high performance computing on graphics processing units: hgpu.org

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Efficient Parallel CKY Parsing on GPUs

Youngmin Yi, Chao-Yue Lai, Slav Petrov, Kurt Keutzer

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Text mining

January 13, 2012 by hgpu

GPU Implementation of a Multiobjective Search Algorithm

Steffen Limmer, Dietmar Fey, Johannes Jahn

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Search, Tesla C2050

January 13, 2012 by hgpu

Scientific Computing with Python on GPUs

Stefan Reiterer

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Tags: Algorithms, Computer science, CUDA, nVidia, Python, Tutorial

January 13, 2012 by hgpu

Mesh deformations in X3D via CUDA with freeform deformation lattices

Yvonne Jung, Holger Graf, Johannes Behr, Arjan Kuijper

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Tags: Algorithms, Computer science, CUDA, Interactive application, nVidia, nVidia GeForce GTX 295

January 13, 2012 by hgpu

Enhancing Data Parallelism for Ant Colony Optimisation on GPUs

Jose M. Cecilia, Jose M. Garcia, Andy Nisbet, Martyn Amos, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Metaheuristics, nVidia, Tesla C2050

January 12, 2012 by hgpu

GKLEE: Concolic Verification and Test Generation for GPUs

Guodong Li, Peng Li, Geof Sawaya, Ganesh Gopalakrishnan, Indradeep Ghosh, Sreeranga P. Rajan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Programming techniques

January 12, 2012 by hgpu

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Maxim Naumov

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, Tesla C2050

January 12, 2012 by hgpu

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

Hartwig Anzt, Maribel Castillo, Juan C. Fernandez, Vincent Heuveline, Francisco D. Igual, Rafael Mayo and Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

January 11, 2012 by hgpu

A parallel Genetic Programming algorithm for classification

Alberto Cano, Amelia Zafra, Sebastian Ventura

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 480

January 11, 2012 by hgpu

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Hartwig Anzt, Vincent Heuveline, Jose I. Aliaga, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, Optimization, Sparse linear iterative solvers, Tesla C1060

January 11, 2012 by hgpu

Exponential integrators on graphic processing units

Lukas Einkemmer

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX, Tesla C1060, Thesis

January 11, 2012 by hgpu

Efficient Ray Tracing of Dynamic Scenes on the GPU

Asger Dam Hoedt

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Tags: 3D Graphics and Realism, Computer science, CUDA, KD-tree, nVidia, nVidia GeForce GTX 330 M, Raytracing, Thesis

January 11, 2012 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Efficient Parallel CKY Parsing on GPUs

GPU Implementation of a Multiobjective Search Algorithm

Scientific Computing with Python on GPUs

Mesh deformations in X3D via CUDA with freeform deformation lattices

Enhancing Data Parallelism for Ant Colony Optimisation on GPUs

GKLEE: Concolic Verification and Test Generation for GPUs

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

A parallel Genetic Programming algorithm for classification

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Exponential integrators on graphic processing units

Efficient Ray Tracing of Dynamic Scenes on the GPU

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)