accULL: An OpenACC Implementation with CUDA and OpenCL Support

Ruym,; Reyes, Ruyman; Iv,; López-Rodríguez, n; Fumero, Juan; Sande, Francisco de

doi:10.1007/978-3-642-32820-6_86

Cited by 52 publications

(34 citation statements)

References 3 publications

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“…Unfortunately, the OpenCL API is too low level [72], resulting in code verbosity and low productivity of programmers, who need to manually perform buffer allocations, data transfers, synchronizations etc. High level annotations [81,2] or embedded languages [97] built on top of OpenCL that automate these tasks and hide their complexity are a better approach. Several authors have considered the programmability of accelerators on top of OpenCL across a cluster [7,35]; however, existing solutions only abstract away some complexities of OpenCL but still expose other low-level details or restrict the non-accelerator part of an application to a single node.…”

Section: Advancing the Programming Environmentsmentioning

confidence: 99%

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On Processing Extreme Data

Petcu¹,

Iuhasz²,

Pop³

et al. 2016

SCPE

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Abstract. Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analyzed in near real-time by using a very large number of memory or storage elements and exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed, the millions of images per day that must be analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage nowadays cannot handle the extreme scale of such application data.Following the need of improvement of current concepts and technologies, we focus in this paper on the needs of data intensive applications running on systems composed of up to millions of computing elements (exascale systems). We propose in this paper a methodology to advance the state-of-the-art. The starting point is the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on exascale systems. This will pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, thus promoting high performance and efficiency, offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real time.

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Section: Advancing the Programming Environmentsmentioning

confidence: 99%

“…In order to exploit all the available hardware in exascale systems, we need to extend and evolve the highlevel programming of accelerators (e.g., [81,97,2]) into high-productivity seamless approaches, for instance, through the natural integration with the ADT previously described.…”

Section: Advancing the Programming Environmentsmentioning

confidence: 99%

On Processing Extreme Data

Petcu¹,

Iuhasz²,

Pop³

et al. 2016

SCPE

Self Cite

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“…The accULL [4] compiler developed by Universidad of La Laguna (Spain) is an open source initiative. accULL consists on a structure of two layers containing YaCF [9] (Yet another Compiler Framework) and Frangollo [10], a runtime library.…”

Section: Accullmentioning

confidence: 99%

“…In particular, OpenUH [3] developed at the University of Houston, and accULL [4] from Universidad de La Laguna (Spain). Both of them are available for free to anyone interested.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of OpenACC Compilers

Barba

González-Escribano

Llanos

2016

Algorithms and Architectures for Parallel Processing

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Abstract. OpenACC has been on development for a few years now. The OpenACC 2.5 specification was recently made public and there are some initiatives for developing full implementations of the standard to make use of accelerator capabilities. There is much to be done yet, but currently, OpenACC for GPUs is reaching a good maturity level in various implementations of the standard, using CUDA and OpenCL as backends. Nvidia is investing in this project and they have released an OpenACC Toolkit, including the PGI Compiler. There are, however, more developments out there. In this work, we analyze different available OpenACC compilers that have been developed by companies or universities during the last years. We check their performance and maturity, keeping in mind that OpenACC is designed to be used without extensive knowledge about parallel programming. Our results show that the compilers are on their way to a reasonable maturity, presenting different strengths and weaknesses.

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“…Among all the parallel algorithms, GPU models obtain the best acceleration [21][22][23]. The multistreaming processors and powerful computational capabilities of GPUs are vitally important for efficient parallel computation with large amounts of data and high-precision requirements [24][25][26][27][28]. Bradbrook et al [29] rewrote the twodimensional diffusive wave model JFLOW using generalpurpose GPUs and achieved substantially faster computation on single-accelerator processors.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Different Parallel Computation Methods for a Two‐Dimensional Dam‐Break Model

Zhang

Jing

et al. 2017

Mathematical Problems in Engineering

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Three parallel methods (OpenMP, MPI, and OpenACC) are evaluated for the computation of a two-dimensional dam-break model using the explicit finite volume method. A dam-break event in the Pangtoupao flood storage area in China is selected as a case study to demonstrate the key technologies for implementing parallel computation. The subsequent acceleration of the methods is also evaluated. The simulation results show that the OpenMP and MPI parallel methods achieve a speedup factor of 9.8× and 5.1×, respectively, on a 32-core computer, whereas the OpenACC parallel method achieves a speedup factor of 20.7× on NVIDIA Tesla K20c graphics card. The results show that if the memory required by the dam-break simulation does not exceed the memory capacity of a single computer, the OpenMP parallel method is a good choice. Moreover, if GPU acceleration is used, the acceleration of the OpenACC parallel method is the best. Finally, the MPI parallel method is suitable for a model that requires little data exchange and large-scale calculation. This study compares the efficiency and methodology of accelerating algorithms for a dam-break model and can also be used as a reference for selecting the best acceleration method for a similar hydrodynamic model.

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accULL: An OpenACC Implementation with CUDA and OpenCL Support

Cited by 52 publications

References 3 publications

On Processing Extreme Data

On Processing Extreme Data

Comparative Analysis of OpenACC Compilers

Comparison of Three Different Parallel Computation Methods for a Two‐Dimensional Dam‐Break Model

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