Monitoring and Migration of a PETSc-based Parallel Application for Medical Imaging in a Grid computing PSE

Murli, A.; Boccia, Vania; Carracciuolo, Luisa; D’Amore, Luisa; Laccetti, Giuliano; Lapegna, Marco

doi:10.1007/978-0-387-73659-4_25

Cited by 21 publications

(10 citation statements)

References 12 publications

(15 reference statements)

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“…Hence, results we presented were concerned with the capability of a multigrained algorithm to solve this problem by using such a framework. We hope that these results encourage readers to further extend the framework according to their specific application's requirements (see References 36,61, and 62 for instance). We are now working to implement our approach in a distributed computing environment or hybrid environments, that is, high performance computing environments where special purpose devices such as Graphics Processing Units are chosen with the aim of accelerating the solution of heavy computational kernels, by combining the fine‐grained parallelism with a coarse grained parallelism based on SCC decomposition.…”

Section: Discussionmentioning

confidence: 91%

Toward a multilevel scalable parallel Zielonka's algorithm for solving parity games

D’Amore

Murano

Sorrentino

et al. 2020

Concurrency and Computation

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Summary In this work, we perform the feasibility analysis of a multi‐grained parallel version of the Zielonka Recursive (ZR) algorithm exploiting the coarse‐ and fine‐ grained concurrency. Coarse‐grained parallelism relies on a suitable splitting of the problem, that is, a graph decomposition based on its Strongly Connected Components (SCC) or a splitting of the formula generating the game, while fine‐grained parallelism is introduced inside the Attractor which is the most intensive computational kernel. This configuration is new and addressed for the first time in this article. Innovation goes from the introduction of properly defined metrics for the strong and weak scaling of the algorithm. These metrics conduct to an analysis of the values of these metrics for the fine grained algorithm, we can infer the expected performance of the multi‐grained parallel algorithm running in a distributed and hybrid computing environment. Results confirm that while a fine‐grained parallelism have a clear performance limitation, the performance gain we can expect to get by employing a multilevel parallelism is significant.

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Section: Discussionmentioning

confidence: 91%

Toward a multilevel scalable parallel Zielonka's algorithm for solving parity games

D’Amore

Murano

Sorrentino

et al. 2020

Concurrency and Computation

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“…A possible next investigation could be to face the problem of fault tolerance in massive multi-core environments where some of the authors have already worked on (see, for example, the works of Caruso et al, 54 Boccia et al, 55 and Murli et al 56 ) and the usage of Service Level Agreement-centered paradigms. 57,58…”

Section: Discussionmentioning

confidence: 99%

Marine bathymetry processing through GPGPU virtualization in high performance cloud computing

Montella

Marcellino

Galletti

et al. 2018

Concurrency and Computation

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Fast technology development has influenced the widespread use of low-power devices in different scientific, environmental, and everyday life areas, giving birth to the Internet of Things.In this paper, we focus on the context of marine studies, addressing the problem of marine bathymetry data processing and analysis via pervasive and Internet-connected sensors and low-power distributed devices. Pervasive and Internet-connected low-power devices (as the components involved in the sensing and processing actions) made diverse and different "things" as a worldwide-distributed system. Given the high complexity of the algorithms involved in these studies, which usually involve general-purpose graphic processing unit (GPGPU) computation, it is impossible for the limited devices to perform the required calculations. To overcome these limitations, in this paper, we propose and implement a vertical application of GVirtuS, the open-source GPGPU virtualization and remoting service, for achieving high performance geographical data interpolation in a high performance cloud computing scenario. We present an innovative implementation by comparing, in terms of performance and accuracy, the inverse distance weighting and kriging interpolation methods in their parallel implementations leveraging on CUDA-enabled GPGPUs. We present a real-world use case related to high-resolution bathymetry interpolation in a crowdsource data context in the Bay of Pozzuoli, Italy.

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“…Gómez-Martín et al, 2016). Everyday usage of supercomputer centres shows the need for the separation of the cloud-like jobs (that do not require a high-bandwidth low-latency interconnect between nodes) from the multi-node parallel jobs (Caruso et al, 2005; Murli et al, 2017). Such a separation is a way for increasing efficiency of supercomputer deployment (Kraemer et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Angara interconnect makes GPU-based Desmos supercomputer an efficient tool for molecular dynamics calculations

Stegaĭlov

Dlinnova

Ismagilov³

et al. 2019

The International Journal of High Performance Computing Applica

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In this article, we describe the Desmos supercomputer that consists of 32 hybrid nodes connected by a low-latency high-bandwidth Angara interconnect with torus topology. This supercomputer is aimed at cost-effective classical molecular dynamics calculations. Desmos serves as a test bed for the Angara interconnect that supports 3-D and 4-D torus network topologies and verifies its ability to unite massively parallel programming systems speeding-up effectively message-passing interface (MPI)-based applications. We describe the Angara interconnect presenting typical MPI benchmarks. Desmos benchmarks results for GROMACS, LAMMPS, VASP and CP2K are compared with the data for other high-performance computing (HPC) systems. Also, we consider the job scheduling statistics for several months of Desmos deployment.

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Monitoring and Migration of a PETSc-based Parallel Application for Medical Imaging in a Grid computing PSE

Cited by 21 publications

References 12 publications

Toward a multilevel scalable parallel Zielonka's algorithm for solving parity games

Toward a multilevel scalable parallel Zielonka's algorithm for solving parity games

Marine bathymetry processing through GPGPU virtualization in high performance cloud computing

Angara interconnect makes GPU-based Desmos supercomputer an efficient tool for molecular dynamics calculations

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