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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

Adhoc On-Demand Distance Vector Protocol For Energy Efficiency

Abhilash C S, Abhishek Varshney, Dilip S, Navik Yogesh Laljibhai, Pradyot H Adavi

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 480, Security

January 3, 2014 by hgpu

Regression Modelling of Power Consumption for Heterogeneous Processors

Tahir Diop

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Tags: Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, OpenCL, Tesla M2090, Thesis

November 27, 2013 by hgpu

Performance and Power Comparisons Between Fermi and Cypress GPUs

Ying Zhang

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, OpenCL, Performance, Thesis

November 18, 2013 by hgpu

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Adhoc On-Demand Distance Vector Protocol For Energy Efficiency

Regression Modelling of Power Consumption for Heterogeneous Processors

Performance and Power Comparisons Between Fermi and Cypress GPUs

Recent source codes

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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