high performance computing on graphics processing units: hgpu.org

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GPU Programming Strategies and Trends in GPU Computing

Andre R. Brodtkorb, Trond R. Hagen, Martin L. Saetra

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Tags: Computer science, Heterogeneous systems, Optimization, Overview

April 2, 2012 by hgpu

Nested Data-Parallelism on the GPU

Lars Bergstrom, John Reppy

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Tags: Algorithms, Compilers, Computer science, CUDA, Data parallelism, nVidia, Package, Programming Languages, Tesla C2050

March 31, 2012 by hgpu

Distributed Password Cracking Platform

Gerrie Veerman, Dimitar Pavlov

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Tags: Computer science, MPI, Review, Security, Thesis

March 31, 2012 by hgpu

Multi-GPU parallelization of a 3D Bayesian CT algorithm and its application on real foam reconstruction with incomplete data set

Nicolas Gac, Alexandre Vabre, Ali Mohammad Djafari

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Tags: Algorithms, Bayesian, Computed tomography, CT, CUDA, GPU cluster, Image reconstruction, Medicine, nVidia, Tesla C1060, Tomography

March 31, 2012 by hgpu

GPGPU-Accelerated Instruction Accurate and Fast Simulation of Thousand-core Platforms

Shivani Raghav, Martino Ruggiero, David Atienza, Christian Pinto, Andrea Marongiu, Luca Benini

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Tags: Computer science, CUDA, Design space exploration, nVidia

March 31, 2012 by hgpu

Adaptive Input-aware Compilation for Graphics Engines

Mehrzad Samadi, Amir Hormati, Mojtaba Mehrara, Janghaeng Lee, and Scott Mahlke

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Tags: Compiler, CUDA, GPU, nVidia, nVidia GeForce GTX 285, Optimization, Portability, Streaming, Tesla C2050

March 31, 2012 by Moaddeli

A Highly Parallel Reuse Distance Analysis Algorithm on GPUs

Huimin Cui, Qing Yi, Jingling Xue, Lei Wang, Yang Yang, Xiaobing Feng

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, Tesla C2050

March 30, 2012 by hgpu

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Jing Wu, Joseph JaJa

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C2050

March 30, 2012 by hgpu

Performance evaluation of GPU memory hierarchy using the FFT

Jacobo Lobeiras, Margarita Amor, Ramon Doallo

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Tags: Benchmarking, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Signal processing

March 30, 2012 by hgpu

A Performance Analysis Framework for Identifying Potential Benefits in GPGPU Applications

Jaewoong Sim, Aniruddha Dasgupta, Hyesoon Kim, and Richard Vuduc

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Tags: Analytical model, CUDA, GPGPU architecture, nVidia, Performance benefit prediction, Performance prediction, Tesla C2050

March 30, 2012 by Moaddeli

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

Cedric Augonnet

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Tags: Cell processor, Computer science, CUDA, Databases, nVidia, nVidia Quadro FX 4600, nVidia Quadro FX 5800, Task scheduling, Tesla C2050, Tesla M2070, Tesla S1070, Thesis

March 29, 2012 by hgpu

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

GPU Programming Strategies and Trends in GPU Computing

Nested Data-Parallelism on the GPU

Distributed Password Cracking Platform

Multi-GPU parallelization of a 3D Bayesian CT algorithm and its application on real foam reconstruction with incomplete data set

GPGPU-Accelerated Instruction Accurate and Fast Simulation of Thousand-core Platforms

Adaptive Input-aware Compilation for Graphics Engines

A Highly Parallel Reuse Distance Analysis Algorithm on GPUs

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Performance evaluation of GPU memory hierarchy using the FFT

A Performance Analysis Framework for Identifying Potential Benefits in GPGPU Applications

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

Recent source codes

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

HPX: a C++ Standard Library for Concurrency and Parallelism

TorchQC: Quantum Dynamics and Machine Learning

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

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