Real-World Distributed Computer with Ibis

Bal, Henri E.; Maassen, Jason; Nieuwpoort, Rob V. van; Drost, Niels; Kemp, Roelof; Kessel, Timo van; Palmer, Nick; Wrzesinska, G.; Kielmann, Thilo; Reeuwijk, Kees van; Seinstra, F.J.; Jacobs, Ceriel J. H.; Verstoep, Kees

doi:10.1109/mc.2010.184

Cited by 33 publications

(41 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the methods described above, we have implemented a proof-ofconcept prototype called QueryPIE using the Java language and the Ibis framework [1].…”

Section: Discussionmentioning

confidence: 99%

“…As a proof of concept, we have implemented a prototype called QueryPIE and tested its performance. QueryPIE has been built on top of the Ibis framework [1] and it was launched on the DAS-4 cluster with up to 8 machines. As we will describe later in the paper, the results indicate that our algorithms manage to keep the query response time in the order of a few milliseconds over triple stores of up to a billion statements.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

Urbani

Harmelen

Schlobach

et al. 2011

The Semantic Web – ISWC 2011

View full text Add to dashboard Cite

Abstract. Both materialization and backward-chaining as different modes of performing inference have complementary advantages and disadvantages.Materialization enables very efficient responses at query time, but at the cost of an expensive up front closure computation, which needs to be redone every time the knowledge base changes. Backward-chaining does not need such an expensive and change-sensitive precomputation, and is therefore suitable for more frequently changing knowledge bases, but has to perform more computation at query time.Materialization has been studied extensively in the recent semantic web literature, and is now available in industrial-strength systems. In this work, we focus instead on backward-chaining, and we present an hybrid algorithm to perform efficient backward-chaining reasoning on very large datasets expressed in the OWL Horst (pD * ) fragment.As a proof of concept, we have implemented a prototype called QueryPIE (Query Parallel Inference Engine), and we have tested its performance on different datasets of up to 1 billion triples. Our parallel implementation greatly reduces the reasoning complexity of a naive backward-chaining approach and returns results for single query-patterns in the order of milliseconds when running on a modest 8 machine cluster.To the best of our knowledge, QueryPIE is the first reported backward-chaining reasoner for OWL Horst that efficiently scales to a billion triples.

show abstract

“…To evaluate the methods described above, we have implemented a proof-ofconcept prototype called QueryPIE using the Java language and the Ibis framework [1].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

Urbani

Harmelen

Schlobach

et al. 2011

The Semantic Web – ISWC 2011

View full text Add to dashboard Cite

show abstract

“…Ibis [11] is both a programming system and a deployment system for distributed applications. It is composed of a set of APIs allowing users to express applications with different abstractions, such as MPI, master-worker, and workflow.…”

Section: Related Workmentioning

confidence: 99%

The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

Calheiros¹,

Vecchiola²,

Karunamoorthy³

et al. 2012

Future Generation Computer Systems

153

View full text Add to dashboard Cite

a b s t r a c tCloud computing alters the way traditional software systems are built and run by introducing a utilitybased model for delivering IT infrastructure, platforms, applications, and services. The consolidation of this new paradigm in both enterprises and academia demanded reconsideration in the way IT resources are used, so Cloud computing can be used together with available resources. A case for the utilization of Clouds for increasing the capacity of computing infrastructures is Desktop Grids: these infrastructures typically provide best effort execution of high throughput jobs and other workloads that fit the model of the platform. By enhancing Desktop Grid infrastructures with Cloud resources, it is possible to offer QoS to users, motivating the adoption of Desktop Grids as a viable platform for application execution. In this paper, we describe how Aneka, a platform for developing scalable applications on the Cloud, supports such a vision by provisioning resources from different sources and supporting different application models. We highlight the key concepts and features of Aneka that support the integration between Desktop Grids and Clouds and present an experiment showing the performance of this integration.

show abstract

“…Essentially, all of these systems consist of a number of basic compute nodes, each having local memories, and each capable of communicating over a local or wide-area connection. The most prominent differences are in the semantic and administrative organization, with many systems providing their own middlewares, programming interfaces, access policies, and protection mechanisms [4].…”

Section: Jungle Computing Systemsmentioning

confidence: 99%

“…We will claim that many of these fundamental methodologies already exist, and have been integrated in our Ibis software system for high-performance and distributed computing [4]. In other words: Jungle Computing is not just a visionary concept; to a large extent we already adhere to its requirements today.…”

Section: Introductionmentioning

confidence: 99%

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Seinstra

Maassen

Nieuwpoort

et al. 2011

Computer Communications and Networks

Self Cite

View full text Add to dashboard Cite

In recent years, the application of high-performance and distributed computing in scientific practice has become increasingly wide-spread. Among the most widely available platforms to scientists are clusters, grids, and cloud systems. Such infrastructures currently are undergoing revolutionary change due to the integration of many-core technologies, providing orders-of-magnitude speed improvements for selected compute kernels. With high-performance and distributed computing systems thus becoming more heterogeneous and hierarchical, programming complexity is vastly increased. Further complexities arise because urgent desire for scalability, and issues including data distribution, software heterogeneity, and ad-hoc hardware availability, commonly force scientists into simultaneous use of multiple platforms (e.g. clusters, grids, and clouds used concurrently). A true computing jungle. In this chapter we explore the possibilities of enabling efficient and transparent use of Jungle Computing Systems in every-day scientific practice. To this end, we discuss the fundamental methodologies required for defining programming models that are tailored to the specific needs of scientific researchers. Importantly, we claim that many of these fundamental methodologies already exist today, as integrated in our Ibis high-performance distributed programming system. We also make a case for the urgent need for easy and efficient Jungle Computing in scientific practice, by exploring a set of state-of-the-art application domains. For one of these domains we present results obtained with Ibis on a real-world Jungle Computing System. The chapter concludes by exploring fundamental research questions to be investigated in the years to come.

show abstract

Real-World Distributed Computer with Ibis

Cited by 33 publications

References 10 publications

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases

The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Contact Info

Product

Resources

About