high performance computing on graphics processing units: hgpu.org

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Processing Big Data in Main Memory and on GPU

Meisam Fathi Salmi

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Tags: big data, Computer science, CUDA, Hadoop, Java, OpenCL, Scala, Spark, Thesis

June 14, 2016 by hgpu

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Jan Masek, Radim Burget, Lukas Povoda, Malay Kishore Dutta

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Tags: Algorithms, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 690, OpenCL

June 14, 2016 by hgpu

Decoupled Vector-Fetch Architecture with a Scalarizing Compiler

Yunsup Lee

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Tags: Computer science, CUDA, Electronic design automation, Heterogeneous systems, LLVM, OpenCL, Performance, PTX, SoC, Thesis

June 9, 2016 by hgpu

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Yusuke Nagasaka, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

June 9, 2016 by hgpu

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Naila Farooqui

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Tags: ATI, ATI Radeon HD 7770, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, PTX, Tesla C2050, Tesla M2075, Thesis

June 9, 2016 by hgpu

Massively-Parallel Lossless Data Decompression

Evangelia Sitaridi, Rene Mueller, Tim Kaldewey, Guy Lohman, Kenneth Ross

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, Tesla K40

June 7, 2016 by hgpu

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Jeffrey Kelling, Geza Odor, Sibylle Gemming

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Tags: Computational Physics, Computer science, CUDA, Materials Science, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce Titan Black, Statistical Mechanics, Tesla K80

June 7, 2016 by hgpu

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Bo Yang, Hui Liu, Zhangxin Chen

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Tags: Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla C2050, Tesla C2070

June 7, 2016 by hgpu

MODESTO: Data-centric Analytic Optimization of Complex Stencil Programs on Heterogeneous Architectures

Tobias Gysi, Tobias Grosser, Torsten Hoefler

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Source codes

Tags: Analytical model, CUDA, nVidia, Optimization, Package, Stencil computation, Tesla K20

June 3, 2016 by gysit

Disc: Approximative Nearest Neighbor Search using Ellipsoids for Photon Mapping on GPUs

Marcus Bergholm, Viktor Kronvall

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Tags: Algorithms, Computer science, CUDA, Image generation, Nearest neighbour, nVidia, nVidia GeForce GTX 970, Sorting, Thesis

June 2, 2016 by hgpu

Weighted Residuals for Very Deep Networks

Falong Shen, Gang Zeng

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X

June 2, 2016 by hgpu

Computer Vision on the GPU — Tools, Algorithms and Frameworks

Hannes Fassold

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Tags: Computer vision, CUDA, GPU, KLT, Optical flow, SIFT

May 31, 2016 by HannesF99

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

Processing Big Data in Main Memory and on GPU

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Decoupled Vector-Fetch Architecture with a Scalarizing Compiler

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Massively-Parallel Lossless Data Decompression

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

MODESTO: Data-centric Analytic Optimization of Complex Stencil Programs on Heterogeneous Architectures

Disc: Approximative Nearest Neighbor Search using Ellipsoids for Photon Mapping on GPUs

Weighted Residuals for Very Deep Networks

Computer Vision on the GPU — Tools, Algorithms and Frameworks

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)