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Deep learning with COTS HPC systems

Adam Coates, Brody Huval, Tao Wang, David J. Wu, Andrew Y. Ng, Bryan Catanzaro

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Tags: Computer science, CUDA, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 680

June 19, 2013 by hgpu

High Resolution Sparse Voxel DAGs

Viktor Kampe, Erik Sintorn, Ulf Assarsson

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Raytracing

June 8, 2013 by hgpu

A comprehensive study of Dynamic Memory Management in OpenCL kernels

Roy Spliet

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Tags: Algorithms, ATI, Computer science, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX 480, nVidia GeForce GTX 680, OpenCL, Performance, Tesla C2050, Tesla K20, Thesis

June 6, 2013 by hgpu

Implicit Skinning: Real-Time Skin Deformation with Contact Modeling

Rodolphe Vaillant, Loic Barthe, Gael Guennebaud, Marie-Paule Cani, Damien Rohmer, Brian Wyvill, Olivier Gourmel, Mathias Paulin

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680

June 6, 2013 by hgpu

Plasma Visualization in Parallel using Particle Systems on Graphical Processing Units

Timothy S. Lyes, K. A. Hawick

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Tags: Computer science, CUDA, Electrostatics, nVidia, nVidia GeForce GTX 680, nVidia Quadro FX 4000, Plasma physics, Rendering, Visualization

June 4, 2013 by hgpu

GPU Acceleration of a Genetic Algorithm for the Synthesis of FSM-based Bimodal Predictors

Martin Burtscher, Hassan Rabeti

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Tags: Algorithms, CAD, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 680

June 4, 2013 by hgpu

Improved Performance of CaFE and IRIS Model Fitting Using CUDA

Dylan Jackson

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Tags: Biology, Biomolecules, CUDA, nVidia, nVidia GeForce GTX 680, Sensing, Thesis

May 31, 2013 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Performance, Tesla C2050, Tesla C2070

May 30, 2013 by hgpu

Job Parallelism using Graphical Processing Unit individual Multi-Processors and Highly Localised Memory

D. P. Playne, K. A. Hawick

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Tesla M2075

May 29, 2013 by hgpu

On-Demand Generating and Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K. A. Hawick, D. P. Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

May 25, 2013 by hgpu

Evaluating the Performance of Legacy Applications on Emerging Parallel Architectures

Simon John Pennycook

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Source codes

Tags: Algorithms, ATI, ATI FirePro V7800, Benchmarking, Computer science, CUDA, MPI, nVidia, nVidia GeForce 8400 GS, nVidia GeForce 9800 GT, nVidia GeForce GTX 680, OpenCL, Performance, Tesla C1060, Tesla C2050, Thesis

May 21, 2013 by hgpu

Extended Data Collection: Analysis of Cache Behavior and Performance of Different BVH Memory Layouts for Tracing Incoherent Rays

Andre Schulz, Sven Widmer, Dominik Wodniok, Michael Goesele

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Raytracing, Tesla C2070, Tesla K20, Visualization

May 16, 2013 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

Deep learning with COTS HPC systems

High Resolution Sparse Voxel DAGs

A comprehensive study of Dynamic Memory Management in OpenCL kernels

Implicit Skinning: Real-Time Skin Deformation with Contact Modeling

Plasma Visualization in Parallel using Particle Systems on Graphical Processing Units

GPU Acceleration of a Genetic Algorithm for the Synthesis of FSM-based Bimodal Predictors

Improved Performance of CaFE and IRIS Model Fitting Using CUDA

Performance Tradeoff Spectrum of Integer and Floating Point Applications

Job Parallelism using Graphical Processing Unit individual Multi-Processors and Highly Localised Memory

On-Demand Generating and Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Evaluating the Performance of Legacy Applications on Emerging Parallel Architectures

Extended Data Collection: Analysis of Cache Behavior and Performance of Different BVH Memory Layouts for Tracing Incoherent Rays

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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