High performance computing using MPI and OpenMP on multi-core parallel systems

Jin, Haoqiang; Jespersen, Dennis C.; Mehrotra, Piyush; Biswas, Rupak; Huang, Lei; Chapman, Barbara

doi:10.1016/j.parco.2011.02.002

Cited by 166 publications

(83 citation statements)

References 20 publications

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“…Clusters of SMP's nodes support differently parallel programming models. However, it significantly increases programming complexity when using the low-level interfaces such as MPI and OpenMP in order to deal with both DM and SM architecture [31]. OpenMP provides an interface for programming SMP between cores, while MPI defines parallelism across processors by calling library function to send and receive messages.…”

Section: Symmetric Multi-processing (Smp) Architecturementioning

confidence: 99%

Shared Memory and Hardware Utilizations for the Parallelization of Local Sequences Alignment using SW Algorithm: A Review

Eltayeeb¹,

Latiff²,

Isnin³

2015

IJCA

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It is becoming increasingly difficult to ignore the importance of aligning DNA and Protein sequences to infer properties of new sequences from well-known reference sequences established and sorted in genetics databanks. Many studies in recent years have focused on different implementations of Sequences Alignment Problems (SAP). However, researcher confused with the ambiguous classification of the SAP. This paper is set out mainly to review, investigate, and analysis current trends in shared memory and hardware implementation of local SAP using Smith-Waterman algorithm. The literatures are addressing and evaluating in order to highlight their advantages and disadvantages.

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Section: Symmetric Multi-processing (Smp) Architecturementioning

confidence: 99%

Shared Memory and Hardware Utilizations for the Parallelization of Local Sequences Alignment using SW Algorithm: A Review

Eltayeeb¹,

Latiff²,

Isnin³

2015

IJCA

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“…The advantages and disadvantages of the proposed parallelisation strategies naturally lead to the solution where both are combined in order to minimise the negative impacts of each [5]. On the one hand, we want to avoid the "hard ceiling" present in the SMP solution by leveraging cross-process communication, while on the other, we want to minimise the communication overhead present in the message-passing paradigm.…”

Section: Hybrid Parallelismmentioning

confidence: 99%

Parallelisation strategies for large scale cellular automata frameworks in pharmaceutical modelling

Bezbradica

Crane

Ruskin

2012

2012 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Cellular Automata (CA) properties facilitate the detail required for the bottom-up approach to modelling and simulation of a broad range of physico-chemical reactions. In pharmaceutical applications, CA models use a combination of discrete-event rules based on probabilistic distributions and fundamental physical laws to predict the behaviour of active substances (drug molecules) and structural changes in Drug Dissolution Systems (DDS) over time. Several models of this type have been described so far in the scientific literature. Yet, practical applications are lacking in the context of large-scale, high-precision, high-fidelity simulations. The key obstacle to parallelisation of such models is not only the amount of data involved, but also the fact that many of these models incorporate agent-like behaviour within the CA framework in order to describe pharmaceutical components. This makes communication across process boundaries expensive. In this paper, we apply different parallelisation strategies to a large scale CA framework, used to model coated drug spheres. We use two parallel-computing application programming interfaces (APIs), namely OpenMP and MPI, to partition the simulation space. We analyse the applicability of each API to the problem individually, as well as in the hybrid solution. We examine speedup potential and overhead for local and global communication for simulation speed and solution scalability. For these types of problems, our results show that performance is much improved for appropriate combinations of parallelisation solutions.

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“…The idea of distributed computing is to combine machines, which is typically commodity hardware, that can be used to parallelize tasks, as for example the libraries and standards cited above that were used in more than scientific domains [11], [12], [17], [18]. But the limitation of the distributed system lies in the fact that these machines are limited in computing power (number of processors in each machine) and the data storage capacity.…”

Section: Introductionmentioning

confidence: 99%

Distributed GPU-Based K-Means Algorithm for Data-Intensive Applications: Large-Sized Image Segmentation Case

Fakhi¹,

Bouattane²,

Youssfi³

et al. 2017

ijacsa

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Abstract-K-means is a compute-intensive iterative algorithm. Its use in a complex scenario is cumbersome, specifically in data-intensive applications. In order to accelerate the K-means running time for data-intensive application, such as large sized image segmentation, we use a distributed multi-agent system accelerated by GPUs. In this K-means version, the input image data are divided into subsets of image data which can be performed independently on GPUs. In each GPU, we offloaded the data assignment and the K-centroids recalculation steps of the K-means algorithm for a massively parallel processing. We have implemented this K-means version on the Nvidia GPU with Compute Unified Device Architecture. The distributed multiagent system was written with Java Agent Development framework.

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High performance computing using MPI and OpenMP on multi-core parallel systems

Cited by 166 publications

References 20 publications

Shared Memory and Hardware Utilizations for the Parallelization of Local Sequences Alignment using SW Algorithm: A Review

Shared Memory and Hardware Utilizations for the Parallelization of Local Sequences Alignment using SW Algorithm: A Review

Parallelisation strategies for large scale cellular automata frameworks in pharmaceutical modelling

Distributed GPU-Based K-Means Algorithm for Data-Intensive Applications: Large-Sized Image Segmentation Case

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