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00925njm 22002777a 4500 dc Qureshi, Yasir Mahmood author Herruzo-Ruiz, José Manuel author Zapater, Marina author Olcoz, Katzalin author González-Navarro, Sonia author Óscar, Plata author Atienza, David author 2021-12 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. Y. M. Qureshi et al., "Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures," in IEEE Transactions on Computers, vol. 70, no. 12, pp. 2218-2233, 1 Dec. 2021, doi: 10.1109/TC.2020.3041402. https://hdl.handle.net/10630/29314 https://doi.org/10.1109/TC.2020.3041402 Genoma humano - Proceso de datos Bioinformática Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures