RT Journal Article
T1 Genome sequence alignment-design space exploration for optimal performance and energy architectures
A1 Qureshi, Yasir Mahmood
A1 Herruzo, José M.
A1 Zapater, Marina
A1 Olcoz Herrero, Katzalin
A1 González Navarro, Sonia
A1 Plata, Óscar
A1 Atienza, David
AB 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 percent performance improvement and 71 percent 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 percent and 61 percent 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.
PB Institute of Electrical and Electronics Engineers (IEEE)
SN 0018-9340
YR 2021
FD 2021-12-01
LK https://hdl.handle.net/20.500.14352/4580
UL https://hdl.handle.net/20.500.14352/4580
LA eng
NO ©2021 IEEEThis work was supported in part by the ERC Consolidator Grant COMPUSAPIEN (GA No. 725657), the EC H2020 WiPLASH (GA No. 863337), the EC H2020 RECIPE (GA No. 801137), the EU FEDER and the Spanish MINECO (GA No. RTI2018-093684-B-I00), the Spanish CM (S2018/TCS-4423), Spanish MINECO TIN2016-80920-R, JA2012 P12-TIC-1470 and UMA18-FEDERJA-197 projects.
NO Unión Europea. H2020
NO Ministerio de Ciencia e Innovación  (MICINN) / FEDER
NO Ministerio de Ciencia e Innovación  (MICINN)
NO Comunidad de Madrid
NO Junta de Andalucía
NO Universidad de Málaga
DS Docta Complutense
RD 6 oct 2024