high performance computing on graphics processing units: hgpu.org

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Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

J. Enos, C. Steffen, J. Fullop, M. Showerman, G. Shi, K. Esler, V. Kindratenko, J. Stone, J. Phillips

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Tags: Computer science, CUDA, Energy-efficient computing, GPU cluster, nVidia, Performance

February 3, 2011 by hgpu

Acceleration and Energy Efficiency of a Geometric Algebra Computation using Reconfigurable Computers and GPUs

Holger Lange, Florian Stock, Andreas Koch, Dietmar Hildenbrand

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Tags: Computer science, CUDA, Energy-efficient computing, FPGA, nVidia, nVidia GeForce GTX 280

January 27, 2011 by hgpu

SPRAT: Runtime processor selection for energy-aware computing

Hiroyuki Takizawa, Katsuto Sato, Hiroaki Kobayashi

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Tags: Computer science, CUDA, Energy-efficient computing, High-level Languages, nVidia, nVidia GeForce 8800 GTX

January 20, 2011 by hgpu

Improving energy and power efficiency using NComputing and approaches for predicting reliability of complex computing systems

Hoang Pham, Hoang Pham

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Tags: Computer science, Energy-efficient computing, nVidia, nVidia GeForce 9800 GX2

January 8, 2011 by hgpu

Power Consumption of GPUs from a Software Perspective

Sylvain Collange, David Defour, Arnaud Tisserand

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 9800 GX2, Tesla C870, Tesla D870, Tesla T10P

January 7, 2011 by hgpu

A low-power handheld GPU using logarithmic arithmetic and triple DVFS power domains

Byeong-Gyu Nam, Jeabin Lee, Kwanho Kim, Seung Jin Lee, and Hoi-Jun Yoo

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Tags: 3D Graphics and Realism, Computer science, Energy-efficient computing, OpenGL, Presentation

December 28, 2010 by hgpu

BLAS Comparison on FPGA, CPU and GPU

Srinidhi Kestur, John D. Davis, Oliver Williams

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Tags: BLAS, Computer science, CUBLAS, CUDA, Energy-efficient computing, FPGA, Linear Algebra, nVidia, nVidia GeForce 9500 GT, Performance, Tesla C1060

December 6, 2010 by hgpu

A flexible simulation framework for graphics architectures

J. W. Sheaffer, D. Luebke, K. Skadron

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Source codes

Tags: Computer science, Energy-efficient computing, nVidia, nVidia GeForce 4, OpenGL, Package

November 29, 2010 by hgpu

On the energy efficiency of graphics processing units for scientific computing

S. Huang, S. Xiao, W. Feng

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280

November 26, 2010 by hgpu

State-of-the-art in heterogeneous computing

Andre R. Brodtkorb, Christopher Dyken, Trond R. Hagen, Jon M. Hjelmervik, Olaf O. Storaasli

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Tags: Computer science, Energy-efficient computing, Review

November 22, 2010 by hgpu

Algorithm level power efficiency optimization for CPU-GPU processing element in data intensive SIMD/SPMD computing

Da Q. Ren

Tags: Computer science, CUDA, Energy-efficient computing, nVidia

November 22, 2010 by hgpu

Power Efficient Large Matrices Multiplication by Load Scheduling on Multi-core and GPU Platform with CUDA

DaQi Ren, Reiji Suda

Tags: Computer science, CUDA, Energy-efficient computing, Matrix multiplication, nVidia

November 8, 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

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high performance computing on graphics processing units: hgpu.org

Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

Acceleration and Energy Efficiency of a Geometric Algebra Computation using Reconfigurable Computers and GPUs

SPRAT: Runtime processor selection for energy-aware computing

Improving energy and power efficiency using NComputing and approaches for predicting reliability of complex computing systems

Power Consumption of GPUs from a Software Perspective

A low-power handheld GPU using logarithmic arithmetic and triple DVFS power domains

BLAS Comparison on FPGA, CPU and GPU

A flexible simulation framework for graphics architectures

On the energy efficiency of graphics processing units for scientific computing

State-of-the-art in heterogeneous computing

Algorithm level power efficiency optimization for CPU-GPU processing element in data intensive SIMD/SPMD computing

Power Efficient Large Matrices Multiplication by Load Scheduling on Multi-core and GPU Platform with CUDA

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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