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× 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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Martínez, H., Barrachina, S., Castillo, M., Quintana-Ortí, E. S., De Argila, J. R., Farré, X., & Navarro, A. (2017). Accelerating FaST-LMM for epistasis tests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10393 LNCS, pp. 548–557). Springer Verlag. https://doi.org/10.1007/978-3-319-65482-9_40
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