high performance computing on graphics processing units: hgpu.org

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GPU Programming Strategies and Trends in GPU Computing

Andre R. Brodtkorb, Trond R. Hagen, Martin L. Saetra

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Tags: Computer science, Heterogeneous systems, Optimization, Overview

April 2, 2012 by hgpu

Adaptive Input-aware Compilation for Graphics Engines

Mehrzad Samadi, Amir Hormati, Mojtaba Mehrara, Janghaeng Lee, and Scott Mahlke

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Tags: Compiler, CUDA, GPU, nVidia, nVidia GeForce GTX 285, Optimization, Portability, Streaming, Tesla C2050

March 31, 2012 by Moaddeli

A computing origami: Optimized code generation for emerging parallel platforms

Hagiescu Miriste Andrei Mihai

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Tags: Code generation, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Optimization, Tesla S1070, Thesis

March 29, 2012 by hgpu

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

James Bergstra, Nicolas Pinto, David Cox

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Tags: Computer science, CUDA, Filtering, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Tesla C1060, Tesla C2070

March 29, 2012 by hgpu

Auto-tuning a High-Level Language Targeted to GPU Codes

Scott Grauer-Gray, Lifan Xu, Robert Searles, Sudhee Ayalasomayajula, John Cavazos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 280, OpenCL, Optimization, Performance, Programming techniques, Stereo vision, Tesla C2050

March 28, 2012 by hgpu

CUDA 2D Stencil Computations for the Jacobi Method

Jose Maria Cecilia, Jose Manuel Garcia, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Differential equations, Linear Algebra, nVidia, Optimization, Tesla C1060, Tesla C870

March 16, 2012 by hgpu

On the Use of Small 2D Convolutions on GPUs

Shams A.H. Al Umairy, Alexander S. van Amesfoort, Irwan D. Setija, Martijn C. van Beurden, Henk J. Sips

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Tags: Computer science, CUDA, Electrodynamics, FFT, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Optimization, Physics, Tesla C1060

March 15, 2012 by hgpu

Performance analysis and optimization of the OP2 framework on many-core architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly

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Source codes

Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, nVidia, OpenMP, Optimization, Package, Performance, Tesla C2050

March 15, 2012 by hgpu

Analyzing CUDA’s Compiler through the Visualization of Decoded GPU Binaries

Cedric Nugteren, Bart Mesman, Henk Corporaal

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Performance, Visualization

March 13, 2012 by hgpu

A Study of CUDA Acceleration and Impact of Data Transfer Overhead in Heterogeneous Environment

Fahian Ahmed, Saddam Quirem, Bum Joo Shin, Duk Joo Son, Young Choon Woo, Byeong Kil Lee, Wan Choi

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Tags: Algorithms, Computer science, CUDA, Databases, Heterogeneous systems, nVidia, Optimization, Security, Tesla C1060

March 9, 2012 by hgpu

Relational Algorithms for Multi-Bulk-Synchronous Processors

Gregory Frederick Diamos, Haicheng Wu, Ashwin Lele, Jin Wang, Sudhakar Yalamanchili

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Tags: Algorithms, Computational Complexity, Computer science, CUDA, Databases, Design space exploration, nVidia, Optimization, Tesla C2050

March 9, 2012 by hgpu

Compiler Assisted Runtime Adaptation

Vlad-Mihai Sima

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Tags: Compilers, Computer science, DSP, FPGA, Heterogeneous systems, OpenCL, Optimization, Thesis

March 8, 2012 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

GPU Programming Strategies and Trends in GPU Computing

Adaptive Input-aware Compilation for Graphics Engines

A computing origami: Optimized code generation for emerging parallel platforms

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

Auto-tuning a High-Level Language Targeted to GPU Codes

CUDA 2D Stencil Computations for the Jacobi Method

On the Use of Small 2D Convolutions on GPUs

Performance analysis and optimization of the OP2 framework on many-core architectures

Analyzing CUDA’s Compiler through the Visualization of Decoded GPU Binaries

A Study of CUDA Acceleration and Impact of Data Transfer Overhead in Heterogeneous Environment

Relational Algorithms for Multi-Bulk-Synchronous Processors

Compiler Assisted Runtime Adaptation

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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