19 citations · 28 across the 5 of their papers we have counts for
6 papers
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…
A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems
Safaa Diab, Amir Nassereldine, Mohammed Alser +3
Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory architectures alleviate thi…
An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System
Juan Gómez-Luna, Yuxin Guo, Sylvan Brocard +5
Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result,…
Exploiting Near-Data Processing to Accelerate Time Series Analysis
Ivan Fernandez, Ricardo Quislant, Christina Giannoula +5
Time series analysis is a key technique for extracting and predicting events in domains as diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and m…
Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures
Geraldo F. Oliveira, Juan Gómez-Luna, Saugata Ghose +1
The increasing prevalence and growing size of data in modern applications have led to high costs for computation in traditional processor-centric computing systems. Moving large vo…
Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases
Geraldo F. Oliveira, Amirali Boroumand, Saugata Ghose +2
Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take plac…