Bilkent University
Department of Computer Engineering


Accelerating Genome Sequence Analysis via Efficient Hardware-Algorithm Co-Design


Damla enol al
Ph.D. candidate in the SAFARI Research Group @ Carnegie Mellon University

Genome sequence analysis can enable significant advancements in areas such as personalized medicine, evolution, and forensics. However, effectively leveraging genome sequencing for advanced scientific and medical breakthroughs requires very high computational power. As prior works have shown, many of the core steps in genome sequence analysis are bottlenecked by the current capabilities of computer systems, as these steps must process a large amount of data.

In this talk, I will describe our ongoing research on accelerating genome sequence analysis by co-designing fast and efficient algorithms along with scalable and energy-efficient customized hardware. I will first discuss our analysis of the multiple steps and the associated tools in the genome sequencing pipeline, to understand where the current tools and algorithms are bottlenecked in the presence of new sequencing technologies (e.g., nanopore sequencing). We find that read alignment is one of the key steps of the pipeline. I will next introduce BitMAC, an in-memory read alignment accelerator for both short and long reads. We find that BitMAC is significantly faster and more energy efficient than state-of-the-art read alignment software and hardware. Finally, I will describe our ongoing and future work on a variety of acceleration mechanisms for other key steps of the genome sequence analysis pipeline, including specialized accelerators, in-memory processing engines, and SIMD architectures.

Bio: Damla Senol Cali is a Ph.D. candidate in the SAFARI Research Group @ Carnegie Mellon University, advised by Prof. Onur Mutlu and Dr. Saugata Ghose. She obtained her M.S. in ECE from Carnegie Mellon University in 2019, and her B.S. in Computer Engineering from Bilkent University in 2015. Her research focuses on hardware/software co-design for accelerating bioinformatics applications and computational methods for the analysis of high-throughput sequencing (HTS) and nanopore sequencing data. She is also excited about memory systems and processing-in-memory. During her Ph.D., she interned at Intel Labs for 3 months.


DATE: 25 December 2019, Wednesday @ 11:00