FastGrid -- The Accelerated AutoGrid Potential Maps Generation for Molecular Docking

The AutoDock suite is widely used molecular docking software consisting of two main programs -- AutoGrid for precomputation of potential grid maps and AutoDock for docking into potential grid maps. In this paper, the acceleration of potential maps generation based on AutoGrid and its implementation called FastGrid is presented. The most computationally expensive algorithms are accelerated using GPU, the rest of algorithms run on CPU with asymptotically lower time complexity that has been obtained using more sophisticated data structures than in the original AutoGrid code. Moreover, the CPU implementation is parallelized to fully exploit computational power of machines that are equipped with more CPU cores than GPUs. Our implementation outperforms original AutoGrid more than 400x for large, but quite common molecules and sufficiently large grids

Caver Web 1.0: identification of tunnels and channels in proteins and analysis of ligand transport

Caver Web 1.0 is a web server for comprehensive analysis of protein tunnels and channels, and study of the ligands’ transport through these transport pathways. Caver Web is the first interactive tool allowing both the analyses within a single graphical user interface. The server is built on top of the abundantly used tunnel detection tool Caver 3.02 and CaverDock 1.0 enabling the study of the ligand transport. The program is easy-to-use as the only required inputs are a protein structure for a tunnel identification and a list of ligands for the transport analysis. The automated guidance procedures assist the users to set up the calculation in a way to obtain biologically relevant results. The identified tunnels, their properties, energy profiles and trajectories for ligands’ passages can be calculated and visualized. The tool is very fast (2–20 min per job) and is applicable even for virtual screening purposes. Its simple setup and comprehensive graphical user interface make the tool accessible for a broad scientific community. The server is freely available at https://loschmidt.chemi.muni.cz/caverweb.Caver Web 1.0 je webový server pro komplexní analýzu tunelů a kanálů v proteinech a pro studium transportu ligandu přes tyto transportní cesty. Caver Web je první interaktivní nástroj umožňující obě analýzy v jednom grafickém uživatelském rozhraní. Server je vybudován nad hojně užívaným nástrojem pro detekci tunelů Caver 3.02 a nad CaverDock 1.0 umožňujícím studium transportu ligandů. Program se snadno ovládá, jelikož vyžaduje pouze strukturu proteinu pro identifikaci tunelů a seznam ligandů pro analýzu transportu. Procedury pro automatické nastavení výpočtů asistují uživatelům tak, aby získali biologicky relevantní výsledky. Identifikované tunely, jejich vlastnosti, energetické profily a trajektorie průchodů ligandů mohou být spočítány a vizualizovány. Nástroj je velmi rychlý (2-20 minut na úlohu) a je použitelný dokonce pro virtuální screening. Jeho snadné nastavení a ucelené grafické rozhraní dělá nástroj přístupným pro širokou vědeckou komunitu. Server je volně k dispozici na https://loschmidt.chemi.muni.cz/caverweb

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Autotuning of performance-relevant source-code parameters allows to automatically tune applications without hard coding optimizations and thus helps with keeping the performance portable. In this paper, we introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA. Using our Kernel Tuning Toolkit, we show that with autotuning most of the kernels reach near-peak performance on various GPUs and outperform baseline implementations on CPUs and Xeon Phis. Our evaluation also demonstrates that autotuning is key to performance portability. In addition to offline tuning, we also introduce dynamic autotuning of code optimization parameters during application runtime. With dynamic tuning, the Kernel Tuning Toolkit enables applications to re-tune performance-critical kernels at runtime whenever needed, for example, when input data changes. Although it is generally believed that autotuning spaces tend to be too large to be searched during application runtime, we show that it is not necessarily the case when tuning spaces are designed rationally. Many of our kernels reach near peak-performance with moderately sized tuning spaces that can be searched at runtime with acceptable overhead. Finally we demonstrate, how dynamic performance tuning can be integrated into a real-world application from cryo-electron microscopy domain

Kernel Tuning Toolkit

Kernel Tuning Toolkit (KTT) is an autotuning framework for CUDA, OpenCL and Vulkan kernels. KTT provides advanced autotuning features such as support for both dynamic (online) and offline tuning, and an ability to tune multiple kernels together with shared tuning parameters. Furthermore, it offers customization features that make integration into larger software suites possible. The framework handles all major steps required for autotuning implementation, including configuration space creation and exploration, kernel code execution and output validation. The public API is available natively in C++ and via bindings in Python

Accelerated RMSD Calculation for Molecular Metadynamics

In this paper, we introduce GPU acceleration of RMSD approximation, which is computationally demanding task in molecular metadynamics. Comparing to tuned CPU implementation, we have reached 4.4x speedup using mid-end GPU. The scaling of our GPU implementation is sufficient to be usable in real-world application

FlexAlign: An Accurate and Fast Algorithm for Movie Alignment in Cryo-Electron Microscopy

© 2020 by the authors.Cryogenic Electron Microscopy (Cryo-EM) has been established as one of the key players in Structural Biology. It can reconstruct a 3D model of the sample at the near-atomic resolution, which led to a Method of the year award by Nature, and the Nobel Prize in 2017. With the growing number of facilities, faster microscopes, and new imaging techniques, new algorithms are needed to process the so-called movies data produced by the microscopes in real-time, while preserving a high resolution and maximum of additional information. In this article, we present a new algorithm used for movie alignment, called FlexAlign. FlexAlign is able to correctly compensate for the shift produced during the movie acquisition on-the-fly, using the current generation of hardware. The algorithm performs a global and elastic local registration of the movie frames using Cross-Correlation and B-spline interpolation for high precision. We show that our execution time is compatible with real-time correction and that we preserve the high-resolution information up to high frequency.The authors would like to acknowledge financial support from: the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Economy and Competitiveness (BIO2016-76400-R), the Instituto de Salud Carlos III, PT17/0009/0010 (ISCIII-SGEFI/ERDF) and the European Union and Horizon 2020 through grant: CORBEL (INFRADEV-01-2014-1, Proposal 654248), INSTRUCT-ULTRA (INFRADEV-03-016-2017, Proposal 731005), EOSC Life (INFRAEOSC-04-2018, Proposal: 824087) and HighResCells (ERC-2018-SyG, Proposal: 810057). The project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660021. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713673. The work was supported from European Regional Development Fund-Project “CERIT Scientific Cloud” (No. CZ.02.1.01/0.0/0.0/16_013/0001802).Peer reviewe

A GPU acceleration of 3-D Fourier reconstruction in cryo-EM

Cryo-electron microscopy is a popular method for macromolecules structure determination. Reconstruction of a 3-D volume from raw data obtained from a microscope is highly computationally demanding. Thus, acceleration of the reconstruction has a great practical value. In this article, we introduce a novel graphics processing unit (GPU)-friendly algorithm for direct Fourier reconstruction, one of the main computational bottlenecks in the 3-D volume reconstruction pipeline for some experimental cases (particularly those with a large number of images and a high internal symmetry). Contrary to the state of the art, our algorithm uses a gather memory pattern, improving cache locality and removing race conditions in parallel writing into the 3-D volume. We also introduce a finely tuned CUDA implementation of our algorithm, using auto-tuning to search for a combination of optimization parameters maximizing performance on a given GPU architecture. Our CUDA implementation is integrated in widely used software Xmipp, version 3.19, reaching 11.4× speedup compared to the original parallel CPU implementation using GPU with comparable power consumption. Moreover, we have reached 31.7× speedup using four GPUs and 2.14×–5.96× speedup compared to optimized GPU implementation based on a scatter memory pattern