high performance computing on graphics processing units: hgpu.org

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Enabling Traceability in an MDE Approach to Improve Performance of GPU Applications

Antonio Wendellde O. Rodrigues, Vincent Aranega, Anne Etien, Frederic Guyomarc'h, Jean-Luc Dekeyser

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Tags: Algorithms, Computer science, nVidia, OpenCL, Performance, Programming techniques, Tesla S1070

October 18, 2011 by hgpu

Data-Driven Programming Abstractions and Optimization for Multi-Core Platforms

Rebecca L. Collins

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Tags: Algorithms, Cell processor, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Programming techniques, Thesis

October 17, 2011 by hgpu

Dynamic Fine-Grain Scheduling of Pipeline Parallelism

Daniel Sanchez, David Lo, Richard M. Yoo, Jeremy Sugerman, Christos Kozyrakis

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques

October 17, 2011 by hgpu

Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

Eva Burrows

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 250, Programming techniques, Sorting, Thesis

October 17, 2011 by hgpu

GPU-to-CPU callbacks

Jeff A. Stuart, Michael Cox, John D. Owens

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

October 16, 2011 by hgpu

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Naoya Maruyama, Tatsuo Nomura, Kento Sato, Satoshi Matsuoka

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Source codes

Tags: Computer science, CUDA, MPI, nVidia, Optimization, Package, Performance, Programming Languages, Programming techniques, Tesla M2050

October 15, 2011 by hgpu

Accelerating code on multi-cores with FastFlow

Marco Aldinucci, Marco Danelutto, Peter Kilpatrick, Massimiliano Meneghin, Massimo Torquati

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Source codes

Tags: Computer science, Memory, Package, Performance, Programming techniques

October 15, 2011 by hgpu

An Analysis of Programmer Productivity versus Performance for High Level Data Parallel Programming

Alex Cole, Alistair McEwan, Satnam Singh

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 460, Programming techniques

October 14, 2011 by hgpu

Towards scalar synchronization in SIMT architectures

Arun Ramamurthy

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Performance, Programming techniques, Thesis

October 14, 2011 by hgpu

A Heterogeneous Parallel Framework for Domain-Specific Languages

Kevin J. Brown, Arvind K. Sujeeth, HyoukJoong Lee, Tiark Rompf, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Programming techniques, Tesla C2050

October 14, 2011 by hgpu

State of The Art Report on GPU

Rita Borgo, Ken Brodlie

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Tags: Computer science, CUDA, DirectX, GLSL, GPU cluster, Hardware, HLSL, Larrabee, nVidia, OpenGL, Programming techniques, Review

October 13, 2011 by hgpu

Performance Characterization and Optimization of Atomic Operations on AMD GPUs

Marwa Elteir, Heshan Lin, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, OpenCL, Optimization, Performance, Programming techniques

October 11, 2011 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Enabling Traceability in an MDE Approach to Improve Performance of GPU Applications

Data-Driven Programming Abstractions and Optimization for Multi-Core Platforms

Dynamic Fine-Grain Scheduling of Pipeline Parallelism

Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

GPU-to-CPU callbacks

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Accelerating code on multi-cores with FastFlow

An Analysis of Programmer Productivity versus Performance for High Level Data Parallel Programming

Towards scalar synchronization in SIMT architectures

A Heterogeneous Parallel Framework for Domain-Specific Languages

State of The Art Report on GPU

Performance Characterization and Optimization of Atomic Operations on AMD GPUs

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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