GPU: Accelerated Computation Routines for Quantum Trajectories Method

Mirza Zuber Baig, Vivek Nainwal, Anoop Kumar +1 more

Joanna Wiśniewska, Marek Sawerwain, W. Leoński

Nowadays, a dynamic progress in computational techniques allows for development of various methods, which offer significant speed-up of computations, especially those related to the problems of quantum optics and quantum computing. In this work, we propose computational solutions which re-implement the quantum trajectory method (QTM) algorithm in modern parallel computation environments in which multi-core CPUs and modern many-core GPUs can be used. In consequence, new computational routines are developed in more effective way than those applied in other commonly used packages, such as Quantum Optics Toolbox (QOT) for Matlab or QuTIP for Python.

Kyle D. Fernandes, C. Alicia Renison, Kevin J. Naidoo

We present here a set of algorithms that completely rewrites the Hartree-Fock (HF) computations common to many legacy electronic structure packages (such as GAMESS-US, GAMESS-UK, and NWChem) into a massively parallel compute scheme that takes advantage of hardware accelerators such as Graphical Processing Units (GPUs). The HF compute algorithm is core to a library of routines that we name the Quantum Supercharger Library (QSL). We briefly evaluate the QSL's performance and report that it accelerates a HF 6-31G Self-Consistent Field (SCF) computation by up to 20 times for medium sized molecules (such as a buckyball) when compared with mature Central Processing Unit algorithms available in the legacy codes in regular use by researchers. It achieves this acceleration by massive parallelization of the one- and two-electron integrals and optimization of the SCF and Direct Inversion in the Iterative Subspace routines through the use of GPU linear algebra libraries. © 2015 Wiley Periodicals, Inc.

Enrico Piccinini, Claudia Benedetti, Ilaria Siloi +2 more

José M. Rodríguez-Borbón, Xian Wang, Adrián Perez Diéguez +2 more

We present an open-source software package, TRAVOLTA (Terrific Refinements to Accelerate, Validate, and Optimize Large Time-dependent Algorithms), for carrying out massively parallelized quantum optimal control calculations on GPUs. The TRAVOLTA software package is a significant overhaul of our previous NIC-CAGE algorithm and also includes algorithmic improvements to the gradient ascent procedure to enable faster convergence. We examine three different variants of GPU parallelization to assess their performance in constructing optimal control fields in a variety of quantum systems. In addition, we provide several examples with extensive benchmarks on our GPU-enhanced TRAVOLTA code to show that it generates the same results as previous CPU-based algorithms but with a speedup that is more than ten times faster. Our GPU enhancements and algorithmic improvements enable large quantum optimal control calculations that can be efficiently and routinely executed on modern multi-core computational hardware. Program Title: TRAVOLTA CPC Library link to program files: https://doi.org/10.17632/grwppm37rn.1 Licensing provisions: GNU General Public License 3 Programming language: C++ and CUDA Supplementary material: Brief review of LU decomposition and raw numerical values used to generate Fig. 6 in the main text. Nature of problem: The TRAVOLTA software package utilizes GPU accelerated routines and new algorithmic improvements to compute optimized electric fields that can drive a system from a known initial vibrational eigenstate to a specified final quantum state with a large (≈1) transition probability. Solution method: Quantum control, GPU acceleration, analytic gradients, Crank-Nicolson propagation, and gradient ascent optimization.

Eladio Gutiérrez, Sergio Romero, Marı́a A. Trenas +1 more