high performance computing on graphics processing units: hgpu.org

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Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Gary Lawson, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

August 15, 2014 by hgpu

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

D. C. Price, M. A. Clark, B. R. Barsdell, R. Babich, L. J. Greenhill

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Source codes

Tags: Astrophysics, CUDA, Energy-efficient computing, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 780, Package, Performance, Signal processing, Tesla K20

August 1, 2014 by hgpu

Energy-Efficient Collective Reduce and Allreduce Operations on Distributed GPUs

Lena Oden, Benjamin Klenk, Holger Froning

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Tags: Computer science, CUDA, Distributed computing, Energy-efficient computing, Heterogeneous systems, nVidia, Tesla K20

July 17, 2014 by hgpu

Energy Efficiency Benefits of Reducing the Voltage Guardband on the Kepler GPU Architecture

Jingwen Leng, Yazhou Zu, Vijay Janapa Reddi

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 680

July 1, 2014 by hgpu

Effect And Analysis of Elastic Fidelity Computing On GPUs

Chetan Arvind Patil, Georgios Tziantzioulis, Nikos Hardavellas

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Tags: Computer science, CUDA, Energy-efficient computing, GPGPU-sim, nVidia, nVidia GeForce GTX 480, OpenCL

June 26, 2014 by hgpu

The Power-Performance Tradeoffs of the Intel Xeon Phi on HPC Applications

Bo Li, Hung-Ching Chang, Shuaiwen Leon Song, Chun-Yi Su, Timmy Meyer, John Mooring, Kirk Cameron

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, nVidia, Performance, Tesla C2050

June 22, 2014 by hgpu

Assessing the Performance-Energy Balance of Graphics Processors for Spectral Unmixing

Sergio Sanchez, German Leon, Antonio Plaza, Enrique S. Quintana-Orti

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Tags: ARM, CUDA, Energy-efficient computing, Image processing, nVidia, nVidia GeForce GTX 480, nVidia Quadro 1000 M, Tesla K20

May 5, 2014 by hgpu

On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications

A. Castello, J. Duato, R. Mayo, A. J. Pena, E. S. Quintana-Orti, V. Roca, F. Silla

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Tags: Bioinformatics, Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Virtualization

April 27, 2014 by hgpu

A Survey of Methods For Analyzing and Improving GPU Energy Efficiency

Sparsh Mittal, Jeffrey S. Vetter

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Tags: comparison, Computer science, cpu, CUDA, Energy-efficient computing, FPGA, Hardware Architecture, nVidia

April 18, 2014 by sparsh0mittal

Energy-Efficient FPGA Implementation for Binomial Option Pricing Using OpenCL

Valentin Mena Morales, Pierre-Henri Horrein, Amer Baghdadi, Erik Hochapfel, Sandrine Vaton

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Tags: Energy-efficient computing, Finance, FPGA, nVidia, nVidia GeForce GTX 660 Ti, OpenCL, Option pricing

April 17, 2014 by hgpu

Consolidating Applications for Energy Efficiency in Heterogeneous Computing Systems

Jing Zhang, Hao Wang, Heshan Lin, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Task scheduling

January 29, 2014 by hgpu

Power Profiling of GeMTC Many Task Computing

Sean Wallace, Scott Krieder, Ioan Raicu

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Tags: Computer science, CUDA, Energy-efficient computing, Intel Xeon Phi, nVidia

January 17, 2014 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

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

Energy-Efficient Collective Reduce and Allreduce Operations on Distributed GPUs

Energy Efficiency Benefits of Reducing the Voltage Guardband on the Kepler GPU Architecture

Effect And Analysis of Elastic Fidelity Computing On GPUs

The Power-Performance Tradeoffs of the Intel Xeon Phi on HPC Applications

Assessing the Performance-Energy Balance of Graphics Processors for Spectral Unmixing

On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications

A Survey of Methods For Analyzing and Improving GPU Energy Efficiency

Energy-Efficient FPGA Implementation for Binomial Option Pricing Using OpenCL

Consolidating Applications for Energy Efficiency in Heterogeneous Computing Systems

Power Profiling of GeMTC Many Task Computing

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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