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Preskoči na sadržaj The challenges of the upcoming exascale supercomputing era in computational biochemistry Dr. Vedran Miletić ( group.miletic.net ) 😎 Group for Applications and Services on Exascale Research Infrastructure, Faculty of Informatics and Digital Technologies, University of Rijeka Research Class, FIDIT, UniRi, 26th January 2022 Stream and recording check OBS BBB Dr. Vedran Miletić's previous research work Dr. Branko Mikac's group at FER Dept. of Telecommunications What to do after finishing the Ph.D. thesis? 🤔 NVIDIA CUDA Teaching Center (later: GPU Education Center) research in Dr. Željko Svedružić’s Biomolecular Structure and Function Group and Group (BioSFGroup, svedruziclab.github.io ) postdoc in Dr. Frauke Gräter's Molecular Biomechanics (MBM) group at Heidelberg Institute for Theoretical Studies collaboration with GROMACS developers from KTH, Max Planck Institute for Biophysical Chemistry (now: Multidisciplinary Sciences), and University of Virginia RxTx Research returned from Heidelberg, became a Senior Lecturer 90% working hours teaching (courses + Bura supercomputer), 10% administration, 0% research started RxTx Research ( rxtxresearch.github.io ) collaboration with Patrik Nikolić ( www.nikoli.ch , former student researcher in BioSFGroup) vision: advancing the pharmaceutical drug research by improving the scientific software behind the scenes developed open-source high-throughput virtual screening engine RxDock ( rxdock.gitlab.io , until promotion to assist. prof.) Group for Applications and Services on Exascale Research Infrastructure (GASERI) The main interest: the application of exascale computing to solve problems in computational biochemistry The goal: design better-performing algorithms and offer their implementations for academic and industrial use to study the existing molecular systems faster study the existing molecular systems in more detail study larger molecular systems Introduction a supercomputer is a computer with a high level of performance as compared to a general-purpose computer also called high performance computer (HPC) measure: floating-point operations per second (FLOPS) PC -> teraFLOPS; Bura -> 100 teraFLOPS modern HPC -> 1 do 10 petaFLOPS, top 442 petaFLOPS future exascalar HPC -> 1+ exaFLOPS nearly exponential growth of FLOPS over time (source: Wikimedia Commons File:Supercomputers-history.svg ) More heterogeneous architectures require complex programming models different types of accelerators GPUs (half, single, double precision), TPUs/TCGPUs, FPGAs in-network and in-storage computation (e.g. Blue Field DPU ) several projects to adjust existing software for the exascale era Software for Exascale Computing (SPPEXA) Exascale Computing Project (ECP) European High-Performance Computing Joint Undertaking (EuropHPC JU) SPPEXA project GROMEX full title: Unified Long-range Electrostatics and Dynamic Protonation for Realistic Biomolecular Simulations on the Exascale principal investigators: Helmut Grubmüller (Max Planck Institute for Biophysical Chemistry, now Multidisciplinary Sciences) Holger Dachsel (Jülich Supercomputing Centre) Berk Hess (Stockholm University) molecular dynamics visualization: Electron transport chain GROMEX The particle mesh Ewald method (PME, currently state of the art in molecular simulation) does not scale to large core counts as it suffers from a communication bottleneck, and does not treat titratable sites efficiently. The fast multipole method (FMM) will enable an efficient calculation of long-range interactions on massively parallel exascale computers, including alternative charge distributions representing various forms of titratable sites. SPPEXA Projects - Phase 2 (2016 - 2018) Planned GROMACS developments (1/2) heterogeneous parallelism presently uses GPUs, could be expanded to also use DPUs custom-silicon Anton 2 supercomputer's hardware and software architecture could be an inspiration identification of packets that do not need to be delivered to all receivers and force reductions NVIDIA already offers free developer kits to interested parties for similar purposes Planned GROMACS developments (2/2) molecular dynamics simulations are periodic simulation box types : cubic, rhombic dodecahedron present design and implementation of the fast multipole method only supports cubic boxes it is possible to also support rhombic dodecahedron: ~30% less volume => ~30% less computation time per step required potentially apply for HrZZ UIP (if announced) Potential GROMACS developments Monte Carlo (Davide Mercadante, University of Auckland) many efforts over the years, none with broad acceptance should be rethought, and then designed and implemented from scratch with exascale in mind polarizable simulations using the classical Drude oscillator model (Justin Lemkul, Virginia Tech) should be parallelized for multi-node execution other drug design tools such as Random Acceleration Molecular Dynamics (Rebecca Wade, Heidelberg Institute for Theoretical Studies and Daria Kokh, Cancer Registry of Baden-Württemberg) Interesting developments in the broader computational biochemistry ecosystem RDKit RxDock 😇 data science: KNIME applied artificial intelligence, machine learning, neural networks, and deep learning e.g. Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discover AlphaFold RDKit and RxDock RDKit, the open-source chemoinformatics toolkit official blog frequently talks about molecular fingerprints database cartridge for PostgreSQL offers scalable molecular storage and retrieval RxDock predicts binding modes of small molecules to proteins and nucleic acids official comparison with rDock shows example videos in the late 2021. we submitted the study of 36 million molecules binding to SARS-CoV-2 main protease KNIME analytics platform set of Lego-like blocks that can be connected via GUI replaces scripting, easy to use for non-programmers state of the art of computational biochemistry methods: Schrödinger on KNIME Hub Vernalis RDKit AlphaFold protein structure != protein sequence: 100 EUR and 20 minutes structure: O(100 000) EUR and many years earlier computational solutions: Folding@home enabled by the evolution of GPUs and developments in AI Forbes calls it The Most Important Achievement In AI-Ever : 'Critical Assessment of Protein Structure Prediction co-founder and long-time protein folding expert John Moult put the AlphaFold achievement in historical context: "This is the first time a serious scientific problem has been solved by AI."' Potential development: HTVSDB web interface and REST API to a molecular database and molecular docking service open-source software so it could be hosted locally by other research groups at other universities unique features: molecular recommendation, federation based on RDKit, RxDock, and potentially AlphaFold long-term evolution on a best-effort basis Figure source: Cui W, Aouidate A, Wang S, Yu Q, Li Y and Yuan S (2020) Discovering Anti-Cancer Drugs via Computational Methods. Front. Pharmacol. 11:733. doi: 10.3389/fphar.2020.00733 Unified vision and specific applications high-throughput virtual screening and molecular dynamics simulations could be offered as a service to Croatian, regional, and EU research groups methods -> algorithms -> applications e.g. industry/academic group has a molecular target RxDock, RDKit (HTVSDB, KNIME/Python automation): millions of molecules -> tens of molecules GROMACS (KNIME/Python automation) -> tens of molecules -> several molecules Author: Vedran Miletić