high performance computing on graphics processing units: hgpu.org

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Anti-parallel Patterns in Fine-grain Data-parallel Programs

Jan Cornelis

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Tags: Benchmarking, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 280, Performance, Thesis

October 10, 2011 by hgpu

Benchmarks Based on Anti-Parallel Patterns for the Evaluation of GPUs

Jan G. Cornelis, Jan Lemeire

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Tags: ATI, ATI Radeon HD 5850, Benchmarking, Computer science, nVidia, nVidia GeForce GTX 280, OpenCL

October 10, 2011 by hgpu

Intel’s Array Building Blocks: A retargetable, dynamic compiler and embedded language

Chris J. Newburn, Byoungro So, Zhenying Liu, Michael McCool, Anwar Ghuloum, Stefanus Du Toit, Zhi Gang Wang, Zhao Hui Du, Yongjian Chen, Gansha Wu, Peng Guo, Zhanglin Liu and Dan Zhang

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Tags: Benchmarking, Code generation, Computer science, Heterogeneous systems, Optimization

October 7, 2011 by hgpu

Divergence Analysis and Optimizations

Bruno Coutinho, Diogo Sampaio, Fernando Magno Quintao Pereira, Wagner Meira Jr.

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Optimization, Package, PTX

October 6, 2011 by hgpu

Offloading Java to Graphics Processors

Peter Calvert

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce GTX 260

October 6, 2011 by hgpu

Parallelisation of Java for Graphics Processors

Peter Calvert

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 260, Package, Thesis

October 6, 2011 by hgpu

Performance Analysis and Optimisation 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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Tags: Benchmarking, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 260, OpenMP, Partial differential equations, PDEs, Tesla C2050

October 5, 2011 by hgpu

Parallel Direct Simulation Monte Carlo Computation Using CUDA on GPUs

C.-C. Su, C.-W. Hsieh, M. R. Smith, M. C. Jermy, and J.-S. Wua

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Tags: Benchmarking, Computer science, CUDA, Monte Carlo simulation, nVidia, Tesla C1060

October 5, 2011 by hgpu

Flexible, high performance convolutional neural networks for image classification

Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber

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Tags: Benchmarking, Computer science, CUDA, Image processing, Neural networks, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia GeForce GTX 580

October 5, 2011 by hgpu

Berkeley Dwarfs on CUDA

Paul Springer

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Thesis

October 4, 2011 by hgpu

A Comprehensive Performance Comparison of CUDA and OpenCL

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Cell processor, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Optimization, Performance

October 1, 2011 by hgpu

Exploring The Latency and Bandwidth Tolerance of CUDA Applications

Gregory Diamos, Sudnya Padalikar

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, Memory, nVidia, Performance, PTX, Tesla C1060

September 30, 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

Anti-parallel Patterns in Fine-grain Data-parallel Programs

Benchmarks Based on Anti-Parallel Patterns for the Evaluation of GPUs

Intel’s Array Building Blocks: A retargetable, dynamic compiler and embedded language

Divergence Analysis and Optimizations

Offloading Java to Graphics Processors

Parallelisation of Java for Graphics Processors

Performance Analysis and Optimisation of the OP2 Framework on Many-core Architectures

Parallel Direct Simulation Monte Carlo Computation Using CUDA on GPUs

Flexible, high performance convolutional neural networks for image classification

Berkeley Dwarfs on CUDA

A Comprehensive Performance Comparison of CUDA and OpenCL

Exploring The Latency and Bandwidth Tolerance of CUDA Applications

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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