high performance computing on graphics processing units: hgpu.org

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Fast K-selection Algorithms for Graphics Processing Units

Tolu Alabi, Jeffrey D. Blanchard, Bradley Gordon, Russel Steinbach

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, nVidia, Package, Sorting, Tesla C2070

December 31, 2011 by hgpu

Improving the speed of neural networks on CPUs

Vincent Vanhoucke, Andrew Senior, Mark Z. Mao

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Tags: Computer science, CUDA, Neural networks, nVidia, Speech recognition, Tesla C2070, Tutorial

December 28, 2011 by hgpu

Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Qi Li

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, Optimization, Sorting, Tesla C1060, Tesla C2050, Tesla C2070, Thesis

December 26, 2011 by hgpu

Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation

Moritz Kreutzer, Georg Hager, Gerhard Wellein, Holger Fehske, Achim Basermann, Alan R. Bishop

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Tags: Computer science, GPU cluster, nVidia, Performance, Sparse matrix, Tesla C2050, Tesla C2070

December 26, 2011 by hgpu

Computational Fluid Dynamics Simulations using Many Graphics Processors

Ali Khajeh-Saeed, J. Blair Perot

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Tags: Algorithms, CUDA, Fluid dynamics, GPU cluster, Laplace and Poisson equation, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, Poisson equation, Tesla C2070, Tesla S1070

December 21, 2011 by hgpu

Toward Accelerating the Matrix Inversion Computation of Symmetric Positive-Definite Matrices on Heterogeneous GPU-Based Systems

Huda Ibeid, Dinesh Kaushik, David Keyes, Hatem Ltaief

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Matrix inversion, nVidia, Tesla C2070

December 14, 2011 by hgpu

Design and study of a massively multi threaded shared memory architecture

Christophe Riolo

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Tags: Algorithms, Computer science, CUDA, Memory, nVidia, Tesla C2070, Thesis

December 12, 2011 by hgpu

Parallelization of an Ultrasound Reconstruction Algorithm for non Destructive Testing on Multicore CPU and GPU

Antoine Pedron, Lionel Lacassagne, Franck Bimbard, Stephane Le Berre

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Tags: Algorithms, CUDA, nVidia, Optimization, Physics, Tesla C1060, Tesla C2070, Ultrasound

December 12, 2011 by hgpu

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

Qiang Liu, Wayne Luk

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Performance, Tesla C2070

December 9, 2011 by hgpu

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Deepak Mathews Panickal

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Tags: ATI, ATI Radeon HD 5470, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Tesla C2070, Thesis

December 9, 2011 by hgpu

Efficient Two-Level Preconditionined Conjugate Gradient Method on the GPU

Rohit Gupta, Martin B. van Gijzen, Kees Vuik

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Tags: Algorithms, Conjugate gradient solver, CUDA, Mathematics, nVidia, Tesla C2070

December 7, 2011 by hgpu

Performance engineering for the Lattice Boltzmann method on GPGPUs: Architectural requirements and performance results

Johannes Habich, Christian Feichtinger, Harald Kostler, Georg Hager, Gerhard Wellein

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Tags: Algorithms, ATI, ATI Radeon HD 6970, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Performance, Tesla C1060, Tesla C2070

December 6, 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

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high performance computing on graphics processing units: hgpu.org

Fast K-selection Algorithms for Graphics Processing Units

Improving the speed of neural networks on CPUs

Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation

Computational Fluid Dynamics Simulations using Many Graphics Processors

Toward Accelerating the Matrix Inversion Computation of Symmetric Positive-Definite Matrices on Heterogeneous GPU-Based Systems

Design and study of a massively multi threaded shared memory architecture

Parallelization of an Ultrasound Reconstruction Algorithm for non Destructive Testing on Multicore CPU and GPU

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Efficient Two-Level Preconditionined Conjugate Gradient Method on the GPU

Performance engineering for the Lattice Boltzmann method on GPGPUs: Architectural requirements and performance results

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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