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

Josefina Lenis , Miquel Angel Senar Rosell

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


© 2017, The Author(s). 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 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.
Original languageEnglish
Pages (from-to)1909-1924
JournalCluster Computing
Issue number3
Publication statusPublished - 1 Sep 2017


  • BWA
  • Bowtie
  • Data partitioning
  • GEM
  • Genomic aligners
  • Memory system performance
  • NUMA
  • SNAP


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