high performance computing on graphics processing units: hgpu.org

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Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K10, Tesla K20

October 29, 2014 by hgpu

Parallel Finite Volume Algorithm on Graphic Processing Units (GPU)

Amirsaman Farrokhpanah, Hanif Montazeri, Javad Mostaghimi

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, Tesla K20

October 27, 2014 by hgpu

Performance Engineering of the Kernel Polynomial Method on Large-Scale CPU-GPU Systems

Moritz Kreutzer, Georg Hager, Gerhard Wellein, Andreas Pieper, Andreas Alvermann, Holger Fehske

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Tags: Computational Engineering, CUDA, Finance, Mesoscale and Nanoscale Physics, nVidia, Tesla K20

October 22, 2014 by hgpu

A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

Kato Mivule, Benjamin Harvey, Crystal Cobb, Hoda El Sayed

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Tags: Computer science, CUDA, MapReduce, nVidia, Overview, Pthreads, Review, Tesla K20

October 18, 2014 by hgpu

StreamWorks: An Energy-efficient Embedded Co-processor for Stream Computing

Amrit Panda

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Tags: Computer science, CUDA, nVidia, Tesla K20, Thesis, Virtualization

October 18, 2014 by hgpu

Cholla : A New Massively-Parallel Hydrodynamics Code For Astrophysical Simulation

Evan E. Schneider, Brant E. Robertson

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Tags: Astrophysics, Computational Physics, CUDA, Fluid dynamics, Instrumentation and Methods for Astrophysics, nVidia, Physics, Tesla K20

October 18, 2014 by hgpu

Multi-GPU Based Lattice Boltzmann Method for Hemodynamic Simulation in Patient-Specific Cerebral Aneurysm

Changsheng Huang, Baochang Shi, Zhaoli Guo, Zhenhua Chai

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Tags: Algorithms, CUDA, Fluid dynamics, Lattice Boltzmann model, MPI, nVidia, Package, Tesla C1060, Tesla K20

October 16, 2014 by hgpu

Pipelined Iterative Solvers with Kernel Fusion for Graphics Processing Units

Karl Rupp, Josef Weinbub, Ansgar Jungel, Tibor Grasser

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Source codes

Tags: AMD FirePro W9000, AMD FirePro W9100, ATI, Computer science, Conjugate gradient solver, CUDA, Linear Algebra, nVidia, OpenCL, Tesla C2050, Tesla K20

October 16, 2014 by hgpu

A Complete and Efficient CUDA-Sharing Solution for HPC Clusters

Antonio J. Pena, Carlos Reano, Federico Silla, Rafael Mayo, Enrique S. Quintana-Orti, Jose Duato

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Tags: Computer science, CUDA, GPU cluster, nVidia, Tesla K20, Virtualization

October 14, 2014 by hgpu

Performance Improvement of Multichannel Audio by Graphics Processing Units

Jose Antonio Belloch Rodriguez

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Tags: Acoustics, Algorithms, Computer science, CUDA, Databases, Filtering, nVidia, nVidia GeForce GTS 360 M, nVidia GeForce GTX 590, nVidia GeForce GTX 690, nVidia Quadro FX 5800, OpenCL, Rendering, Signal processing, Tesla C2070, Tesla K20, Thesis

October 11, 2014 by hgpu

FDTD on Distributed Heterogeneous Multi-GPU Systems

Eirik Myklebost

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Tags: Algorithms, Benchmarking, Cluster computing, CUDA, Electrodynamics, FDTD, Finite-difference time-domain, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, nVidia GeForce GTX 760, Physics, Tesla K20, Thesis, Ultrasound

October 10, 2014 by hgpu

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

Ahmad Abdelfattah, David Keyes, Hatem Ltaief

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Mathematical Software, nVidia, Package, Tesla K20

October 8, 2014 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

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Parallel Finite Volume Algorithm on Graphic Processing Units (GPU)

Performance Engineering of the Kernel Polynomial Method on Large-Scale CPU-GPU Systems

A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

StreamWorks: An Energy-efficient Embedded Co-processor for Stream Computing

Cholla : A New Massively-Parallel Hydrodynamics Code For Astrophysical Simulation

Multi-GPU Based Lattice Boltzmann Method for Hemodynamic Simulation in Patient-Specific Cerebral Aneurysm

Pipelined Iterative Solvers with Kernel Fusion for Graphics Processing Units

A Complete and Efficient CUDA-Sharing Solution for HPC Clusters

Performance Improvement of Multichannel Audio by Graphics Processing Units

FDTD on Distributed Heterogeneous Multi-GPU Systems

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

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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