high performance computing on graphics processing units: hgpu.org

Applications

Hardware accelerators for biocomputing: A survey

Souradip Sarkar, Turbo Majumder, Ananth Kalyanaraman, Partha Pratim Pande

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Tags: Biology, Cell processor, CUDA, FPGA, Molecular dynamics, nVidia, nVidia GeForce 7800 GTX, Performance, Review, Sequence alignment

August 7, 2011 by hgpu

Accelerated simulation of spiking neural networks using GPUs

Andreas K. Fidjeland, Murray P. Shanahan

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Tags: Biology, CUDA, Neural networks, nVidia, Tesla C1060

August 6, 2011 by hgpu

Unifying stream based and reconfigurable computing to design application accelerators

Bruno Francisco, Frederico Pratas, Leonel Sousa

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Tags: Bioinformatics, Biology, Computer science, Data parallelism, FPGA, nVidia, nVidia GeForce GTX 285

August 2, 2011 by hgpu

Near-real-time simulations of biolelectric activity in small mammalian hearts using graphical processing units

E.J. Vigmond, P.M. Boyle, L.J. Leon, G. Plank

Tags: Biology, Heart, Medicine, ODEs, Ordinary differential equations, Partial differential equations, PDEs

July 27, 2011 by hgpu

High-Throughput Sequence Translation Using CUDA

Sun Wei-dong, Ma Zong-min

Tags: Bioinformatics, Biology, CUDA, nVidia, Sequence matching

July 25, 2011 by hgpu

A Coarse Grain Reconfigurable Architecture for sequence alignment problems in bio-informatics

Pei Liu, A. Hemani

Tags: Algorithms, Bioinformatics, Biology, FPGA, Sequence alignment, Smith-Waterman algorithm

July 25, 2011 by hgpu

On the Robust Mapping of Dynamic Programming onto a Graphics Processing Unit

Shucai Xiao, A.M. Aji, Wu-chun Feng

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Tags: Algorithms, Bioinformatics, Biology, Cell processor, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 280, Sequence alignment, Smith-Waterman algorithm

July 23, 2011 by hgpu

Parallelizing the Cellular Potts Model on graphics processing units

Jose J. Tapia, Roshan M. D'Souza

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Tags: Biology, Computational biology, CUDA, nVidia, Tesla C1060

July 22, 2011 by hgpu

Accelerating Partitional Algorithms for Flow Cytometry on GPUs

Jeremy Espenshade, Andrew Pangborn, Gregor von Laszewski, Douglas Roberts, James S. Cavenaugh

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Tags: Algorithms, Biology, Clustering, CUDA, nVidia, Tesla C870

July 18, 2011 by hgpu

Accelerating the Nussinov RNA folding algorithm with CUDA/GPU

Dar-Jen Chang, C. Kimmer, Ming Ouyang

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Tags: Biology, CUDA, Macromolecule, nVidia, Tesla C1060, Tesla C2050, Tesla C870

July 12, 2011 by hgpu

Harnessing the power of idle GPUs for acceleration of biological sequence alignment

Fumihiko Ino, Yuki Kotani, Yuma Munekawa, Kenichi Hagihara

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce 7900 GTX, nVidia GeForce 7950 GX2, nVidia GeForce 8800 GTX, OpenGL, Sequence alignment

July 12, 2011 by hgpu

Graphics processing unit parallel accelerated solution of the discrete ordinates for photon transport in biological tissues

Kuan Peng, Xinbo Gao, Xiaochao Qu, Nunu Ren, Xueli Chen, Xiaowei He, Xiaorei Wang, Jimin Liang, Jie Tian

Tags: Biology, CUDA, Image reconstruction, nVidia, nVidia GeForce GTX 260, Optics, Tomography

July 12, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Hardware accelerators for biocomputing: A survey

Accelerated simulation of spiking neural networks using GPUs

Unifying stream based and reconfigurable computing to design application accelerators

Near-real-time simulations of biolelectric activity in small mammalian hearts using graphical processing units

High-Throughput Sequence Translation Using CUDA

A Coarse Grain Reconfigurable Architecture for sequence alignment problems in bio-informatics

On the Robust Mapping of Dynamic Programming onto a Graphics Processing Unit

Parallelizing the Cellular Potts Model on graphics processing units

Accelerating Partitional Algorithms for Flow Cytometry on GPUs

Accelerating the Nussinov RNA folding algorithm with CUDA/GPU

Harnessing the power of idle GPUs for acceleration of biological sequence alignment

Graphics processing unit parallel accelerated solution of the discrete ordinates for photon transport in biological tissues

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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