Towards chemically accurate QM/MM simulations on GPUs.

Computational chemistry simulations are extensively used to model natural phenomena. To maintain performance similar to molecular mechanics, but achieve comparable accuracy to quantum mechanical calculations, many researchers are using hybrid QM/MM methods. In this article we evaluate our GPU-accelerated ONIOM implementation by measurements on the crambin and HIV integrase proteins with different size QM model systems. We demonstrate that by using a larger QM region, a better energy accuracy can be achieved at the expense of simulation time. This trade-off is important to consider for the researcher running QM/MM calculations. Furthermore, we show that the ONIOM energy monotonically approaches the pure quantum mechanical energy of the whole system. The experiments are made feasible by utilizing the cutting-edge BrianQC quantum chemistry module for Hartree-Fock level SCF and our GPU-accelerated MMFF94 force field implementation for molecular mechanics calculations.

Calculation of Quantum Chemical Two-Electron Integrals by Applying Compiler Technology on GPU.

In this article, we present an effective approach to calculate quantum chemical two-electron integrals over basis sets consisting of Gaussian-type basis functions on graphical processing unit (GPU). Our framework generates several different variants called routes to the same integral problem with different integral algorithms (McMurchie-Davidson, Head-Gordon-Pople, and Rys) and precision. Each route is benchmarked on more GPU architectures, and with this data, a model is fitted to select the best available route for an integral task given a GPU architecture. Moreover, this approach supports the computation of high angular momentum orbitals up to g effectively on GPU, tested up to cc-pVQZ-sized basis sets. Rigorous analysis is shown regarding the effectiveness of our method. Molecule simulations with several basis sets are measured using NVIDIA GTX 1080 Ti, NVIDIA P100, and NVIDIA V100 cards.

Fast DRR generation for 2D to 3D registration on GPUs.

PURPOSE: The generation of digitally reconstructed radiographs (DRRs) is the most time consuming step on the CPU in intensity based two-dimensional x-ray to three-dimensional (CT or 3D rotational x-ray) medical image registration, which has application in several image guided interventions. This work presents optimized DRR rendering on graphical processor units (GPUs) and compares performance achievable on four commercially available devices. METHODS: A ray-cast based DRR rendering was implemented for a 512 × 512 × 72 CT volume. The block size parameter was optimized for four different GPUs for a region of interest (ROI) of 400 × 225 pixels with different sampling ratios (1.1%-9.1% and 100%). Performance was statistically evaluated and compared for the four GPUs. The method and the block size dependence were validated on the latest GPU for several parameter settings with a public gold standard dataset (512 × 512 × 825 CT) for registration purposes. RESULTS: Depending on the GPU, the full ROI is rendered in 2.7-5.2 ms. If sampling ratio of 1.1%-9.1% is applied, execution time is in the range of 0.3-7.3 ms. On all GPUs, the mean of the execution time increased linearly with respect to the number of pixels if sampling was used. CONCLUSIONS: The presented results outperform other results from the literature. This indicates that automatic 2D to 3D registration, which typically requires a couple of hundred DRR renderings to converge, can be performed quasi on-line, in less than a second or depending on the application and hardware in less than a couple of seconds. Accordingly, a whole new field of applications is opened for image guided interventions, where the registration is continuously performed to match the real-time x-ray.