1 code implementation • 20 Jul 2022 • Can Firtina, Kamlesh Pillai, Gurpreet S. Kalsi, Bharathwaj Suresh, Damla Senol Cali, Jeremie Kim, Taha Shahroodi, Meryem Banu Cavlak, Joel Lindegger, Mohammed Alser, Juan Gómez Luna, Sreenivas Subramoney, Onur Mutlu
When we analyze the state-of-the-art works, we find that there is a pressing need for a flexible, high-performant, and energy-efficient hardware-software co-design to efficiently and effectively solve all the major inefficiencies in the Baum-Welch algorithm for pHMMs.
1 code implementation • 16 Dec 2021 • Can Firtina, Jisung Park, Mohammed Alser, Jeremie S. Kim, Damla Senol Cali, Taha Shahroodi, Nika Mansouri Ghiasi, Gagandeep Singh, Konstantinos Kanellopoulos, Can Alkan, Onur Mutlu
We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seeds matches.
2 code implementations • 16 Sep 2020 • Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl, Can Firtina, Lavanya Subramanian, Jeremie S. Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, Anant Nori, Allison Scibisz, Sreenivas Subramoney, Can Alkan, Saugata Ghose, Onur Mutlu
Unfortunately, it is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data.
Hardware Architecture Genomics
There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i. e., remapping).
As a result, the combined DRAM-workload behavior is often difficult to intuitively determine today, which can hinder memory optimizations in both hardware and software.
Hardware Architecture Performance
Our experiments with real read sets demonstrate that Apollo is the only algorithm that 1) uses reads from any sequencing technology within a single run and 2) scales well to polish large assemblies without splitting the assembly into multiple parts.
State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i. e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i. e., sequence alignment) to determine the origin of the read.