high performance computing on graphics processing units: hgpu.org

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A Game Architecture Based on Multiple GPUs With Energy Management

Marcelo Zamith, Luis Valente, Mark Joselli, Jose Silva Junior, Esteban Clua, Bruno Feijo

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Tags: Computer science, Energy-efficient computing, Games, nVidia, nVidia GeForce GTX 480, Tesla K20

November 8, 2013 by hgpu

Initial Explorations of ARM Processors for Scientific Computing

David Abdurachmanov, Peter Elmer, Giulio Eulisse, Shahzad Muzaffar

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Tags: ARM, Computational Physics, Energy-efficient computing, High Energy Physics - Experiment, Numerical Analysis, Physics

November 4, 2013 by hgpu

PAKCK: Performance and Power Analysis of Key Computational Kernels on CPUs and GPUs

Julia S. Mullen, Michael M. Wolf, Anna Klein

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, Tesla C2075

November 3, 2013 by hgpu

Energy Efficiency Studies of Mont Blanc Applications

Mads Holden

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Source codes

Tags: ARM, Computer science, Energy-efficient computing, Matrix multiplication, OpenCL, Package, Thesis

October 21, 2013 by hgpu

Power Control for GPU Clusters in processing large-scale streams

Qingkui Chen, Haifeng Wang, Bocheng Liu

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Tags: Computer science, CUDA, Energy-efficient computing, GPU cluster, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480

October 13, 2013 by hgpu

APOGEE: adaptive prefetching on GPUs for energy efficiency

Ankit Sethia, Ganesh Dasika, Mehrzad Samadi, Scott Mahlke

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

September 28, 2013 by hgpu

On the Performance and Energy-efficiency of Multi-core SIMD CPUs and CUDA-enabled GPUs

Ronald Duarte, Resit Sendag, Frederick J. Vetter

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, Performance

September 16, 2013 by hgpu

An Energy Optimization of a GPU Application by Grid Design Space Exploration

Hana Park, Jungmin So, Young-Woong Ko, Jeong-Gun Lee

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Tags: Computer science, CUDA, Design space exploration, Energy-efficient computing, nVidia, nVidia GeForce GTX 660

August 8, 2013 by hgpu

Effects of Dynamic Voltage and Frequency Scaling on a K20 GPU

Rong Ge, Ryan Vogt, Jahangir Majumder, Arif Alam, Martin Burtscher, Ziliang Zong

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

July 24, 2013 by hgpu

Comparison and Analysis of GPU Energy Efficiency For CUDA and OpenCL

Joe Jackson

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Tags: Computer science, CUDA, Energy-efficient computing, Matrix multiplication, nVidia, nVidia GeForce 9800 GT, OpenCL

July 8, 2013 by hgpu

An Energy Efficient GPGPU Memory Hierarchy with Tiny Incoherent Caches

Alamelu Sankaranarayanan, Ehsan K. Ardestani, Jose Luis Briz, Jose Renau

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Tags: Computer science, CUDA, Energy-efficient computing, GPGPU-sim, Memory, nVidia, OpenCL

June 24, 2013 by hgpu

Exploring GPGPUs Workload Characteristics and Power Consumption

Sohan Lal, Jan Lucas, Mauricio Alvarez-Mesa, Ahmed Elhossini, Ben Juurlink

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Tags: Computer science, CUDA, Energy-efficient computing, GPGPU-sim, nVidia, nVidia GeForce GTX 580, Performance

June 22, 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

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high performance computing on graphics processing units: hgpu.org

A Game Architecture Based on Multiple GPUs With Energy Management

Initial Explorations of ARM Processors for Scientific Computing

PAKCK: Performance and Power Analysis of Key Computational Kernels on CPUs and GPUs

Energy Efficiency Studies of Mont Blanc Applications

Power Control for GPU Clusters in processing large-scale streams

APOGEE: adaptive prefetching on GPUs for energy efficiency

On the Performance and Energy-efficiency of Multi-core SIMD CPUs and CUDA-enabled GPUs

An Energy Optimization of a GPU Application by Grid Design Space Exploration

Effects of Dynamic Voltage and Frequency Scaling on a K20 GPU

Comparison and Analysis of GPU Energy Efficiency For CUDA and OpenCL

An Energy Efficient GPGPU Memory Hierarchy with Tiny Incoherent Caches

Exploring GPGPUs Workload Characteristics and Power Consumption

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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