Accelerating MOPAC for high-performance computers with Multi- GPUs and Many-core CPUs

Gerd Rocha

Abstract

Our research group has been contributing to MOPAC since 2004 when we developed Sparkle Model for lanthanide complexes [1][2] and RM1 semiempirical method for organic chemistry[3]. Since 2008, we have been investing our efforts to accelerate MOPAC through re-coding time-consuming parts of MOPAC code by applying both shared-memory and fine-graining parallel strategies[4][5][6] and coupling MOPAC with molecular dynamics codes for performing hybrid QM/MM simulations[7]. Single point energy, geometry optimization, and vibrational frequency calculations were accelerated for high-performance computers containing many GPUs and multi-core CPUs. Our efforts produced speedups of over 50 times compared to the serial version of MOPAC. This talk will present our new developments in parallelizing semiempirical calculations for large molecules[6].

Reference

  1. Rocha GB, Freire RO, da Costa, NB, et al (2004) Sparkle Model for AM1 Calculation of Lanthanide Complexes: Improved Parameters for Europium. Inorg Chem 43:2346–2354. https://doi.org/10.1021/ic034882p
  2. Freire RO, Rocha GB, Simas AM (2005) Sparkle model for the calculation of lanthanide complexes: AM1 parameters for Eu(III), Gd(III), and Tb(III). Inorg Chem 44:3299–3310. https://doi.org/10.1021/ic048530+
  3. Rocha GB, Freire RO, Simas AM, Stewart JJP (2006) RM1: A reparameterization of AM1 for H, C, N, O, P, S, F, Cl, Br, and I. J Comput Chem 27:. https://doi.org/10.1002/jcc.20425
  4. Birgin EG, Martínez JM, Martínez L, Rocha GB (2013) Sparse projected-gradient method as a linear- scaling low-memory alternative to diagonalization in self-consistent field electronic structure calculations. J Chem Theory Comput 9:1043–1051. https://doi.org/10.1021/ct3009683
  5. Maia JDC, Urquiza Carvalho GA, Mangueira CP, et al (2012) GPU linear algebra libraries and GPGPU programming for accelerating MOPAC semiempirical quantum chemistry calculations. J Chem Theory Comput 8:3072–3081. https://doi.org/10.1021/ct3004645
  6. Maia JDC, dos Anjos Formiga Cabral L, Rocha GB (2020) GPU algorithms for density matrix methods on MOPAC: linear scaling electronic structure calculations for large molecular systems. J Mol Model 26:313. https://doi.org/10.1007/s00894-020-04571-6
  7. Melo MCR, Bernardi RC, Rudack T, et al (2018) NAMD goes quantum: an integrative suite for hybrid simulations. Nat Methods 15:351–354. https://doi.org/10.1038/nmeth.4638