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Tuning iteration space slicing based tiled multi-core code implementing Nussinov’s RNA folding

Overview of attention for article published in BMC Bioinformatics, January 2018
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Title
Tuning iteration space slicing based tiled multi-core code implementing Nussinov’s RNA folding
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-018-2008-6
Pubmed ID
Authors

Marek Palkowski, Wlodzimierz Bielecki

Abstract

RNA folding is an ongoing compute-intensive task of bioinformatics. Parallelization and improving code locality for this kind of algorithms is one of the most relevant areas in computational biology. Fortunately, RNA secondary structure approaches, such as Nussinov's recurrence, involve mathematical operations over affine control loops whose iteration space can be represented by the polyhedral model. This allows us to apply powerful polyhedral compilation techniques based on the transitive closure of dependence graphs to generate parallel tiled code implementing Nussinov's RNA folding. Such techniques are within the iteration space slicing framework - the transitive dependences are applied to the statement instances of interest to produce valid tiles. The main problem at generating parallel tiled code is defining a proper tile size and tile dimension which impact parallelism degree and code locality. To choose the best tile size and tile dimension, we first construct parallel parametric tiled code (parameters are variables defining tile size). With this purpose, we first generate two nonparametric tiled codes with different fixed tile sizes but with the same code structure and then derive a general affine model, which describes all integer factors available in expressions of those codes. Using this model and known integer factors present in the mentioned expressions (they define the left-hand side of the model), we find unknown integers in this model for each integer factor available in the same fixed tiled code position and replace in this code expressions, including integer factors, with those including parameters. Then we use this parallel parametric tiled code to implement the well-known tile size selection (TSS) technique, which allows us to discover in a given search space the best tile size and tile dimension maximizing target code performance. For a given search space, the presented approach allows us to choose the best tile size and tile dimension in parallel tiled code implementing Nussinov's RNA folding. Experimental results, received on modern Intel multi-core processors, demonstrate that this code outperforms known closely related implementations when the length of RNA strands is bigger than 2500.

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Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 100%
Researcher 1 50%
Readers by discipline Count As %
Mathematics 1 50%
Computer Science 1 50%
Agricultural and Biological Sciences 1 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 January 2018.
All research outputs
#9,509,309
of 12,373,969 outputs
Outputs from BMC Bioinformatics
#3,583
of 4,592 outputs
Outputs of similar age
#262,998
of 397,426 outputs
Outputs of similar age from BMC Bioinformatics
#321
of 450 outputs
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