Benchmarking and Profiling for Pre-Alignment Filtering in Memory
doi: 10.4121/94b9bfa7-42d5-418e-a947-a02c244921a6.v1
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doi: 10.4121/94b9bfa7-42d5-418e-a947-a02c244921a6
doi: 10.4121/94b9bfa7-42d5-418e-a947-a02c244921a6
Datacite citation style:
Shahroodi, Taha; Miao, Michael (2023): Benchmarking and Profiling for Pre-Alignment Filtering in Memory. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/94b9bfa7-42d5-418e-a947-a02c244921a6.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Benchmarking and Profiling for Pre-Alignment Filtering in Memory used for the evaluation of the papers titled RattlesnakeJake: A Fast and Accurate Pre-Alignment Filter Suitable for Computation-in-Memory, SieveMem : A Computation-in-Memory Architecture for Fast and Accurate Pre-Alignment , and the M.Sc. thesis of Michael Miao at QCE department.
history
- 2023-09-04 first online, published, posted
publisher
4TU.ResearchData
format
zipped shape files
organizations
TU Delft, Faculty of Engineering, Mathematics and Computer Science (EEMCS/EWI), Department of Quantum & Computer Engineering (QCE)Eidgenössische Technische Hochschule (ETH) Zürich
DATA - restricted access
Reason
Discussions in the Data Management Plan (DMP) Agreement.
End User Licence Agreement
The data that support all the results within this paper and other findings of this study are available from the corresponding author upon reasonable request.
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