high performance computing on graphics processing units: hgpu.org

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Performance Analysis on Energy Efficient High-Performance Architectures

Roman Iakymchuk, Francois Trahay

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

June 16, 2013 by hgpu

Cost-Effective Soft-Error Protection for SRAM-Based Structures in GPGPUs

Jingweijia Tan, Zhi Li, Xin Fu

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

June 13, 2013 by hgpu

How a Single Chip Causes Massive Power Bills. GPUSimPow: A GPGPU Power Simulator

Jan Lucas, Sohan Lal, Michael Andersch, Mauricio Alvarez-Mesa, Ben Juurlink

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Source codes

Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 580, Package

June 7, 2013 by hgpu

pVOCL: Power-Aware Dynamic Placement and Migration in Virtualized GPU Environments

Palden Lama, Yan Li, Ashwin M. Aji, Pavan Balaji, James Dinan, Shucai Xiao, Yunquan Zhang, Wu-chun Feng, Rajeev Thakur, Xiaobo Zhou

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Tags: Computer science, Energy-efficient computing, nVidia, OpenCL, Tesla C1060, Virtualization

April 8, 2013 by hgpu

The Stencil Processing Unit: GPGPU Done Right

Sanjay Rajopadhye, Guillaume Iooss, Tomofumi Yuki, Dan Connors

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Tags: Computer science, Energy-efficient computing

April 3, 2013 by hgpu

Exploring power efficiency and optimizations targeting heterogeneous applications

Yash Sanjeev Ukidave

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Tags: AMD Fusion, ATI, ATI Radeon HD 7770, Computer science, Energy-efficient computing, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, Thesis

March 16, 2013 by hgpu

A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

Shuaiwen Song, Chunyi Su, Barry Rountree, Kirk W. Cameron

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Tags: Biomolecules, Computer science, CUDA, Electrostatics, Energy-efficient computing, GPU cluster, Heterogeneous systems, Machine learning, nVidia, Tesla C2075, Tesla M2090

February 18, 2013 by hgpu

Scan Test Power Simulation on GPGPUs

S. Holst, E. Schneider, H.-J. Wunderlich

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

January 18, 2013 by hgpu

Inter-Warp Instruction Temporal Locality in Deep-Multithreaded GPUs

Ahmad Lashgar, Amirali Baniasadi, Ahmad Khonsari

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Tags: Computer science, Energy-efficient computing, GPGPU-sim, nVidia, Tesla

January 17, 2013 by hgpu

Energy efficiency vs. performance of the numerical solution of PDEs: an application study on a low-power ARM-based cluster

Dominik Goeddeke, Dimitri Komatitsch, Markus Geveler, Dirk Ribbrock, Nikola Rajovic, Nikola Puzovic, Alex Ramirez

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Tags: ARM, Differential equations, Energy-efficient computing, Fluid dynamics, Fluid simulation, Lattice Boltzmann model, nVidia, Partial differential equations, PDEs, Spectral elements

November 26, 2012 by hgpu

Power and Performance Analysis of GPU-Accelerated Systems

Yuki Abe, Hiroshi Sasaki, Martin Peres, Koji Inoue, Kazuaki Murakami, Shinpei Kato

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

September 6, 2012 by hgpu

GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures

Kai Ma, Xue Li, Wei Chen, Chi Zhang, Xiaorui Wang

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Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX

June 26, 2012 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

Performance Analysis on Energy Efficient High-Performance Architectures

Cost-Effective Soft-Error Protection for SRAM-Based Structures in GPGPUs

How a Single Chip Causes Massive Power Bills. GPUSimPow: A GPGPU Power Simulator

pVOCL: Power-Aware Dynamic Placement and Migration in Virtualized GPU Environments

The Stencil Processing Unit: GPGPU Done Right

Exploring power efficiency and optimizations targeting heterogeneous applications

A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

Scan Test Power Simulation on GPGPUs

Inter-Warp Instruction Temporal Locality in Deep-Multithreaded GPUs

Energy efficiency vs. performance of the numerical solution of PDEs: an application study on a low-power ARM-based cluster

Power and Performance Analysis of GPU-Accelerated Systems

GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures

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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