Parallel and Scalable Short-Read Alignment on Multi-Core Clusters Using UPC++

González-Domínguez, Jorge; Liu, Yongchao; Schmidt, Bertil

doi:10.1371/journal.pone.0145490

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…Many sequence alignment applications based on FM-index have emerged recently, such as HISAT [28], Bowtie [4], BWA [6], [5] and SOAP [7], [29]. Furthermore, implementations for specific architectures or accelerators have been published, including GPUs (Arioc [30], CUSHAW2 [31], BarraCUDA [32], [33]), Clusters (CUSHAW3 [34]), Clouds (BigBWA [35]) and FPGAs (FHAST [36]).…”

Section: Related Workmentioning

confidence: 99%

Accelerating Sequence Alignments Based on FM-Index Using the Intel KNL Processor

Herruzo

González-Navarro

Ibáñez

et al. 2020

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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FM-index is a compact data structure suitable for fast matches of short reads to large reference genomes. The matching algorithm using this index exhibits irregular memory access patterns that cause frequent cache misses, resulting in a memory bound problem. This paper analyzes different FM-index versions presented in the literature, focusing on those computing aspects related to the data access. As a result of the analysis, we propose a new organization of FM-index that minimizes the demand for memory bandwidth, allowing a great improvement of performance on processors with high-bandwidth memory, such as the second-generation Intel Xeon Phi (Knights Landing, or KNL), integrating ultra high-bandwidth stacked memory technology. As the roofline model shows, our implementation reaches 95% of the peak random access bandwidth limit when executed on the KNL and almost all the available bandwidth when executed on other Intel Xeon architectures with conventional DDR memory. In addition, the obtained throughput in KNL is much higher than the results reported for GPUs in the literature.

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Section: Related Workmentioning

confidence: 99%

Accelerating Sequence Alignments Based on FM-Index Using the Intel KNL Processor

Herruzo

González-Navarro

Ibáñez

et al. 2020

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…using static workload distribution) or varied workload per job (e.g. using a shared integer counter to realize dynamic workload distribution via remote memory access operations in MPI [41] and Unified Parallel C (UPC) programming models [42] [43]) and is also particularly useful for parallel computing architectures with hardware ! " # 0.5 # " $ % " % 0.25 # 2&' % 1( ! "…”

Section: All-pairs Computation Frameworkmentioning

confidence: 99%

Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors

Liu,

Pan,

Green

et al. 2017

Preprint

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Pairwise association measure is an important operation in data analytics. Kendall's tau coefficient is one widely used correlation coefficient identifying non-linear relationships between ordinal variables. In this paper, we investigated a parallel algorithm accelerating all-pairs Kendall's tau coefficient computation via single instruction multiple data (SIMD) vectorized sorting on Intel Xeon Phis by taking advantage of many processing cores and 512-bit SIMD vector instructions. To facilitate workload balancing and overcome on-chip memory limitation, we proposed a generic framework for symmetric all-pairs computation by building provable bijective functions between job identifier and coordinate space. Performance evaluation demonstrated that our algorithm on one 5110P Phi achieves two orders-of-magnitude speedups over 16-threaded MATLAB and three ordersof-magnitude speedups over sequential R, both running on high-end CPUs. Besides, our algorithm exhibited rather good distributed computing scalability with respect to number of Phis. Source code and datasets are publicly available at http://lightpcc.sourceforge.net.

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“…GPUs are also explored for genome sequencing as in [25]. In [26], authors utilize HPC type many-core x86 clusters for NGS. However, the underlying behaviour in NGS applications, whether it is based on FMindex or graph based search, behaves like pointer chasing, with random accesses to the memory and low cache locality.…”

Section: Introductionmentioning

confidence: 99%

Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures

Qureshi

Herruzo

Zapater

et al. 2020

IEEE Trans. Comput.

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Next generation workloads, such as genome sequencing, have an astounding impact in the healthcare sector. Sequence alignment, the first step in genome sequencing, has experienced recent breakthroughs, which resulted in next generation sequencing (NGS). As NGS applications are memory bounded with random memory access patterns, we propose the use of high bandwidth memories like 3D stacked HBM2, instead of traditional DRAMs like DDR4, along with energy efficient compute cores to improve both performance and energy efficiency. Three state-of-the-art NGS applications, Bowtie2, BWA-MEM and HISAT2, are used as case studies to explore and optimize NGS computing architectures. Then, using the gem5-X architectural simulator, we obtain an overall 68% performance improvement and 71% energy savings using HBM2 instead of DDR4. Furthermore, we propose an architecture based on ARMv8 cores and demonstrate that 16 ARMv8 64-bit OoO cores with HBM2 outperforms 32-cores of Intel Xeon Phi Knights Landing (KNL) processor with 3D stacked memory. Moreover, we show that by using frequency scaling we can achieve up to 59% and 61% energy savings for ARM in-order and OoO cores, respectively. Lastly, we show that many ARMv8 in-order cores at 1.5GHz match the performance of fewer OoO cores at 2GHz, while attaining 4.5x energy savings.

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Parallel and Scalable Short-Read Alignment on Multi-Core Clusters Using UPC++

Cited by 12 publications

References 30 publications

Accelerating Sequence Alignments Based on FM-Index Using the Intel KNL Processor

Accelerating Sequence Alignments Based on FM-Index Using the Intel KNL Processor

Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors

Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures

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