high performance computing on graphics processing units: hgpu.org

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Portable Performance on Heterogeneous Architectures

Phitchaya Mangpo Phothilimthana, Jason Ansel, Jonathan Ragan-Kelley, Saman Amarasinghe

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Tags: Artificial intelligence, ATI, ATI Radeon HD 6630 M, Compilers, Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, OpenCL, Performance, Tesla C2070

February 2, 2013 by hgpu

An Automatic Host and Device Memory Allocation Method for OpenMPC

Hiroaki Uchiyama, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, OpenMP, Performance, Tesla C1060

December 20, 2012 by hgpu

The Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System

Qingyu Meng, Alan Humphrey, Martin Berzins

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, MPI, nVidia, nVidia GeForce GTX 570, Task scheduling, Tesla C2070

November 24, 2012 by hgpu

Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels

Apan Qasem

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Tags: Compilers, Computer science, CUDA, Memory model, nVidia, nVidia GeForce 9800 GT, Tesla C2050

November 8, 2012 by hgpu

Characterization and Exploitation of GPU Memory Systems

Kenneth Sydney Lee

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Tags: AMD Fusion, ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Computer science, Memory model, nVidia, nVidia GeForce GTX 280, OpenCL, Performance, Tesla C2075, Thesis

October 30, 2012 by hgpu

Task Parallelism and Data Distribution: An Overview of Explicit Parallel Programming Languages

Dounia Khaldi, Pierre Jouvelot, Corinne Ancourt, Francois Irigoin

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Tags: Computer science, Data parallelism, Memory model, OpenCL, OpenMP, Overview

October 23, 2012 by hgpu

CUDA-Zero: a framework for porting shared memory GPU applications to multi-GPUs

Chen DeHao, Chen WenGuang, Zheng WeiMin

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Tags: Computer science, CUDA, Memory model, nVidia, Tesla C1060

September 30, 2012 by hgpu

Fast and Efficient Automatic Memory Management for GPUs using Compiler-Assisted Runtime Coherence Scheme

Sreepathi Pai, R. Govindarajan, Matthew J. Thazhuthaveetil

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Tags: Computer science, CUDA, Memory model, nVidia, Performance, Tesla C1060

September 21, 2012 by hgpu

Shared Memory Multiplexing: A Novel Way to Improve GPGPU Throughput

Yi Yang, Ping Xiang, Mike Mantor, Norm Rubin, Huiyang Zhou

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance

August 21, 2012 by hgpu

Scaling CUDA for Distributed Heterogeneous Processors

Siu Kwan Lam

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, MPI, nVidia, Package, PTX, Python, Thesis

July 20, 2012 by hgpu

Parallel Memory Defragmentation on a GPU

Ronald Veldema, Michael Philippsen

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 560 Ti, Performance, Storage system

July 6, 2012 by hgpu

Design of a Hybrid Memory System for General-Purpose Graphics Processing Units

Patrick Carpenter

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, nVidia GeForce GTX 480, Tesla C2070, Tesla M2050, Thesis

May 6, 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

Portable Performance on Heterogeneous Architectures

An Automatic Host and Device Memory Allocation Method for OpenMPC

The Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System

Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels

Characterization and Exploitation of GPU Memory Systems

Task Parallelism and Data Distribution: An Overview of Explicit Parallel Programming Languages

CUDA-Zero: a framework for porting shared memory GPU applications to multi-GPUs

Fast and Efficient Automatic Memory Management for GPUs using Compiler-Assisted Runtime Coherence Scheme

Shared Memory Multiplexing: A Novel Way to Improve GPGPU Throughput

Scaling CUDA for Distributed Heterogeneous Processors

Parallel Memory Defragmentation on a GPU

Design of a Hybrid Memory System for General-Purpose Graphics Processing Units

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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