high performance computing on graphics processing units: hgpu.org

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Comparing performance and energy efficiency of FPGAs and GPUs for high productivity computing

Brahim Betkaoui, David B. Thomas, Wayne Luk

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Tags: Computer science, CUDA, Energy-efficient computing, FPGA, nVidia, Performance, Tesla C1060

July 7, 2011 by hgpu

Introducing Energy Efficiency into Graphics Processors

B. V. N. Silpa, Preeti Ranjan Panda

Tags: Computer science, Energy-efficient computing, Hardware Architecture

July 7, 2011 by hgpu

Energy-saving techniques for low-power graphics processing unit

Chia-Ming Chang, Shao-Yi Chien, You-Ming Tsao, Chih-Hao Sun, Ka-Hang Lok, Yu-Jung Cheng

Tags: Computer science, Energy-efficient computing, Hardware Architecture

July 4, 2011 by hgpu

Performance and Power Efficiency Analysis of the Symmetric Cryptograph on Two Stream Processor Architectures

Guang Xu, Hong An, Gu Liu, Ping Yao, Mu Xu, Wenting Han, Xiaoqiang Li, Xiurui Hao

Tags: AES, Computer science, Energy-efficient computing, nVidia, nVidia GeForce 9800 GTX, Security

June 28, 2011 by hgpu

In Situ Power Analysis of General Purpose Graphical Processing Units

M.Z. Shaikh, M. Gregoire, W. Li, M. Wroblewski, S. Simon

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

June 14, 2011 by hgpu

Coordinate strip-mining and kernel fusion to lower power consumption on GPU

Guibin Wang

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

May 22, 2011 by hgpu

GPU-based X server on top of EGL and openVG

Dongkyun Jeong, Kamalneet Singh, Namin Kim, Soochan Lim

Tags: Computer science, Energy-efficient computing, Operating systems

May 16, 2011 by hgpu

Performance and power consumption investigation for execution of integer operations on CPU and GPU processors for multimedia applications

A. Iovanovici, C. Visan, M. Marcu

Tags: Computer science, Energy-efficient computing, Performance

May 14, 2011 by hgpu

A 57mW embedded mixed-mode neuro-fuzzy accelerator for intelligent multi-core processor

Jinwook Oh, Junyoung Park, Gyeonghoon Kim, Seungjin Lee, Hoi-Jun Yoo

Tags: Artificial intelligence, ASIC, Computer science, Energy-efficient computing, Neural networks

May 4, 2011 by hgpu

Statistical power modeling of GPU kernels using performance counters

Hitoshi Nagasaka, Naoya Maruyama, Akira Nukada, Toshio Endo, Satoshi Matsuoka

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 285

May 3, 2011 by hgpu

EcoG: A Power-Efficient GPU Cluster Architecture for Scientific Computing

Mike Showerman, Jeremy Enos, Craig Steffen, Sean Treichler, William Gropp, Wen-mei W. Hwu

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

April 30, 2011 by hgpu

Accurate Measurements and Precise Modeling of Power Dissipation of CUDA Kernels toward Power Optimized High Performance CPU-GPU Computing

R. Suda, Da Qi Ren

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 8800 GTS

April 16, 2011 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

Comparing performance and energy efficiency of FPGAs and GPUs for high productivity computing

Introducing Energy Efficiency into Graphics Processors

Energy-saving techniques for low-power graphics processing unit

Performance and Power Efficiency Analysis of the Symmetric Cryptograph on Two Stream Processor Architectures

In Situ Power Analysis of General Purpose Graphical Processing Units

Coordinate strip-mining and kernel fusion to lower power consumption on GPU

GPU-based X server on top of EGL and openVG

Performance and power consumption investigation for execution of integer operations on CPU and GPU processors for multimedia applications

A 57mW embedded mixed-mode neuro-fuzzy accelerator for intelligent multi-core processor

Statistical power modeling of GPU kernels using performance counters

EcoG: A Power-Efficient GPU Cluster Architecture for Scientific Computing

Accurate Measurements and Precise Modeling of Power Dissipation of CUDA Kernels toward Power Optimized High Performance CPU-GPU Computing

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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