Parallel tiled Nussinov RNA folding loop nest generated using both dependence graph transitive closure and loop skewing

Palkowski, Marek; Bielecki, Wlodzimierz

doi:10.1186/s12859-017-1707-8

Cited by 22 publications

(40 citation statements)

References 17 publications

(20 reference statements)

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“…Let us recap tiled code generation for Nussinov’s algorithm presented in [ 4 ]. To generate valid 3-D tiled code for the Nussinov loop nest, we adopt the approach presented in paper [ 14 ], which is based on the transitive closure of dependence graphs.…”

Section: Methodsmentioning

confidence: 99%

“…In paper [ 4 ], we presented loop tiling based on the transitive closure of a dependence graph for Nussinov’s algorithm. It is within the iteration space slicing (ISS) framework [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…The key step in calculating an iteration space slice is to calculate the transitive closure of the data dependence graph of the program; then transitive dependences are applied to the statement instances of interest to produce valid tiles. The idea of tiling, presented in paper [ 4 ], is to transform (correct) original rectangular fixed tiles so that all target tiles are valid under lexicographic order. We demonstrated higher speed-up of generated tiled code (for a properly chosen size of original tiles) than that of code produced with state-of the-art source-to-source optimizing compilers.…”

Section: Introductionmentioning

confidence: 99%

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

Palkowski

Bielecki

2018

BMC Bioinformatics

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BackgroundRNA 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.ResultsTo 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.ConclusionsFor 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-018-2008-6) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

“…In paper [ 4 ], we presented loop tiling based on the transitive closure of a dependence graph for Nussinov’s algorithm. It is within the iteration space slicing (ISS) framework [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Palkowski

Bielecki

2018

BMC Bioinformatics

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“…Palkowski et al implemented the Nussinov algorithm in multicore CPUs and in Intel Xeon Phi accelerators. In their proposal, an automatic loop nest tiling is applied, generating a 3D DP matrix with a more regular data access pattern.…”

Section: Rna Structural Alignmentmentioning

confidence: 99%

Using GPU to accelerate the pairwise structural RNA alignment with base pair probabilities

Sundfeld

Teodoro

Havgaard

et al. 2019

Concurrency and Computation

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Structural alignments of Ribonucleic acid (RNA) sequences solved by the Sankoff algorithm are computationally expensive and often require constraints to be used in practice. Modern Graphics Processing Units (GPUs) contain more than 1000 cores, which compute in parallel to speed up applications. Here, we present a GPU-based solution to the RNA structural alignment problem that makes use of precalculated base pair probabilities on the individual sequences.We designed and developed an unconstrained version of the Sankoff algorithm, obtaining the optimal result and calculating the entire four-dimension dynamic programming matrix (4D DP).Our approach uses a two-level wavefront strategy to exploit parallelism. The 4D DP matrix is divided in one external matrix (EM) and several internal matrices (IM). We applied wavefront strategies on the EM and IMs in a two-level hierarchical way. At the first level, the wavefront is applied to the EM, calculating the cells that belong to the same diagonal in parallel. In the second level, since each cell in the EM is itself an IM matrix, the cells that belong to the same IM diagonal are calculated in parallel. The results obtained with real RNA sequences show that our GPU version is capable of outperforming a multicore CPU version of the unconstrained version of the Sankoff algorithm. Compared with the CPU-based version running on 32 cores, our approach is able to achieve a speedup of 7.81x on the NVidia Tesla P100. In this case, the execution time was reduced from 6 hours and 18 minutes (32 cores) to 48 minutes and 20 seconds (GPU). KEYWORDSbase-pairing probabilities, GPUs, high-performance computing, RNA, Sankoff algorithm INTRODUCTIONBioinformatics is an interdisciplinary area that involves computer science, statistics, mathematics, and biology, aiming at proposing algorithms and tools to help biologists in their data analysis. Sequence comparison is one of the most basic operations in Bioinformatics, and its goal is to expose the evolutionary relationship among two or more sequences by computing a similarity score and an alignment. A comparison operation can take into account the biological sequences themselves (1D comparison) or their structure (2D and 3D comparison).Structural comparisons are usually done for Ribonucleic acid (RNA) sequences, which fold over themselves, generating a structure.Nussinov et al 1 proposed an algorithm based on dynamic programming to compute the 2D structure of a given RNA sequence. This algorithm was extended to full energy calculation in several versions, eg, 2 and further to a full structural alignment by Sankoff 3 for more than one sequence, giving as output the 2D consensus structure of a set of sequences, folding and aligning the sequences simultaneously. Sankoff's algorithm runs in O(L 3N ) time and uses O(L 2N ) memory, for N sequences of length L, and provides the optimal solution.Due to its high computational cost and huge memory requirements, Sankoff's algorithm is considered unfeasible. For this reason, many practical Bioinformatics tool...

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“…ATF is applied in other compilers such as Apollo and PPCG as well as commercial R-STREAM and IBM-XL. ATF has some drawbacks, papers [4], [5], [6] this approach is the cost of additional memory management, which is overcome by tiling strategies [9]. In paper [10], Li's method was improved, but it allows for generating only serial code.…”

Section: Introductionmentioning

confidence: 99%

Parallel cache-efficient code for computing the McCaskill partition functions

Palkowski

Bielecki

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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We present parallel tiled optimized McCaskill's partition functions computation code. That CPU and memory intensive dynamic programming task is within computational biology. To optimize code, we use the authorial source-to-source TRACO compiler and compare obtained code performance to that generated with the state-of-the-art PluTo compiler based on the affine transformations framework (ATF). Although PLuTo generates tiled code with outstanding locality, it fails to parallelize tiled code. A TRACO tiling strategy uses the transitive closure of a dependence graph to avoid affine function calculation. The ISL scheduler is used to parallelize tiled loop nests. An experimental study carried out on a multi-core computer demonstrates considerable speed-up of generated code for the larger number of threads.

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Parallel tiled Nussinov RNA folding loop nest generated using both dependence graph transitive closure and loop skewing

Cited by 22 publications

References 17 publications

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

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

Using GPU to accelerate the pairwise structural RNA alignment with base pair probabilities

Parallel cache-efficient code for computing the McCaskill partition functions

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