high performance computing on graphics processing units: hgpu.org

hgpu.org » Task scheduling

Globally scheduled real-time multiprocessor systems with GPUs

Glenn A. Elliott, James H. Anderson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Task scheduling

March 16, 2012 by hgpu

Dynamic Task-Scheduling and Resource Management for GPU Accelerators in Medical Imaging

Richard Membarth, Jan-Hugo Lupp, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

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Tags: CUDA, Image processing, Medicine, nVidia, nVidia Quadro FX 1800, Task scheduling, Tesla C2050

March 8, 2012 by hgpu

Using Graphical Processing Units in Scheduling Problems

K. Mihaly, O. Hornyak

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Tags: Algorithms, Computer science, CUDA, nVidia, Task scheduling, Tesla C1060

February 13, 2012 by hgpu

Work Stealing Inside GPUs

Julio Toss

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 280, Task scheduling, Tesla C2050, Thesis

February 13, 2012 by hgpu

Realtime scheduling using GPUs – proof of feasibility

Peter Fodrek, Ludovit Farkas, Tomas Murgas

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GT 310 M, OpenCL, Task scheduling

January 25, 2012 by hgpu

On the Correctness of the SIMT Execution Model of GPUs

Axel Habermaier, Alexander Knapp

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Tags: Computer science, CUDA, nVidia, Task scheduling, Thesis

January 20, 2012 by hgpu

Low-power Task Scheduling for GPU Energy Reduction

Li Tang, Yiji Zhang

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 210, nVidia GeForce GTX 280, nVidia GeForce GTX 570, Operating systems, Task scheduling

January 6, 2012 by hgpu

An optimal k-exclusion real-time locking protocol motivated by multi-GPU systems

Glenn A. Elliott, James H. Anderson

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Tags: Algorithms, Computer science, GPU cluster, Task scheduling

January 3, 2012 by hgpu

GPU implementations of scheduling heuristics for heterogeneous computing environments

Sergio Nesmachnow, Mauro Canabe

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Task scheduling, Tesla C1060

December 9, 2011 by hgpu

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Alecio Pedro Delazari Binotto

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, OpenCL, Task scheduling, Thesis

December 5, 2011 by hgpu

Dynamic Task Parallelism with a GPU Work-Stealing Runtime System

Sanjay Chatterjee, Max Grossman, Alina Sbirlea, Vivek Sarkar

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Tags: Computer science, CUDA, Data parallelism, nVidia, Task scheduling, Tesla C2050

November 29, 2011 by hgpu

Symphony: A Scheduler for Client-Server Applications on Coprocessor-based Heterogeneous Clusters

M. Mustafa Rafique, Srihari Cadambi, Kunal Rao, Ali R. Butt, Srimat Chakradhar

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Tags: Cluster computing, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Task scheduling, Tesla C2050

November 24, 2011 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

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

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

BoltzGen:Toward Universal Binder Design

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

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high performance computing on graphics processing units: hgpu.org

Globally scheduled real-time multiprocessor systems with GPUs

Dynamic Task-Scheduling and Resource Management for GPU Accelerators in Medical Imaging

Using Graphical Processing Units in Scheduling Problems

Work Stealing Inside GPUs

Realtime scheduling using GPUs – proof of feasibility

On the Correctness of the SIMT Execution Model of GPUs

Low-power Task Scheduling for GPU Energy Reduction

An optimal k-exclusion real-time locking protocol motivated by multi-GPU systems

GPU implementations of scheduling heuristics for heterogeneous computing environments

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Dynamic Task Parallelism with a GPU Work-Stealing Runtime System

Symphony: A Scheduler for Client-Server Applications on Coprocessor-based Heterogeneous Clusters

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

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

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

BoltzGen:Toward Universal Binder Design

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

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