high performance computing on graphics processing units: hgpu.org

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Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Michel Steuwer, Christian Fensch, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Programming Languages

February 10, 2015 by hgpu

Comparison of OpenCL performance on different platforms using VexCL and Blaze

Hammad Mazhar, Dan Negrut

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Tags: AMD Radeon R7 260, ATI, Benchmarking, Computer science, Intel Xeon Phi, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20

February 6, 2015 by hgpu

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

Naznin Fauzia, Louis-Noel Pouchet, P. Sadayappan

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Tags: Computer science, CUDA, Memory, nVidia, Performance, PTX, Tesla K20

February 2, 2015 by hgpu

Different Optimization Strategies and Performance Evaluation of Reduction on Multicore CUDA Architecture

Chhaya Patel

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance

January 30, 2015 by hgpu

The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing

Blesson Varghese

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Tags: Computational Engineering, Computer science, CUDA, Finance, Intel Xeon Phi, nVidia, Performance, Tesla C2075

January 28, 2015 by hgpu

GPU computing architecture for irregular parallelism

Wilson Wai Lun Fung

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Performance, Thesis

January 26, 2015 by hgpu

Performance Analysis of Join Algorithms on GPUs

Ran Rui, Hao Li, Yi-Cheng Tu

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Tags: Algorithms, Computer science, CUDA, Databases, Join, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, Performance

January 26, 2015 by hgpu

Can Portability Improve Performance? An Empirical Study of Parallel Graph Analytics

Ana Lucia Varbanescu, Merijn Verstraaten, Cees de Laat, Ate Penders, Alexandru Iosup, Henk Sips

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Tags: Algorithms, Computer science, Graph theory, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Tesla C2050

January 23, 2015 by hgpu

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Matrix multiplication, nVidia, OpenCL, Performance, Tesla K20

January 21, 2015 by hgpu

GPU concurrency: Weak behaviours and programming assumptions

Jade Alglave, Mark Batty, Alastair F. Donaldson, Ganesh Gopalakrishnan, Jeroen Ketema, Daniel Poetzl, Tyler Sorensen, John Wickerson

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Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Computer science, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 540 M, nVidia GeForce GTX 660, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Tesla C2075

January 21, 2015 by hgpu

Global finite element matrix construction based on a CPU-GPU implementation

Francisco Javier Ramírez-Gil, Marcos de Sales Guerra Tsuzuki, Wilfredo Montealegre-Rubio

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Tags: Analysis of PDEs, Computational Engineering, CUDA, FEM, Numerical Analysis, parallel computing, PDEs, Performance

January 21, 2015 by fjramireg

Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

Chapa Martell Mario Alberto, Hiroyuki Sato

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Tags: Computer science, nVidia, nVidia GeForce GTX 660, OpenCL, Performance

January 13, 2015 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

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Comparison of OpenCL performance on different platforms using VexCL and Blaze

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

Different Optimization Strategies and Performance Evaluation of Reduction on Multicore CUDA Architecture

The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing

GPU computing architecture for irregular parallelism

Performance Analysis of Join Algorithms on GPUs

Can Portability Improve Performance? An Empirical Study of Parallel Graph Analytics

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

GPU concurrency: Weak behaviours and programming assumptions

Global finite element matrix construction based on a CPU-GPU implementation

Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

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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