Extending OpenMP to Survive the Heterogeneous Multi-Core Era

Ayguadé, Eduard; Badía, Rosa M.; Bellens, Pieter; Cabrera, Daniel; Durán, Alejandro; Ferrer, Roger; González, Marc; Igual, Francisco D.; Jiménez-González, Daniel; Labarta, Jesús; Martinell, Luis; Martorell, Xavier; Mayo, Rafael; Pérez, Josep M.; Planas, Judit; Quintana–Ort́ı, Enrique S.

doi:10.1007/s10766-010-0135-4

Cited by 55 publications

(45 citation statements)

References 6 publications

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“…Finally, in [23] the authors propose criticality-aware dynamic task scheduling on heterogeneous architectures with OmpSs [24,25]. The paper proposes scheduling policies in order to reduce the total execution time of a task represented by DAG.…”

Section: Related Workmentioning

confidence: 99%

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

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183

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h i g h l i g h t s• Energy-aware run-time scheduler for task-based applications.• Model for estimating the application Energy consumption.• Methodology to automatically generate the required power consumption profile.• Multi-heuristic resource allocation algorithm to get solutions in polynomial time.• Energy saving/performance trade-off evaluation for different scenarios. a r t i c l e i n f o b s t r a c tGreen Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently taskbased applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.

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Section: Related Workmentioning

confidence: 99%

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

Self Cite

183

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“…• OmpSs, is an in-house programming model that supports irregular and asynchronous parallelism and heterogeneous architectures, including data dependences and data-flow concepts to automatically move data as necessary [1]. OmpSs is available for GPUs, FPGAs, and co-processors, such as Xeon Phi.…”

Section: Target Programming Models and Architecturesmentioning

confidence: 99%

Evaluating directive-based programming models on Wave Propagation Kernels

Rodríguez¹,

Farré²,

Rosas³

et al. 2017

79th EAGE Conference and Exhibition 2017 - Workshops

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HPC systems have become mandatory to tackle the ever-increasing challenges imposed by new exploration areas around the world. The requirement for more HPC resources depends on the complexity of the area under exploration, yet the larger the HPC system, the more the energy consumption involved. Reduction of overall power consumption in HPC facilities, lead technologies vendors to introduce many-core devices and heterogeneous computing to the supercomputers, thus, forcing exploration codes to be ported to such new architectures. As the Oil & Gas industry has more than 30 years of legacy code, the effort to adapt it could be huge. To this extent, several programming models emerged, e.g. high-level directive-based programming models, such as OpenMP, OpenACC, and OmpSs rely on specifying to the compiler the parallelism directives to release users from manually decomposing and processing the parallel regions. The results show that it is possible to obtain a parallel code for current heterogeneous HPC architectures investing a few hours or days. The obtained speedup is at least an order of magnitude w.r.t. a sequential code. However, we provide parallelism inside a single computational node, and a wider study for evaluating the costs of porting and parallelizing across computational nodes is pending.Authors thank Repsol for the permission to publish the present research, carried out at the Repsol-BSC Research Center. This work has received funding from the European Union’s Horizon 2020 Programme (2014-2020) and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement n.◦ 689772.Peer ReviewedPostprint (author's final draft

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“…The OmpSs programming model [8] covers different homogeneous and heterogeneous architectures used nowadays 1 #pragma omp target device(cuda) implements(matmul_tile) \ and is open to cover future systems designed with new raising architectures.…”

Section: The Ompss Programming Modelmentioning

confidence: 99%

Self-Adaptive OmpSs Tasks in Heterogeneous Environments

Planas

Badía

Ayguadé

et al. 2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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Abstract-As new heterogeneous systems and hardware accelerators appear, high performance computers can reach a higher level of computational power. Nevertheless, this does not come for free: the more heterogeneity the system presents, the more complex becomes the programming task in terms of resource management.OmpSs is a task-based programming model and framework focused on the runtime exploitation of parallelism from annotated sequential applications. This paper presents a set of extensions to this framework: we show how the application programmer can expose different specialized versions of tasks (i.e. pieces of specific code targeted and optimized for a particular architecture) and how the system can choose between these versions at runtime to obtain the best performance achievable for the given application. From the results obtained in a multi-GPU system, we prove that our proposal gives flexibility to application's source code and can potentially increase application's performance.

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Extending OpenMP to Survive the Heterogeneous Multi-Core Era

Cited by 55 publications

References 6 publications

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Evaluating directive-based programming models on Wave Propagation Kernels

Self-Adaptive OmpSs Tasks in Heterogeneous Environments

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