Molecular modeling on GPUs

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Molecular modeling on GPUs - Wikipedia

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Ionic liquid simulation on GPU (Abalone)

Molecular modeling on GPU is the technique of using a graphics processing unit (GPU) for molecular simulations.^[1]

In 2007, Nvidia introduced video cards that could be used not only to show graphics but also for scientific calculations. These cards include many arithmetic units (as of 2016^[update], up to 3,584 in Tesla P100) working in parallel. Long before this event, the computational power of video cards was purely used to accelerate graphics calculations. What was new is that Nvidia made it possible to develop parallel programs in a high-level application programming interface (API) named CUDA. This technology substantially simplified programming by enabling programs to be written in C/C++. More recently, OpenCL allows cross-platform GPU acceleration.

Quantum chemistry calculations^[2]^[3]^[4]^[5]^[6]^[7] and molecular mechanics simulations^[8]^[9]^[10] (molecular modeling in terms of classical mechanics) are among beneficial applications of this technology. The video cards can accelerate the calculations tens of times, so a PC with such a card has the power similar to that of a cluster of workstations based on common processors.

GPU accelerated molecular modelling software

Programs

Abalone – Molecular Dynamics (Benchmark)
ACEMD on GPUs since 2009 Benchmark
AMBER on GPUs version
Ascalaph on GPUs version – Ascalaph Liquid GPU
AutoDock – Molecular docking
BigDFT Ab initio program based on wavelet
BrianQC Quantum chemistry (HF and DFT) and molecular mechanics
Blaze ligand-based virtual screening
CHARMM – Molecular dynamics [1]
CP2K Ab initio molecular dynamics
Desmond (software) on GPUs, workstations, and clusters
Firefly (formerly PC GAMESS)
FastROCS
GOMC – GPU Optimized Monte Carlo simulation engine
GPIUTMD – Graphical processors for Many-Particle Dynamics
GPU4PySCF – GPU accelerated plugin package for PySCF
GPUMD - A light weight general-purpose molecular dynamics code
GROMACS on GPUs ^[11]
HALMD – Highly Accelerated Large-scale MD package
HOOMD-blue Archived 2011-11-11 at the Wayback Machine – Highly Optimized Object-oriented Many-particle Dynamics—Blue Edition
LAMMPS on GPUs version – lammps for accelerators
LIO DFT-Based GPU optimized code - [2]
Octopus has support for OpenCL.
oxDNA – DNA and RNA coarse-grained simulations on GPUs
PWmat – Plane-Wave Density Functional Theory simulations
RUMD - Roskilde University Molecular Dynamics^[12]
TeraChem – Quantum chemistry and ab initio Molecular Dynamics
TINKER on GPUs.^[13]
VMD & NAMD on GPUs versions
YASARA runs MD simulations on all GPUs using OpenCL.

API

BrianQC – has an open C level API for quantum chemistry simulations on GPUs, provides GPU-accelerated version of Q-Chem and PSI
OpenMM – an API for accelerating molecular dynamics on GPUs, v1.0 provides GPU-accelerated version of GROMACS
mdcore – an open-source platform-independent library for molecular dynamics simulations on modern shared-memory parallel architectures.

Distributed computing projects

GPUGRID distributed supercomputing infrastructure
Folding@home distributed computing project
Exscalate4Cov large-scale virtual screening experiment

