Abstract
We introduce an enhanced version of FaST-LMM that maintains the sensitivity of this software when applied to identify epistasis interactions while delivering an acceleration factor that is close to 7.5\(\times \) on a server equipped with a state-of-the-art graphics coprocessor. This performance boost is obtained from the combined effects of integrating a dictionary for faster storage of the test results; a re-organization of the original FaST-LMM Python code; and off-loading of compute-intensive parts to the graphics accelerator.
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There is also a FaST-LMM version written in C++ but, according to the authors, the Python code contains the most advanced features.
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References
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Martínez, H. et al. (2017). Accelerating FaST-LMM for Epistasis Tests. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_40
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DOI: https://doi.org/10.1007/978-3-319-65482-9_40
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