46 citations · 139 across the 15 of their papers we have counts for
6 papers · 1 filter
TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering
Meryem Banu Cavlak, Gagandeep Singh, Mohammed Alser +6
Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art…
Morpheus: Extending the Last Level Cache Capacity in GPU Systems Using Idle GPU Core Resources
Sina Darabi, Mohammad Sadrosadati, Joël Lindegger +6
Graphics Processing Units (GPUs) are widely-used accelerators for data-parallel applications. In many GPU applications, GPU memory bandwidth bottlenecks performance, causing underu…
Scrooge: A Fast and Memory-Frugal Genomic Sequence Aligner for CPUs, GPUs, and ASICs
Joël Lindegger, Damla Senol Cali, Mohammed Alser +3
Pairwise sequence alignment is a very time-consuming step in common bioinformatics pipelines. Speeding up this step requires heuristics, efficient implementations, and/or hardware…
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
Can Firtina, Kamlesh Pillai, Gurpreet S. Kalsi +10
Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequen…
SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping
Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger +15
A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e.,…
Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm
Joël Lindegger, Damla Senol Cali, Mohammed Alser +2
We improve on GenASM, a recent algorithm for genomic sequence alignment, by significantly reducing its memory footprint and bandwidth requirement. Our algorithmic improvements redu…