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Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

V.N. Pistulkar, C.A. Uttarwar

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Tags: Computer science, CUDA, GPU cluster, nVidia, PTX, Tesla C1060

July 5, 2012 by hgpu

CUDA-accelerated Hierarchical K-means

Che-Chun Lee, Kuan-Yu Chu

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Tags: Clustering, CUDA, Image processing, nVidia, Tesla C1060

July 1, 2012 by hgpu

A Scheduling Framework for a Heterogeneous Parallel Architecture

Wei Zhang

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 1800, Task scheduling, Tesla C1060, Thesis

June 30, 2012 by hgpu

A Parallel Image Segmentation Algorithm on GPUs

P. N. Happ, R. Q. Feitosa, C. Bentes, R. Farias

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Tags: CUDA, Image processing, nVidia, nVidia GeForce 9600 GT, Tesla C1060

June 29, 2012 by hgpu

Improving GPU Simulations of Spiking Neural P Systems

Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-Del-Amor, Mario J. Perez-Jimenez

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Tags: Computer science, CUDA, Linear Algebra, Membrane computing, Neural networks, nVidia, P systems, Tesla C1060

June 25, 2012 by hgpu

Parakeet: A Just-In-Time Parallel Accelerator for Python

Alex Rubinsteyn, Eric Hielscher, Nathaniel Weinman, Dennis Shasha

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Tags: Computer science, CUDA, nVidia, Python, Tesla C1060

June 14, 2012 by hgpu

Range query processing in a multi-GPU environment

Ricardo J. Barrientos, Jose I. Gomez, Christian Tenllado, Manuel Prieto Matias, Mauricio Marin

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Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Databases, GPU cluster, nVidia, OpenMP, Tesla C1060

June 11, 2012 by hgpu

Autotuning Stencil-Based Computations on GPUs

Azamat Mametjanov, Daniel Lowell, Ching-Chen May, Boyana Norris

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Source codes

Tags: Computer science, CUDA, Differential equations, nVidia, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C1060, Tesla C2070

June 9, 2012 by hgpu

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born

Andreas W. Gotz, Mark J. Williamson, Dong Xu, Duncan Poole, Scott Le Grand, Ross C. Walker

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Tags: Algorithms, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 580, Tesla C1060, Tesla C2070, Tesla M2090

May 26, 2012 by hgpu

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Mai Zheng, Vignesh T. Ravi, Wenjing Ma, Feng Qin, Gagan Agrawal

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Tags: Computer science, CUDA, nVidia, Optimization, Performance, Tesla C1060

May 23, 2012 by hgpu

Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing

Carlos Gonzalez, Sergio Sanchez, Abel Paz, Javier Resano, Daniel Mozos, Antonio Plaza

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Tags: Algorithms, CUDA, FPGA, Image processing, nVidia, Sensing, Tesla C1060

May 19, 2012 by hgpu

Mapping a Data-Flow Programming Model onto Heterogeneous Platforms

Alina Sbirlea, Yi Zou, Zoran Budimlic, Jason Cong, Vivek Sarkar

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Tags: Algorithms, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Programming techniques, Tesla C1060

May 14, 2012 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

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

CUDA-accelerated Hierarchical K-means

A Scheduling Framework for a Heterogeneous Parallel Architecture

A Parallel Image Segmentation Algorithm on GPUs

Improving GPU Simulations of Spiking Neural P Systems

Parakeet: A Just-In-Time Parallel Accelerator for Python

Range query processing in a multi-GPU environment

Autotuning Stencil-Based Computations on GPUs

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing

Mapping a Data-Flow Programming Model onto Heterogeneous Platforms

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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