high performance computing on graphics processing units: hgpu.org

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MARC: A Many-Core Approach to Reconfigurable Computing

Ilia Lebedev, Shaoyi Cheng, Austin Doupnik, James Martin, Christopher Fletcher, Daniel Burke, Mingjie Lin, John Wawrzynek

Tags: Compilers, Computer science, FPGA, OpenCL, Performance

April 2, 2011 by hgpu

OpenCL-based design methodology for application-specific processors

Pekka O. Jaaskelainen, Carlos S. de La Lama, Pablo Huerta, Jarmo H. Takala

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Tags: AES, Computer science, FPGA, MPI, OpenCL, Security

March 30, 2011 by hgpu

Bridging the GPGPU-FPGA efficiency gap

Christopher W. Fletcher, Ilia A. Lebedev, Narges B. Asadi, Daniel R. Burke, John Wawrzynek

Tags: Algorithms, Bayesian, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL

March 22, 2011 by hgpu

Performance comparison of FPGA, GPU and CPU in image processing

Shuichi Asano, Tsutomu Maruyama, Yoshiki Yamaguchi

Tags: Filtering, FPGA, Image processing, Performance

March 21, 2011 by hgpu

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

David Barrie Thomas, Lee Howes, Wayne Luk

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Pseudo-random number generators

March 20, 2011 by hgpu

Mersenne Twister Random Number Generation on FPGA, CPU and GPU

Xiang Tian, Khaled Benkrid

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Pseudo-random number generators

March 20, 2011 by hgpu

FPGA vs. GPU for sparse matrix vector multiply

Yan Zhang, Yasser H. Shalabi, Rishabh Jain, Krishna K. Nagar, Jason D. Bakos

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Tags: Computer science, CUDA, FPGA, Linear Algebra, nVidia, nVidia GeForce GTX 260, Sparse matrix

March 18, 2011 by hgpu

Rotationally invariant sparse patch matching on GPU and FPGA

Zachary K Baker, Reid Porter

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Tags: FPGA, Image processing, Nearest neighbour, nVidia, nVidia GeForce 7900 GTX, OpenGL

March 16, 2011 by hgpu

Hardware Acceleration of EDA Algorithms: Custom ICs, FPGAs and GPUs

Kanupriya Gulati and Sunil P. Khatri

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Tags: Book, CAD, Computer science, CUDA, Electronic design automation, FPGA, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 280, nVidia Quadro FX 5800, Review

March 14, 2011 by hgpu

Comparison of FPGA and GPU implementations of real-time stereo vision

R. Kalarot, J. Morris

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Tags: Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia GeForce GTX 280

March 9, 2011 by hgpu

Application-guided tool development for architecturally diverse computation

Roger D. Chamberlain, Jeremy Buhler, Mark A. Franklin, James H. Buckley

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Tags: Algorithms, Astrophysics, Biology, Computer science, CUDA, Finance, FPGA, Heterogeneous systems, Monte Carlo simulation, nVidia, nVidia GeForce GTX 260, Search, Security

March 7, 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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

MARC: A Many-Core Approach to Reconfigurable Computing

OpenCL-based design methodology for application-specific processors

Bridging the GPGPU-FPGA efficiency gap

Performance comparison of FPGA, GPU and CPU in image processing

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

Mersenne Twister Random Number Generation on FPGA, CPU and GPU

FPGA vs. GPU for sparse matrix vector multiply

Rotationally invariant sparse patch matching on GPU and FPGA

Hardware Acceleration of EDA Algorithms: Custom ICs, FPGAs and GPUs

Comparison of FPGA and GPU implementations of real-time stereo vision

Application-guided tool development for architecturally diverse computation

Real-Time Approaches to Computer Vision

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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