References

^ Stone JE, Phillips JC, Freddolino PL, Hardy DJ, Trabuco LG, Schulten K (December 2007). "Accelerating molecular modeling applications with graphics processors". Journal of Computational Chemistry. 28 (16): 2618–2640. CiteSeerX 10.1.1.466.3823. doi:10.1002/jcc.20829. PMID 17894371. S2CID 15313533.
^ Yasuda K (August 2008). "Accelerating Density Functional Calculations with Graphics Processing Unit". Journal of Chemical Theory and Computation. 4 (8): 1230–1236. doi:10.1021/ct8001046. PMID 26631699.
^ Yasuda K (February 2008). "Two-electron integral evaluation on the graphics processor unit". Journal of Computational Chemistry. 29 (3): 334–342. CiteSeerX 10.1.1.498.364. doi:10.1002/jcc.20779. PMID 17614340. S2CID 8078401.
^ Vogt L, Olivares-Amaya R, Kermes S, Shao Y, Amador-Bedolla C, Aspuru-Guzik A (March 2008). "Accelerating resolution-of-the-identity second-order Møller-Plesset quantum chemistry calculations with graphical processing units". The Journal of Physical Chemistry A. 112 (10): 2049–2057. Bibcode:2008JPCA..112.2049V. doi:10.1021/jp0776762. PMID 18229900. S2CID 4566211.
^ Ufimtsev IS, Martínez TJ (February 2008). "Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation". Journal of Chemical Theory and Computation. 4 (2): 222–231. doi:10.1021/ct700268q. PMID 26620654.
^ Ivan S. Ufimtsev & Todd J. Martinez (2008). "Graphical Processing Units for Quantum Chemistry". Computing in Science & Engineering. 10 (6): 26–34. Bibcode:2008CSE....10f..26U. doi:10.1109/MCSE.2008.148. S2CID 10225262.
^ Tornai GJ, Ladjánszki I, Rák Á, Kis G, Cserey G (October 2019). "Calculation of Quantum Chemical Two-Electron Integrals by Applying Compiler Technology on GPU". Journal of Chemical Theory and Computation. 15 (10): 5319–5331. doi:10.1021/acs.jctc.9b00560. PMID 31503475. S2CID 202555796.
^ Joshua A. Anderson; Chris D. Lorenz; A. Travesset (2008). "General Purpose Molecular Dynamics Simulations Fully Implemented on Graphics Processing Units". Journal of Computational Physics. 227 (10): 5342–5359. Bibcode:2008JCoPh.227.5342A. CiteSeerX 10.1.1.552.2883. doi:10.1016/j.jcp.2008.01.047.
^ Christopher I. Rodrigues; David J. Hardy; John E. Stone; Klaus Schulten & Wen-Mei W. Hwu. (2008). "GPU acceleration of cutoff pair potentials for molecular modeling applications". In CF'08: Proceedings of the 2008 Conference on Computing Frontiers, New York, NY, USA: 273–282.
^ Peter H. Colberg; Felix Höfling (2011). "Highly accelerated simulations of glassy dynamics using GPUs: Caveats on limited floating-point precision". Comput. Phys. Commun. 182 (5): 1120–1129. arXiv:0912.3824. Bibcode:2011CoPhC.182.1120C. doi:10.1016/j.cpc.2011.01.009. S2CID 7173093.
^ Yousif RH (2020). "Exploring the Molecular Interactions between Neoculin and the Human Sweet Taste Receptors through Computational Approaches" (PDF). Sains Malaysiana. 49 (3): 517–525. doi:10.17576/jsm-2020-4903-06.
^ Bailey N, Ingebrigtsen T, Hansen JS, Veldhorst A, Bøhling L, Lemarchand C, et al. (2017-12-14). "RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles". SciPost Physics. 3 (6): 038. arXiv:1506.05094. Bibcode:2017ScPP....3...38B. doi:10.21468/SciPostPhys.3.6.038. ISSN 2542-4653. S2CID 43964588.
^ Harger M, Li D, Wang Z, Dalby K, Lagardère L, Piquemal JP, et al. (September 2017). "Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs". Journal of Computational Chemistry. 38 (23): 2047–2055. doi:10.1002/jcc.24853. PMC 5539969. PMID 28600826.

External links

More links for classical and quantum сhemistry on GPUs

Processor technologies

Models

Architecture

Instruction set
architectures

Types	Orthogonal instruction set CISC RISC Application-specific EDGE TRIPS VLIW EPIC MISC OISC NISC ZISC VISC architecture Quantum computing Comparison Addressing modes
Instruction sets	Motorola 68000 series VAX PDP-11 x86 ARM Stanford MIPS MIPS MIPS-X Power POWER PowerPC Power ISA Clipper architecture SPARC SuperH DEC Alpha ETRAX CRIS M32R Unicore Itanium OpenRISC RISC-V MicroBlaze LMC System/3x0 S/360 S/370 S/390 z/Architecture Tilera ISA VISC architecture Epiphany architecture Others

Execution

Instruction pipelining	Pipeline stall Operand forwarding Classic RISC pipeline
Hazards	Data dependency Structural Control False sharing
Out-of-order	Scoreboarding Tomasulo's algorithm Reservation station Re-order buffer Register renaming Wide-issue
Speculative	Branch prediction Memory dependence prediction

Parallelism

Level	Bit Bit-serial Word Instruction Pipelining Scalar Superscalar Task Thread Process Data Vector Memory Distributed
Multithreading	Temporal Simultaneous Hyperthreading Simultaneous and heterogenous Speculative Preemptive Cooperative
Flynn's taxonomy	SISD SIMD Array processing (SIMT) Pipelined processing Associative processing SWAR MISD MIMD SPMD

Processor
performance

Transistor count
Instructions per cycle (IPC)
- Cycles per instruction (CPI)
Instructions per second (IPS)
Floating-point operations per second (FLOPS)
Transactions per second (TPS)
Synaptic updates per second (SUPS)
Performance per watt (PPW)
Cache performance metrics
Computer performance by orders of magnitude

Types

By application	Embedded system Microprocessor Microcontroller Mobile Ultra-low-voltage ASIP Soft microprocessor
Systems on chip	System on a chip (SoC) Multiprocessor (MPSoC) Cypress PSoC Network on a chip (NoC)
Hardware accelerators	Coprocessor AI accelerator Graphics processing unit (GPU) Image processor Vision processing unit (VPU) Physics processing unit (PPU) Digital signal processor (DSP) Tensor Processing Unit (TPU) Secure cryptoprocessor Network processor Baseband processor

Word size

Core count

Components

Functional units	Arithmetic logic unit (ALU) Address generation unit (AGU) Floating-point unit (FPU) Memory management unit (MMU) Load–store unit Translation lookaside buffer (TLB) Branch predictor Branch target predictor Integrated memory controller (IMC) Memory management unit Instruction decoder
Logic	Combinational Sequential Glue Logic gate Quantum Array
Registers	Processor register Status register Stack register Register file Memory buffer Memory address register Program counter
Control unit	Hardwired control unit Instruction unit Data buffer Write buffer Microcode ROM Counter
Datapath	Multiplexer Demultiplexer Adder Multiplier CPU Binary decoder Address decoder Sum-addressed decoder Barrel shifter
Circuitry	Integrated circuit 3D Mixed-signal Power management Boolean Digital Analog Quantum Switch

Power
management

Categories:

Hidden categories:

GPU accelerated molecular modelling software

Programs

API

Distributed computing projects

See also

References

External links