Sequence Alignment Tools: One Parallel Pattern to Rule Them All?

Misale, Claudia; Ferrero, Giulio; Torquati, Massimo; Aldinucci, Marco

doi:10.1155/2014/539410

Cited by 12 publications

(9 citation statements)

References 39 publications

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“…In computational electromagnetics modelling problems with up to one billion variables have been addressed with both memory-and CPU-intensive algorithms, solving major longstanding problems. More structured approaches based on pattern-based parallel programming effectively cater for the design and development of parallel pipelines for M&S in systems biology and next generation sequencing [1,2], providing developers with portability across a variety of HPC platforms, like clusters of multi-cores [3,4] as well as cloud infrastructures [5].…”

Section: Background and State Of The Artmentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

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Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned.

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Section: Background and State Of The Artmentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

Self Cite

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“…Our work, however, focuses on the evaluation of techniques that can be applied at the application level and therefore don't require root permissions to be applied to any NUMA system. Genome alignment problems have been considered by Misale et al [20]. The authors implement a framework to work under BOWTIE2 and BWA to improve the local affinity of the original algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Rewriting the parallel section using Cilk removes the load imbalance, resulting in a factor 2× performance improvement over the original BWA. In both cases-Misale et al [20] and Herzeel et al [9]-the source code of the applications -aligners-are modified, which might be a costly action and dependent on the application version. Abuin et.…”

Section: Related Workmentioning

confidence: 99%

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A performance comparison of data and memory allocation strategies for sequence aligners on NUMA architectures

Lenis

Senar

2017

Cluster Comput

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Over the last several years, many sequence alignment tools have appeared and become popular for the fast evolution of next generation sequencing technologies. Obviously, researchers that use such tools are interested in getting maximum performance when they execute them in modern infrastructures. Today's NUMA (Non-uniform memory access) architectures present major challenges in getting such applications to achieve good scalability as more processors/cores are used. The memory system in NUMA systems shows a high complexity and may be the main cause for the loss of an application's performance. The existence of several memory banks in NUMA systems implies a logical increase in latency associated with the accesses of a given processor to a remote bank. This phenomenon is usually attenuated by the application of strategies that tend to increase the locality of memory accesses. However, NUMA systems may also suffer from contention problems that can occur when concurrent accesses are concentrated on a reduced number of banks. Sequence alignment tools use large data structures to contain reference genomes to which all reads are aligned. Therefore, these tools are very sensitive to performance problems related to the memory system. The main goal of this study is to explore the trade-offs between data locality and data dispersion in NUMA systems. We have performed experiments with several popular sequence alignment tools on two This work has been supported by MINECO-Spain under contract TIN2014-53234-C2-1-R. widely available NUMA systems to assess the performance of different memory allocation policies and data partitioning strategies. We find that there is not one method that is best in all cases. However, we conclude that memory interleaving is the memory allocation strategy that provides the best performance when a large number of processors and memory banks are used. In the case of data partitioning, the best results are usually obtained when the number of partitions used is greater, sometimes combined with an interleave policy.

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“…Related work Two prior studies [19,20], examined Bowtie 2 thread scaling with synchronization and Non-Uniform Memory Access (NUMA) as primary concerns. By adapting Bowtie 2 to the FastFlow [21] parallel framework and by making effective use of (a) thread pinning and (b) interleaving of memory pages across NUMA sockets, the modifications improved Bowtie 2's thread scaling.…”

Section: Introductionmentioning

confidence: 99%

Scaling read aligners to hundreds of threads on general-purpose processors

Langmead

Wilks

Antonescu

et al. 2017

Preprint

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General-purpose processors can now contain many dozens of processor cores and support hundreds of simultaneous threads of execution. To make best use of these threads, genomics software must contend with new and subtle computer architecture issues. We discuss some of these and propose methods for improving thread scaling in tools that analyze each read independently, such as read aligners. We implement these methods in new versions of Bowtie, Bowtie 2 and HISAT. We greatly improve thread scaling in many scenarios, including on the recent Intel Xeon Phi architecture. We also highlight how bottlenecks are exacerbated by variablerecord-length file formats like FASTQ and suggest changes that enable superior scaling.

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Sequence Alignment Tools: One Parallel Pattern to Rule Them All?

Cited by 12 publications

References 39 publications

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Why High-Performance Modelling and Simulation for Big Data Applications Matters

A performance comparison of data and memory allocation strategies for sequence aligners on NUMA architectures

Scaling read aligners to hundreds of threads on general-purpose processors

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