high performance computing on graphics processing units: hgpu.org

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Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors

Nachiket Kapre, Andre DeHon

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Tags: Cell processor, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 9600 GT, Performance

December 26, 2010 by hgpu

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

Frederico Pratas, Pedro Trancoso, Alexandros Stama

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Tags: Cell processor, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Performance, Programming techniques

December 24, 2010 by hgpu

Pushing the Envelope: Extreme Network Coding on the GPU

Hassan Shojania, Baochun Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques

December 22, 2010 by hgpu

Demystifying GPU microarchitecture through microbenchmarking

Henry Wong, Misel-Myrto Papadopoulou, Maryam Sadooghi-Alvandi, Andreas Moshovos

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Tags: Architecture, Computer science, nVidia, nVidia GeForce GTX 280, Performance

December 21, 2010 by hgpu

Shader Performance Analysis on a Modern GPU Architecture

Victor Moya, Carlos Gonzalez, Jordi Roca, Agustin Fernandez, Roger Espasa

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Tags: 3D Graphics and Realism, ATI, Computer science, OpenGL, Performance, Rendering

December 18, 2010 by hgpu

Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

Kaushik Datta, Mark Murphy, Vasily Volkov, Samuel Williams, Jonathan Carter, Leonid Oliker, David Patterson, John Shalf, Katherine Yelick

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Tags: Cell processor, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 280, Performance

December 14, 2010 by hgpu

Accelerating Compute-Intensive Applications with GPUs and FPGAs

Shuai Che, Jie Li, Jeremy W. Sheaffer, Kevin Skadron, John Lach

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Source codes

Tags: Architecture, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Package, Performance

December 13, 2010 by hgpu

Fast scan algorithms on graphics processors

Yuri Dotsenko, Naga K. Govindaraju, Peter P. Sloan, Charles Boyd, John Manferdelli

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Tags: Algorithms, Computer science, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX, Performance

December 11, 2010 by hgpu

Linear genetic programming GPGPU on Microsoft’s Xbox 360

G. Wilson, W. Banzhaf

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Tags: Computer science, Performance, Xbox 360

December 11, 2010 by hgpu

Towards acceleration of fault simulation using graphics processing units

Kanupriya Gulati, Sunil P. Khatri

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Performance

December 11, 2010 by hgpu

Value Prediction and Speculative Execution on GPU

Shaoshan Liu, Christine Eisenbeis, Jean-Luc Gaudiot

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Performance

December 11, 2010 by hgpu

BLAS Comparison on FPGA, CPU and GPU

Srinidhi Kestur, John D. Davis, Oliver Williams

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Tags: BLAS, Computer science, CUBLAS, CUDA, Energy-efficient computing, FPGA, Linear Algebra, nVidia, nVidia GeForce 9500 GT, Performance, Tesla C1060

December 6, 2010 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

Pushing the Envelope: Extreme Network Coding on the GPU

Demystifying GPU microarchitecture through microbenchmarking

Shader Performance Analysis on a Modern GPU Architecture

Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

Accelerating Compute-Intensive Applications with GPUs and FPGAs

Fast scan algorithms on graphics processors

Linear genetic programming GPGPU on Microsoft’s Xbox 360

Towards acceleration of fault simulation using graphics processing units

Value Prediction and Speculative Execution on GPU

BLAS Comparison on FPGA, CPU and GPU

Recent source codes

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

ROCm's implementation of Gromacs

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

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