Heterogeneity as key feature of high performance computing: the PQE1 prototype

Palazzari, P.; Arrcipiani, L.; Celino, Massimo; Guadagni, R.; Marongiu, A.; Mathis, Agostino; Novelli, Paolo; Rosato, Vittorio

doi:10.1109/hcw.2000.843729

Cited by 12 publications

(6 citation statements)

References 28 publications

(26 reference statements)

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“…RELATED WORK This paper presents a different approach for the integration of heterogeneous resources within a HPC system. In fact, the actual idea is similar to the concept behind the development of the Quadrics Supercomputing World's PQE 2000 system in the late 1990s [16]. Here the role of the Cluster as a more general purpose system was filled by a Meiko CS2 system utilizing SPARC processors and a proprietary interconnect build by Meiko.…”

Section: Benchmarking Resultsmentioning

confidence: 99%

Application Performance on a Cluster-Booster System

Kreuzer¹,

Eicker²,

Amaya

et al. 2018

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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The DEEP projects have developed a variety of hardware and software technologies aiming at improving the efficiency and usability of next generation high-performance computers. They evolve around an innovative concept for heterogeneous systems: the Cluster-Booster architecture. In it, a general purpose cluster is tightly coupled to a many-core system (the Booster). This modular way of integrating heterogeneous components enables applications to freely choose the kind of computing resources on which it runs most efficiently. Codes might even be partitioned to map specific requirements of codeparts onto the best suited hardware. This paper presents for the first time measurements done by a real world scientific application demonstrating the performance gain achieved with this kind of code-partition approach.

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Section: Benchmarking Resultsmentioning

confidence: 99%

Application Performance on a Cluster-Booster System

Kreuzer¹,

Eicker²,

Amaya

et al. 2018

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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“…In [13], the authors introduce the PQE1 Prototype which is intended for simulation of complex physical models and strains to develop highly specialized heterogeneous systems for scientific computing. It maps taskheterogeneity to system-heterogeneity using a hierarchical model.…”

Section: Previous Workmentioning

confidence: 99%

Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

Duselis

Cauich

Wang

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

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This paper provides a framework for dynamic adaptive computing in heterogeneous clusters for computationally intensive applications. The framework considers a set of discoverable interconnected computational resources and either a parallel or sequential workload needing to be executed. An adaptive inclusion/exclusion algorithm is used to select the resources by using novel performance measurements and profiling techniques. Furthermore, contrary to a greedy approach where all the resources are seized for the workload application, our framework only harnesses the best fit resources measured against system-wide performance characterization, and is contingent upon the current workload definition. The intelligent selection of a subset of resources has proven to achieve better performance; especially in environments with a high level of heterogeneity where the characteristics of some resources may not achieve the best performance the cluster can provide. Additionally, this paper provides a novel analysis of the workload and cluster characteristics, exhibiting analytical starting points to be used in the resource selection.

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“…In [15], the authors introduce the PQE1 Prototype which is intended for simulation of complex physical models and strains to develop highly specialized heterogeneous systems for scientific computing. It maps taskheterogeneity to system-heterogeneity using a hierarchical model.…”

Section: A Prior Workmentioning

confidence: 99%

Concurrent adaptive computing in heterogeneous environments (CACHE)

Duselis

Scherson

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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We introduce a computational framework for concurrent adaptive computing in heterogeneous environments for computationally intensive applications. This framework considers the presence of inter-connected computational resources which are discoverable and a workload which needs to be executed either by concurrent means or on a singular resource. The selection of resources, using a novel measurement of performance, leads to the adaptive inclusion/exclusion of resources to be used in the efficient execution of workload computations. The adaptive approach is that it makes a determination to include a proper subset of resources for system inclusion to execute a workload, which is contrary (except when the subset is allinclusive) to a greedy approach where all the resources are seized for the workload application. The selection of a subset of resources may be more efficient due to the high level of heterogeneity of the resources, where, for some resources, certain resource selections may be detrimental or have no value to send work there. Furthermore, this framework aims to lessen the unpredictability and uncontrollability of heterogeneous systems by using this analysis for resource selection.

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Heterogeneity as key feature of high performance computing: the PQE1 prototype

Abstract: In this work we present the results of a project aimed at assembling an hybrid massively parallel machine, the PQE1 prototype, devoted to the simulation of complex physical models.

Cited by 12 publications

References 28 publications

Application Performance on a Cluster-Booster System

Application Performance on a Cluster-Booster System

Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

Concurrent adaptive computing in heterogeneous environments (CACHE)

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