high performance computing on graphics processing units: hgpu.org

hgpu.org » Task scheduling

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Vishakha Gupta, Karsten Schwan, Niraj Tolia, Vanish Talwar, Parthasarathy Ranganathan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX, Operating systems, Task scheduling, Virtualization

September 7, 2011 by hgpu

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

Marcin Bogdanski, Peter R. Lewis, Tobias Becker, Xin Yao

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Tags: Algorithms, Computer science, Finance, FPGA, Heterogeneous systems, Machine learning, Task scheduling

September 3, 2011 by hgpu

SCF: a device- and language-independent task coordination framework for reconfigurable, heterogeneous systems

V. Aggarwal, R. Garcia, G. Stitt, A. George, H. Lam

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Tags: Computer science, FPGA, Heterogeneous systems, Performance, Software Engineering, Task scheduling

August 24, 2011 by hgpu

MDR: performance model driven runtime for heterogeneous parallel platforms

Jacques A. Pienaar, Anand Raghunathan, Srimat Chakradhar

Tags: Computer science, Heterogeneous systems, Performance, Programming techniques, Task scheduling

August 23, 2011 by hgpu

Towards a More Efficient Use of GPUs

Pedro Valero, Fernando L. Pelayo

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Tags: Computer science, nVidia, nVidia GeForce 9600 M GT, nVidia GeForce GTX 285, Performance, Task scheduling

July 30, 2011 by hgpu

Dynamic Self-Rescheduling of Tasks over a Heterogeneous Platform

A.P.D. Binotto, E.P. Freitas, M. Gotz, C.E. Pereira, A. Stork, T. Larsson

Tags: Algorithms, Computer science, Heterogeneous systems, Image processing, Task scheduling

July 29, 2011 by hgpu

Effective Dynamic Scheduling on Heterogeneous Multi/Manycore Desktop Platforms

Alecio P.D. Binotto, Bernardo M.V. Pedras, Marcelo Goetz, Arjan Kuijper, Carlos E. Pereira, Andre Stork, Dieter W. Fellner

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Task scheduling

July 27, 2011 by hgpu

Data-Aware Task Scheduling on Multi-accelerator Based Platforms

Cedric Augonnet, Jerome Clet-Ortega, Samuel Thibault, Raymond Namyst

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia Quadro FX 5800, Package, Prefetch, Task scheduling, Tesla S1070

July 26, 2011 by hgpu

Dynamic load balancing on heterogeneous multicore/multiGPU systems

Alejandro Acosta, Robert Corujo, Vicente Blanco, Francisco Almeida

Tags: Computer science, GPU cluster, Heterogeneous systems, Performance, Task scheduling

June 8, 2011 by hgpu

Task Partition Comparison between Multi-core System and GPU

Weimin Zheng, Bo Wang, Yongwei Wu

Tags: Algorithms, Computer science, Grid, Task scheduling

May 20, 2011 by hgpu

Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters

Koichi Shirahata, Hitoshi Sato, Satoshi Matsuoka

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Presentation, Task scheduling, Tesla S1070

April 16, 2011 by hgpu

A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL

Dominik Grewe, Michael O'Boyle

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Tags: ATI, ATI Radeon HD 5970, Benchmarking, Computer science, Heterogeneous systems, OpenCL, Package, Task scheduling

April 4, 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

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

SCF: a device- and language-independent task coordination framework for reconfigurable, heterogeneous systems

MDR: performance model driven runtime for heterogeneous parallel platforms

Towards a More Efficient Use of GPUs

Dynamic Self-Rescheduling of Tasks over a Heterogeneous Platform

Effective Dynamic Scheduling on Heterogeneous Multi/Manycore Desktop Platforms

Data-Aware Task Scheduling on Multi-accelerator Based Platforms

Dynamic load balancing on heterogeneous multicore/multiGPU systems

Task Partition Comparison between Multi-core System and GPU

Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters

A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL

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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