high performance computing on graphics processing units: hgpu.org

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Power-Efficient Accelerators for High-Performance Applications

Ganesh Suryanarayan Dasika

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Tags: Computer science, Energy-efficient computing, Performance, Thesis

October 2, 2011 by hgpu

Design space exploration towards a realtime and energy-aware GPGPU-based analysis of biosensor data

Constantin Timm, Frank Weichert, Peter Marwedel, Heinrich Muller

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Tags: Computer science, Design space exploration, Energy-efficient computing, Image processing, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTS 250, nVidia ION, OpenCL, Sensing

September 28, 2011 by hgpu

Global optimization model on power efficiency of GPU and multicore processing element for SIMD computing with CUDA

Da-Qi Ren, Reiji Suda

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 8800 GTS, Optimization, Tesla C2050

September 28, 2011 by hgpu

The Test and Evaluation Uses of Heterogeneous Computing: GPGPUs and Other Approaches

Dan M. Davis, Gene Wagenbreth, Robert F. Lucas, Paul C. Gregory

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Tags: Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, Overview, Tesla C1060

September 25, 2011 by hgpu

Energy-efficient computing for extreme-scale science

David Donofrio, Leonid Oliker, John Shalf, Michael F. Wehner, Chris Rowen, Jens Krueger, Shoaib Kamil, Marghoob Mohiyuddin

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Tags: Computer science, Energy-efficient computing, Review

September 12, 2011 by hgpu

The future of microprocessors

Shekhar Borkar, Andrew A. Chien

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

September 7, 2011 by hgpu

Energy-efficient mechanisms for managing thread context in throughput processors

Mark Gebhart, Daniel R. Johnson, David Tarjan, Stephen W. Keckler, William J. Dally, Erik Lindholm, Kevin Skadron

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

September 7, 2011 by hgpu

An 8.6 mW 25 Mvertices/s 400-MFLOPS 800-MOPS 8.91 mm Multimedia Stream Processor Core for Mobile Applications

Shao-Yi Chien, You-Ming Tsao, Chin-Hsiang Chang, Yu-Cheng Lin

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

September 3, 2011 by hgpu

Energy-aware metrics for benchmarking heterogeneous systems

Simon McIntosh-Smith, Terry Wilson, Jon Crisp, Amaurys Avila Ibarra, Richard B. Sessions

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Tags: Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, Molecular modeling, nVidia, nVidia GeForce GTX 280, OpenCL, Tesla C2050

August 18, 2011 by hgpu

Power-efficient medical image processing using PUMA

Ganesh Dasika, Kevin Fan, Scott Mahlke

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Tags: Algorithms, CUDA, Energy-efficient computing, Image processing, Image reconstruction, Medicine, MRI, nVidia, nVidia GeForce GTX 280

July 31, 2011 by hgpu

An energy model for graphics processing units

J. Pool, A. Lastra, M. Singh

Tags: Algorithms, Computer science, Energy-efficient computing

July 25, 2011 by hgpu

Power and Performance Characterization of Computational Kernels on the GPU

Y. Jiao, H. Lin, P. Balaji, W. Feng

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Tags: Computer science, Energy-efficient computing, FFT, nVidia, nVidia GeForce GTX 280, OpenCL, Performance

July 9, 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

Power-Efficient Accelerators for High-Performance Applications

Design space exploration towards a realtime and energy-aware GPGPU-based analysis of biosensor data

Global optimization model on power efficiency of GPU and multicore processing element for SIMD computing with CUDA

The Test and Evaluation Uses of Heterogeneous Computing: GPGPUs and Other Approaches

Energy-efficient computing for extreme-scale science

The future of microprocessors

Energy-efficient mechanisms for managing thread context in throughput processors

An 8.6 mW 25 Mvertices/s 400-MFLOPS 800-MOPS 8.91 mm Multimedia Stream Processor Core for Mobile Applications

Energy-aware metrics for benchmarking heterogeneous systems

Power-efficient medical image processing using PUMA

An energy model for graphics processing units

Power and Performance Characterization of Computational Kernels on the GPU

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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