high performance computing on graphics processing units: hgpu.org

hgpu.org » Programming techniques

High-performance CUDA kernel execution on FPGAs

Alexandros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen Mei

View

Download (PDF)

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

January 11, 2011 by hgpu

Parallel programming for multimedia applications

Hari Kalva, Aleksandar Colic, Adriana Garcia, Borko Furht

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

January 9, 2011 by hgpu

Raising the level of many-core programming with compiler technology: meeting a grand challenge

Wen-mei Hwu

Tags: Compilers, Computer science, CUDA, nVidia, OpenCL, Programming techniques

January 9, 2011 by hgpu

Four styles of parallel and net programming

Zhiwei Xu, Yongqiang He, Wei Lin, Li Zha

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

January 8, 2011 by hgpu

Ubiquitous Parallel Computing from Berkeley, Illinois, and Stanford

Bryan Catanzaro, Armando Fox, Kurt Keutzer, David Patterson, Bor-Yiing Su, Marc Snir, Kunle Olukotun, Pat Hanrahan, Hassan Chafi

View

Download (PDF)

Tags: Computer science, Programming techniques, Review

January 5, 2011 by hgpu

Practical Random Linear Network Coding on GPUs

Xiaowen Chu, Kaiyong Zhao and Mea Wang

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Programming techniques

January 4, 2011 by hgpu

A hardware-aware debugger for the OpenGL shading language

Magnus Strengert, Thomas Klein, Thomas Ertl

View

Download (PDF)

Source codes

Tags: Computer science, GLSL, High-level Languages, nVidia, OpenGL, Package, Programming techniques

January 3, 2011 by hgpu

Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware

Yong Cao, Debprakash Patnaik, Sean Ponce, Jeremy Archuleta, Patrick Butler, Wu-chun Feng and Naren Ramakrishnan

View

Download (PDF)

Tags: Computer science, CUDA, Data mining, Neuroscience, nVidia, nVidia GeForce GTX 280, Programming techniques

January 2, 2011 by hgpu

Automatic parallelization for graphics processing units

Alan Leung, Ondrej Lhotak, Ghulam Lashari

View

Download (PDF)

Tags: Computer science, Java, nVidia, nVidia GeForce 7800, Programming techniques, RapidMind

January 2, 2011 by hgpu

Introduction to GPGPU programming

Jay E. Steele, Robert Cochran

Tags: Computer science, High-level Languages, Programming techniques, Review

December 29, 2010 by hgpu

A code motion technique for accelerating general-purpose computation on the GPU

Takatoshi Ikeda, Fumihiko Ino, and Kenichi Hagihar

View

Download (PDF)

Tags: Computer science, nVidia, nVidia GeForce 6800, OpenGL, Programming techniques

December 24, 2010 by hgpu

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

Frederico Pratas, Pedro Trancoso, Alexandros Stama

View

Download (PDF)

Tags: Cell processor, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Performance, Programming techniques

December 24, 2010 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

* * *

high performance computing on graphics processing units: hgpu.org

High-performance CUDA kernel execution on FPGAs

Parallel programming for multimedia applications

Raising the level of many-core programming with compiler technology: meeting a grand challenge

Four styles of parallel and net programming

Ubiquitous Parallel Computing from Berkeley, Illinois, and Stanford

Practical Random Linear Network Coding on GPUs

A hardware-aware debugger for the OpenGL shading language

Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware

Automatic parallelization for graphics processing units

Introduction to GPGPU programming

A code motion technique for accelerating general-purpose computation on the GPU

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

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

Most viewed papers (last 30 days)