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Recognizing the importance of whole genome alignment (WGA), the National Institutes for Health maintains LASTZ, a sequential WGA application. As genomic data grows, there is a compelling need for scalable, high-performance WGA. Unfortunately, high-sensitivity, `gapped' alignment which uses dynamic programming (DP) is slow, whereas faster alignment with ungapped filtering is often less sensitive. We develop FastZ, a GPU-accelerated, gapped WGA software which matches gapped LASTZ in sensitivity. FastZ employs a novel inspector-executor scheme in which (a) the lightweight inspector elides DP traceback except in common, extremely short alignments, where the inspector performs limited, eager traceback to eliminate the executor, and (b) executor trimming avoids unnecessary work. Further, FastZ employs register-based cyclic-buffering to drastically reduce memory traffic, and groups DP problems by size for load balance. FastZ running on an RTX 3080 GPU and our multicore implementation of LASTZ achieve 111x and 20x speedups over the sequential LASTZ, respectively.
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Supporting Multidimensional Data Analysis for High-School Students in the Era of Machine Learning
Machine Learning (ML) opens exciting scientific opportunities in K-12 STEM classrooms. However, students struggle with interpreting ML patterns due to limited data literacy. Face glyphs offer unique benefit by leveraging our brain’s facial feature processing. Yet, they have limitations like lacking contextual information and data biases. To address this, we created three enhanced face glyph visualizations: feature-independent and feature-aligned range views, and the sequential feature inspector. In a study with 25 high school students, feature-aligned range visualization helped contextual analysis, and the sequential feature inspector reduced missing data risks. Face glyphs also benefit the global interpretation of data.
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- Award ID(s):
- 2225227
- PAR ID:
- 10519137
- Publisher / Repository:
- International Society of the Learning Sciences
- Date Published:
- Page Range / eLocation ID:
- 1255 to 1258
- Format(s):
- Medium: X
- Location:
- Buffalo, New York
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.22318/icls2024.287761
