high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Biology

A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors

Jayram Moorkanikara M. Nageswaran, Nikil Dutt, Jeffrey L. Krichmar, Alex Nicolau, Alexander V. Veidenbaum

Tags: Biology, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, STDP

November 13, 2010 by hgpu

Fast recursive filters for simulating nonlinear dynamic systems

J. H. van Hateren

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Tags: Algorithms, Biology, Neurons and Cognition

November 13, 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

ABC-SysBio–approximate Bayesian computation in Python with GPU support

Juliane Liepe, Chris Barnes, Erika Cule, Kamil Erguler, Paul Kirk, Tina Toni, Michael P. H. Stumpf

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Tags: Bioinformatics, Biology, CUDA, nVidia, Package, Python

November 9, 2010 by hgpu

Monte Carlo randomization tests for large-scale abundance datasets on the GPU

John L. Van Hemert, Julie A. Dickerson

Tags: Bioinformatics, Biology, CUDA, Medicine, Metabolomics, Monte Carlo simulation, Next-Generation sequencing, nVidia

November 8, 2010 by hgpu

Parallel, distributed and GPU computing technologies in single-particle electron microscopy

M. Schmeisser, B. C. Heisen, M. Luettich, B. Busche, F. Hauer, T. Koske, K. H. Knauber, H. Stark

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Tags: Biology, Cg, Cluster computing, Electron microscopy, GLSL, MPI, nVidia, OpenGL, Physics

November 8, 2010 by hgpu

GPU-based Real-Time Soft Tissue Deformation with Cutting and Haptic Feedback

Hadrien Courtecuisse, Hoeryong Jung, Jeremie Allard, Christian Duriez, Doo Y. Lee, Stephane Cotin

Tags: Biology, Biomechanics, Finite element method, Medicine, Surgical simulation

November 8, 2010 by hgpu

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

Casey S. Greene, Nicholas A. Sinnott-Armstrong, Daniel S. Himmelstein, Paul J. Park, Jason H. Moore, Brent T. Harris

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 295, Package

November 7, 2010 by hgpu

Performance Analysis of General-Purpose Computation on Commodity Graphics Hardware: A Case Study Using Bioinformatics

Weiguo Liu, Bertil Schmidt, Wolfgang Muller-Wittig

Tags: Biology, Computer science, nVidia, Sequence alignment, Signal processing

November 7, 2010 by hgpu

Accelerating epistasis analysis in human genetics with consumer graphics hardware

Nicholas S. Armstrong, Casey Greene, Fabio Cancare, Jason Moore

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 280

November 7, 2010 by hgpu

CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions

Yongchao Liu, Bertil Schmidt, Douglas Maskell

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, Package, Smith-Waterman algorithm

November 6, 2010 by hgpu

Automatic fitting of spiking neuron models to electrophysiological recordings

Cyrille Rossant, Dan F. Goodman, Jonathan Platkiewicz, Romain Brette

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Tags: Adaptive threshold, Biology, CUDA, Distributed computing, Neuroscience, nVidia, nVidia GeForce GTX 295, Optimization, Package

November 6, 2010 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

KernelBench: Can LLMs Write GPU Kernels?

KernelBench: Can LLMs Write Efficient GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Seamless acceleration of Fortran intrinsics via AMD AI engines

Forecasting time series with constraints

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors

Fast recursive filters for simulating nonlinear dynamic systems

A molecular docking system using CUDA

ABC-SysBio–approximate Bayesian computation in Python with GPU support

Monte Carlo randomization tests for large-scale abundance datasets on the GPU

Parallel, distributed and GPU computing technologies in single-particle electron microscopy

GPU-based Real-Time Soft Tissue Deformation with Cutting and Haptic Feedback

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

Performance Analysis of General-Purpose Computation on Commodity Graphics Hardware: A Case Study Using Bioinformatics

Accelerating epistasis analysis in human genetics with consumer graphics hardware

CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions

Automatic fitting of spiking neuron models to electrophysiological recordings

Recent source codes

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

KernelBench: Can LLMs Write GPU Kernels?

An MLIR-based toolchain for AMD AI Engine-enabled devices

Forecasting time series with constraints

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