia processing architecture and
its realisation in Java. We delve into the astrometric solution which is the
most advanced and most complex part of the processing. The Gaia simulator is
also written in Java and is the most mature code in the system. This has been
successfully running since about 2005 on the supercomputer "Marenostrum" in
Barcelona. We relate experiences of using Java on a large shared machine.
Finally we discuss Java, including some of its problems, for scientific
computing.Comment: Experimental Astronomy, August 201
Design considerations for workflow management systems use in production genomics research and the clinic
Abstract The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance
Group Communication Patterns for High Performance Computing in Scala
We developed a Functional object-oriented Parallel framework (FooPar) for
high-level high-performance computing in Scala. Central to this framework are
Distributed Memory Parallel Data structures (DPDs), i.e., collections of data
distributed in a shared nothing system together with parallel operations on
these data. In this paper, we first present FooPar's architecture and the idea
of DPDs and group communications. Then, we show how DPDs can be implemented
elegantly and efficiently in Scala based on the Traversable/Builder pattern,
unifying Functional and Object-Oriented Programming. We prove the correctness
and safety of one communication algorithm and show how specification testing
(via ScalaCheck) can be used to bridge the gap between proof and
implementation. Furthermore, we show that the group communication operations of
FooPar outperform those of the MPJ Express open source MPI-bindings for Java,
both asymptotically and empirically. FooPar has already been shown to be
capable of achieving close-to-optimal performance for dense matrix-matrix
multiplication via JNI. In this article, we present results on a parallel
implementation of the Floyd-Warshall algorithm in FooPar, achieving more than
94 % efficiency compared to the serial version on a cluster using 100 cores for
matrices of dimension 38000 x 38000
A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction
This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization
A taxonomy of the quality attributes for distributed applications
The software engineering community has paid little attention to non-functional requirements, or quality attributes, compared with studies performed on capture, analysis and validation of functional requirements.
This circumstance becomes more intense in the case of distributed applications. In these applications we have to take into account, besides the quality attributes such as correctness, robustness, extendibility, reusability, compatibility, efficiency, portability and ease of use, others like reliability, scalability, transparency, security, interoperability, concurrency, etc.
In this work we will show how these last attributes are related to different abstractions that coexist in the problem domain. To achieve this goal, we have established a taxonomy of quality attributes of distributed applications and have determined the set of necessary services to support such attributes
A Test Suite for High-Performance Parallel Java
The Java programming language has a number of features that make it attractive for writing high-quality, portable parallel programs. A pure object formulation, strong typing and the exception model make programs easier to create, debug, and maintain. The elegant threading provides a simple route to parallelism on shared-memory machines. Anticipating great improvements in numerical performance, this paper presents a suite of simple programs that indicate how a pure Java Navier-Stokes solver might perform. The suite includes a parallel Euler solver. We present results from a 32-processor Hewlett-Packard machine and a 4-processor Sun server. While speedup is excellent on both machines, indicating a high-quality thread scheduler, the single-processor performance needs much improvement
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