high performance computing on graphics processing units: hgpu.org

Applications

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An open framework for rapid prototyping of signal processing applications

Maxime Pelcat, Jonathan Piat, Matthieu Wipliez, Slaheddine Aridhi, and Jean-Francois Nezan

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Tags: Algorithms, Graph theory, Signal processing

August 24, 2011 by hgpu

SCF: a device- and language-independent task coordination framework for reconfigurable, heterogeneous systems

V. Aggarwal, R. Garcia, G. Stitt, A. George, H. Lam

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Tags: Computer science, FPGA, Heterogeneous systems, Performance, Software Engineering, Task scheduling

August 24, 2011 by hgpu

Precise dynamic analysis for slack elasticity: adding buffering without adding bugs

Sarvani Vakkalanka, Anh Vo, Ganesh Gopalakrishnan, Robert M. Kirby

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Tags: Algorithms, Computer science, MPI, Network communications

August 24, 2011 by hgpu

Elastic pipeline: addressing GPU on-chip shared memory bank conflicts

Chunyang Gou, Georgi N. Gaydadjiev

Tags: Computer science, Memory, Memory model, Performance

August 24, 2011 by hgpu

JCudaMP: OpenMP/Java on CUDA

Georg Dotzler, Ronald Veldema, Michael Klemm

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Tags: Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce GTX 280, OpenMP, Programming Languages, Programming techniques

August 23, 2011 by hgpu

Implementing the PGI Accelerator model

Michael Wolfe

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization, Programming Languages, Tesla C1060, Tesla S1070

August 23, 2011 by hgpu

MDR: performance model driven runtime for heterogeneous parallel platforms

Jacques A. Pienaar, Anand Raghunathan, Srimat Chakradhar

Tags: Computer science, Heterogeneous systems, Performance, Programming techniques, Task scheduling

August 23, 2011 by hgpu

Bounding the effect of partition camping in GPU kernels

Ashwin M. Aji, Mayank Daga, Wu-chun Feng

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 280, Performance

August 23, 2011 by hgpu

Ypnos: declarative, parallel structured grid programming

Dominic A. Orchard, Max Bolingbroke, Alan Mycroft

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Tags: Computer science, High-level Languages, Programming Languages

August 23, 2011 by hgpu

Fast parallel surface and solid voxelization on GPUs

Michael Schwarz, Hans-Peter Seidel

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480

August 23, 2011 by hgpu

High performance content-based matching using GPUs

Alessandro Margara, Gianpaolo Cugola

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Source codes

Tags: Algorithms, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 460, Package, Performance, Programming techniques

August 23, 2011 by hgpu

Workload and network-optimized computing systems

D. P. LaPotin, S. Daijavad, C. L. Johnson, S. W. Hunter, K. Ishizaki, H. Franke, H. D. Achilles, D. P. Dumarot, N. A. Greco, B. Davari

Tags: Computer science, Performance, Programming techniques

August 23, 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

Applications

An open framework for rapid prototyping of signal processing applications

SCF: a device- and language-independent task coordination framework for reconfigurable, heterogeneous systems

Precise dynamic analysis for slack elasticity: adding buffering without adding bugs

Elastic pipeline: addressing GPU on-chip shared memory bank conflicts

JCudaMP: OpenMP/Java on CUDA

Implementing the PGI Accelerator model

MDR: performance model driven runtime for heterogeneous parallel platforms

Bounding the effect of partition camping in GPU kernels

Ypnos: declarative, parallel structured grid programming

Fast parallel surface and solid voxelization on GPUs

High performance content-based matching using GPUs

Workload and network-optimized computing systems

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)