Türk Ulusal Bilim e-Altyapısı TRUBA’da Moleküler Dinamik Paketi GROMACS’in Performans Optimizasyonu

Öz

Yüksek performanslı hesaplama sistemlerinin kullanımının artmasıyla, bu sistemlerde çalıştırılan programların performans optimizasyonu öncelikli hale gelmiştir. Bu duruma istinaden, bu çalışmamızda, yaygın olarak kullanılan moleküler dinamik paketi GROMACS’in, TÜBİTAK ULAKBİM tarafından kullanıma sunulan TRUBA hesaplama kümelerindeki en iyi performans kriterlerini bulmayı hedefledik. Performans tarama çalışmamız sırasında, farklı hesaplama kümelerinde, farklı CPU/GPU çekirdek oranı ve GROMACS versiyonlarını denedik. Bu süreç sonunda en iyi performanslı hesaplama kümesi akya-cuda, en iyi CPU/GPU çekirdek sayı oranı 40/1 ve en hızlı GROMACS versiyonu GROMACS 2020 olarak tespit edilmiştir. Benzer bir çalışma yürütecek araştırmacıların yararlanması adına, performans optimizasyon dosyalarımız ve ayrıntılı sonuçlarımız https://github.com/CSB-KaracaLab/gmx_performance_on_HPC adresinde incelemeye açılmıştır.

Anahtar Kelimeler

• Moleküler Dinamik
• Yüksek Başarımlı Hesaplama Kümeleri
• Optimizasyon
• GROMACS

Kaynakça

1. [1] Alder, B. J., & Wainwright, T. E. (1959). Studies in molecular dynamics. I. General method. The Journal of Chemical Physics, 31(2). https://doi.org/10.1063/1.1730376
2. [2] Rahman, A. (1964). Correlations in the motion of atoms in liquid argon. Physical Review, 136(2A). https://doi.org/10.1103/PhysRev.136.A405
3. [3] Páll, S., Abraham, M. J., Kutzner, C., Hess, B., & Lindahl, E. (2015). Tackling exascale software challenges in molecular dynamics simulations with GROMACS. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8759. https://doi.org/10.1007/978-3-319-15976-8_1
4. [4] Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindah, E. (2015). Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2. https://doi.org/10.1016/j.softx.2015.06.001
5. [5] Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005). Scalable molecular dynamics with NAMD. In Journal of Computational Chemistry (Vol. 26, Issue 16). https://doi.org/10.1002/jcc.20289
6. [6] Pearlman, D. A., Case, D. A., Caldwell, J. W., Ross, W. S., Cheatham, T. E., DeBolt, S., Ferguson, D., Seibel, G., & Kollman, P. (1995). AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Computer Physics Communications, 91(1–3). https://doi.org/10.1016/0010-4655(95)00041-D
7. [7] Bird, A. (2007). Perceptions of epigenetics. In Nature (Vol. 447, Issue 7143). https://doi.org/10.1038/nature05913
8. [8] Mazzio, E. A., & Soliman, K. F. A. (2012). Basic concepts of epigenetics impact of environmental signals on gene expression. In Epigenetics (Vol. 7, Issue 2). https://doi.org/10.4161/epi.7.2.18764

9. [9] Khavari, D. A., Sen, G. L., & Rinn, J. L. (2010). DNA methylation and epigenetic control of cellular differentiation. In Cell Cycle (Vol. 9, Issue 19). https://doi.org/10.4161/cc.9.19.13385
10. [10] Lee, J. H., Hart, S. R. L., & Skalnik, D. G. (2004). Histone Deacetylase Activity Is Required for Embryonic Stem Cell Differentiation. Genesis, 38(1). https://doi.org/10.1002/gene.10250
11. [11] Weinhold, B. (2006). Epigenetics: the science of change. Environmental Health Perspectives, 114(3). https://doi.org/10.1289/ehp.114-a160
12. [12] Kulis, M., & Esteller, M. (2010). DNA Methylation and Cancer. In Advances in Genetics (Vol. 70, Issue C). https://doi.org/10.1016/B978-0-12-380866-0.60002-2
13. [13] Law, J. A., & Jacobsen, S. E. (2010). Establishing, maintaining and modifying DNA methylation patterns in plants and animals. In Nature Reviews Genetics (Vol. 11, Issue 3). https://doi.org/10.1038/nrg2719
14. [14] Chédin, F. (2011). The DNMT3 family of mammalian de novo DNA methyltransferases. In Progress in Molecular Biology and Translational Science (Vol. 101). https://doi.org/10.1016/B978-0-12-387685-0.00007-X
15. [15] Zhang, Z. M., Lu, R., Wang, P., Yu, Y., Chen, D., Gao, L., Liu, S., Ji, D., Rothbart, S. B., Wang, Y., Wang, G. G., & Song, J. (2018). Structural basis for DNMT3A-mediated de novo DNA methylation. Nature, 554(7692). https://doi.org/10.1038/nature25477
16. [16] Norvil, A. B., Petell, C. J., Alabdi, L., Wu, L., Rossie, S., & Gowher, H. (2018). Dnmt3b Methylates DNA by a Noncooperative Mechanism, and Its Activity Is Unaffected by Manipulations at the Predicted Dimer Interface. Biochemistry, 57(29). https://doi.org/10.1021/acs.biochem.6b00964
17. [17] TRUBA. https://www.truba.gov.tr/index.php/en/main-page/
18. [18] TRUBA Wiki Sayfası. http://wiki.truba.gov.tr/index.php/Ana_sayfa
19. [19] Ivani, I., Dans, P. D., Noy, A., Pérez, A., Faustino, I., Hospital, A., Walther, J., Andrio, P., Goñi, R., Balaceanu, A., Portella, G., Battistini, F., Gelpí, J. L., González, C., Vendruscolo, M., Laughton, C. A., Harris, S. A., Case, D. A., & Orozco, M. (2015). Parmbsc1: A refined force field for DNA simulations. Nature Methods, 13(1). https://doi.org/10.1038/nmeth.3658