Search Results for author: Damla Senol Cali

Found 8 papers, 8 papers with code

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis

1 code implementation20 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.

Multiple Sequence Alignment

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

1 code implementation16 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.

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

2 code implementations16 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

AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes

1 code implementation18 Dec 2019 Jeremie S. Kim, Can Firtina, Meryem Banu Cavlak, Damla Senol Cali, Mohammed Alser, Nastaran Hajinazar, Can Alkan, Onur Mutlu

There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i. e., remapping).

Understanding the Interactions of Workloads and DRAM Types: A Comprehensive Experimental Study

7 code implementations20 Feb 2019 Saugata Ghose, Tianshi Li, Nastaran Hajinazar, Damla Senol Cali, Onur Mutlu

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

Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm

1 code implementation12 Feb 2019 Can Firtina, Jeremie S. Kim, Mohammed Alser, Damla Senol Cali, A. Ercument Cicek, Can Alkan, Onur Mutlu

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.

GRIM-Filter: Fast Seed Location Filtering in DNA Read Mapping Using Processing-in-Memory Technologies

1 code implementation2 Nov 2017 Jeremie S. Kim, Damla Senol Cali, Hongyi Xin, Donghyuk Lee, Saugata Ghose, Mohammed Alser, Hasan Hassan, Oguz Ergin, Can Alkan, Onur Mutlu

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.

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