Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern

Aldinucci, Marco; Pezzi, Guilherme Peretti; Drocco, Maurizio; Spampinato, Concetto; Torquati, Massimo

doi:10.1177/1094342014567907

Cited by 13 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As future work, we plan to extend GRPPI for supporting more complex parallel patterns, such as windowed and keyed stream farms, stream iteration and the Stencil‐Reduce patterns. Furthermore, we intend to include other execution environments as for the offered parallel frameworks, eg, FastFlow, SkePU, and OpenCL SYCL.…”

Section: Discussionmentioning

confidence: 99%

A generic parallel pattern interface for stream and data processing

Astorga

Dolz

Fernández

et al. 2017

Concurrency and Computation

View full text Add to dashboard Cite

SummaryCurrent parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge.Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations. An approach to relieve developers from this burden is the use of pattern-based parallel programming frameworks, such as SkePU, 2FastFlow 3 , or Intel TBB. 4 In this sense, design patterns provide a way to encapsulate (using a building blocks approach) algorithmic aspects, allowing users to implement more robust, readable, and portable solutions with such a high-level of abstraction. Basically, these patterns instantiate parallelism while hide away the complexity of concurrency mechanisms, eg, thread management, synchronizations, or data sharing. Examples of applications coming from multiple domains (eg, financial, medical, and mathematical) and improving their performance through parallel programming design patterns, can be widely found in the literature. [5][6][7] Nevertheless, although all these skeletons aim to simplify the development of parallel applications, there is not a unified standard. 8 Therefore, users require understanding different frameworks, not only to decide which fits best for their purposes, but also to properly use them. Not to mention the migration efforts of applications among frameworks, which becomes as well an arduous task.In order to mitigate this situation, this paper presents GRPPI, a generic and reusable high-level C++ parallel pattern interface that comprises both stream and data-parallel patterns. In general, the goal of

show abstract

Section: Discussionmentioning

confidence: 99%

A generic parallel pattern interface for stream and data processing

Astorga

Dolz

Fernández

et al. 2017

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Also, the FastFlow parallel programming environment has recently been extended to support GPUs via CUDA [4] and OpenCL (as described in the present work). FastFlow CPU implementations of patterns are realised via non-blocking graphs of threads connected by way of lock-free channels [11], while the GPU implementation is realised by way of the OpenCL bindings and offloading techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The basic building block of Loop-of-stencil-reduce is the stencil-reduce pattern [4], which applies a reduce pattern to the result of a stencil application (i.e. functional composition).…”

Section: Related Workmentioning

confidence: 99%

“…III-B; and the two-phase video restoration algorithm. For more details on the video restoration algorithm we refer to [5], [4].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…1) The Loop-of-stencil-reduce pattern is an evolution of the stencil-reduce pattern [4]. Specifically, Loop-of-stencilreduce has been refined to explicitly include the iterative behaviour and the optimisations enabled by the knowledge of iterative behaviour.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Loop-of-Stencil-Reduce Paradigm

Aldinucci

Danelutto

Drocco

et al. 2015

2015 IEEE Trustcom/BigDataSE/Ispa

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper we advocate the Loop-of-stencilreduce pattern as a way to simplify the parallel programming of heterogeneous platforms (multicore+GPUs). Loop-of-Stencilreduce is general enough to subsume map, reduce, map-reduce, stencil, stencil-reduce, and, crucially, their usage in a loop. It transparently targets (by using OpenCL) combinations of CPU cores and GPUs, and it makes it possible to simplify the deployment of a single stencil computation kernel on different GPUs. The paper discusses the implementation of Loop-of-stencilreduce within the FastFlow parallel framework, considering a simple iterative data-parallel application as running example (Game of Life) and a highly effective parallel filter for visual data restoration to assess performance. Thanks to the high-level design of the Loop-of-stencil-reduce, it was possible to run the filter seamlessly on a multicore machine, on multi-GPUs, and on both.

show abstract

SIMD Monte-Carlo Numerical Simulations Accelerated on GPU and Xeon Phi

Plazolles

Baz

Spel

et al. 2017

Int J Parallel Prog

View full text Add to dashboard Cite

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern

Cited by 13 publications

References 32 publications

A generic parallel pattern interface for stream and data processing

A generic parallel pattern interface for stream and data processing

The Loop-of-Stencil-Reduce Paradigm

SIMD Monte-Carlo Numerical Simulations Accelerated on GPU and Xeon Phi

Contact Info

Product

Resources

About