high performance computing on graphics processing units: hgpu.org

Applications

Enhanced molecular dynamics performance with a programmable graphics processor

D. C. Rapaport

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Tags: Chemistry, Computational chemistry, Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia Quadro FX 770 M, Physics

November 13, 2010 by hgpu

Sop-GPU: Accelerating biomolecular simulations in the centisecond timescale using graphics processors

A. Zhmurov, R. I. Dima, Y. Kholodov, V. Barsegov

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Tags: Bioinformatics, Biomolecular assemblies, Chemistry, CUDA, Dynamic force spectroscopy, Langevin dynamics, Molecular dynamics, nVidia, nVidia GeForce GTX 295

November 9, 2010 by hgpu

A molecular docking system using CUDA

Youngtae Roh, Jun Lee, Sungjun Park, Jee I. Kim

Tags: Bioinformatics, Biology, Biophysics, Chemistry, Molecular dynamics

November 9, 2010 by hgpu

SIML: A Fast SIMD Algorithm for Calculating LINGO Chemical Similarities on GPUs and CPUs

Imran S. Haque, Vijay S. Pande, W. Patrick Walters

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Source codes

Tags: Chemistry, CUDA, nVidia, nVidia GeForce GTS 250, OpenMP, Package, Tesla T10

November 9, 2010 by hgpu

Theory of square, rectangular, and microband electrodes through explicit GPU simulation

Ian Cutress, Richard Compton

Tags: Chemistry, Electrochemistry, Finite difference

November 8, 2010 by hgpu

Large-scale mixer simulations using massively parallel GPU architectures

Charles A. Radeke, Benjamin J. Glasser, Johannes G. Khinast

Tags: Chemistry, CUDA, nVidia, Pharmaceuticals, Powder processing

November 8, 2010 by hgpu

Biomolecular electrostatics simulation with a parallel FMM-based BEM, using up to 512 GPUs

Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, L. A. Barba, Tsuyoshi Hamada

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Tags: Chemistry, CUDA, GPU cluster, Molecular modeling, nVidia, nVidia GeForce GTX 295, Package

November 7, 2010 by hgpu

Efficient nonbonded interactions for molecular dynamics on a graphics processing unit

P. Eastman, V. S. Pande

Tags: Chemistry, Computational chemistry, Molecular dynamics, Solvent effects

November 7, 2010 by hgpu

MDLab: A molecular dynamics simulation prototyping environment

Trevor Cickovski, Santanu Chatterjee, Jacob Wenger, Christopher R. Sweet, Jesus A. Izaguirre

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Tags: Chemistry, Computational chemistry, CUDA, Molecular dynamics, nVidia, Package, Python

November 6, 2010 by hgpu

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

I. Buch, M. J. Harvey, T. Giorgino, D. P. Anderson, G. De Fabritiis

Tags: Biochemistry, Chemistry, Molecular dynamics, Thermodynamics

November 6, 2010 by hgpu

Iterative induced dipoles computation for molecular mechanics on GPUs

Frederico Pratas,Ricardo A. Mata,Leonel Sousa

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Molecular dynamics, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285

November 5, 2010 by hgpu

Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1-39)

Vincent A. Voelz, Gregory R. Bowman, Kyle Beauchamp, Vijay S. Pande

Tags: Bioinformatics, Biology, Chemistry, Molecular dynamics

November 4, 2010 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

IterApp: Iterative Application Skeleton

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Enhanced molecular dynamics performance with a programmable graphics processor

Sop-GPU: Accelerating biomolecular simulations in the centisecond timescale using graphics processors

A molecular docking system using CUDA

SIML: A Fast SIMD Algorithm for Calculating LINGO Chemical Similarities on GPUs and CPUs

Theory of square, rectangular, and microband electrodes through explicit GPU simulation

Large-scale mixer simulations using massively parallel GPU architectures

Biomolecular electrostatics simulation with a parallel FMM-based BEM, using up to 512 GPUs

Efficient nonbonded interactions for molecular dynamics on a graphics processing unit

MDLab: A molecular dynamics simulation prototyping environment

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

Iterative induced dipoles computation for molecular mechanics on GPUs

Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1-39)

Recent source codes

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

IterApp: Iterative Application Skeleton

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

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