high performance computing on graphics processing units: hgpu.org

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Bothnia: a dual-personality extension to the Intel integrated graphics driver

Gautham N. Chinya, Jamison D. Collins, Perry H. Wang, Hong Jiang, Guei Y. Lueh, Thomas A. Piazza, Hong Wang

Tags: Computer science, Intel, Intel GMA X3000, Programming techniques

February 28, 2011 by hgpu

Automatic data movement and computation mapping for multi-level parallel architectures with explicitly managed memories

Muthu Manikandan Baskaran, Uday Bondhugula, Sriram Krishnamoorthy, J. Ramanujam, Atanas Rountev, P. Sadayappan

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Performance

February 28, 2011 by hgpu

Fast and Memory Efficient GPU-Based Rendering of Tensor Data

Mario Hlawitschka, Sebastian Eichelbaum, Gerik Scheuermann

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Tags: Computer science, OpenGL, Physics, Rendering, Visualization

February 27, 2011 by hgpu

Efficient hierarchical parallel genetic algorithms using grid computing

Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff, Bu-Sung Lee

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Tags: Algorithms, Computer science, Genetic programming, Grid

February 27, 2011 by hgpu

GPU ray casting of virtual globes

Patrick Cozzi, Frank Stoner

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Tags: 3D Graphics and Realism, Computer science, nVidia, nVidia GeForce 8400 GS, Rendering, Visualization

February 27, 2011 by hgpu

Data Buffering Optimization Methods toward a Uniform Programming Interface for GPU-based Applications

Shinichi Yamagiwa, Leonel Sousa, Diogo Antao

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Tags: Computer science, DirectX, nVidia, nVidia GeForce 7400 GO, OpenGL, Programming techniques

February 27, 2011 by hgpu

Accelerating CUDA Graph Algorithms at Maximum Warp

Sungpack Hong, Sang Kyun Kim, Tayo Oguntebi, Kunle Olukotun

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 275, Package, Performance, Programming techniques

February 27, 2011 by hgpu

A Domain-Specific Approach To Heterogeneous Parallelism

Hassan Chafi, Arvind K. Sujeeth, Kevin J. Brown, HyoukJoong Lee, Anand R. Atreya, Kunle Olukotun

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, nVidia GeForce GTX 275

February 27, 2011 by hgpu

StarPU: a Runtime System for Scheduling Tasks over Accelerator-Based Multicore Machines

Cedric Augonnet, Samuel Thibault, Raymond Namyst

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Tags: Computer science, CUDA, Distributed computing, nVidia, nVidia Quadro FX 4600, nVidia Quadro FX 5800, Package, Performance

February 26, 2011 by hgpu

A Tuning Framework for Software-Managed Memory Hierarchies

Manman Ren, Ji Young Park, Mike Houston, Alex Aiken, William J. Dally

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Tags: Algorithms, Cell processor, Compilers, Computer science, Optimization, Performance, Playstation, Programming Languages

February 26, 2011 by hgpu

Real-Time Approaches to Computer Vision

Ron Tal

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Tags: Algorithms, Computer science, Computer vision, CUDA, FPGA, nVidia

February 26, 2011 by hgpu

Believe it or Not! Multi-core CPUs Can Match GPU Performance for FLOP-intensive Application!

Rajesh Bordawekar, Uday Bondhugula, Ravi Rao

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Performance, Programming techniques

February 26, 2011 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

Bothnia: a dual-personality extension to the Intel integrated graphics driver

Automatic data movement and computation mapping for multi-level parallel architectures with explicitly managed memories

Fast and Memory Efficient GPU-Based Rendering of Tensor Data

Efficient hierarchical parallel genetic algorithms using grid computing

GPU ray casting of virtual globes

Data Buffering Optimization Methods toward a Uniform Programming Interface for GPU-based Applications

Accelerating CUDA Graph Algorithms at Maximum Warp

A Domain-Specific Approach To Heterogeneous Parallelism

StarPU: a Runtime System for Scheduling Tasks over Accelerator-Based Multicore Machines

A Tuning Framework for Software-Managed Memory Hierarchies

Real-Time Approaches to Computer Vision

Believe it or Not! Multi-core CPUs Can Match GPU Performance for FLOP-intensive Application!

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)