high performance computing on graphics processing units: hgpu.org

hgpu.org » Task scheduling

Automated architecture-aware mapping of streaming applications onto GPUs

Andrei Hagiescu, Huynh Phung Huynh, Weng-Fai Wong, Rick Siow Mong Goh

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, OpenCL, Performance, Task scheduling, Tesla S1070, Tesla S2050

November 23, 2011 by hgpu

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Chris Gregg, Michael Boyer, Kim Hazelwood, Kevin Skadron

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Tags: ATI, ATI Radeon HD 5870, Computer science, Heterogeneous systems, OpenCL, Performance, Task scheduling

November 22, 2011 by hgpu

A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA

Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham

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Tags: Algorithms, Computer science, CUDA, Differential evolution, Genetic programming, Metaheuristics, nVidia, Task scheduling, Tesla C2050

November 2, 2011 by hgpu

An Implementation of Differential Evolution for Independent Tasks Scheduling on GPU

Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Differential evolution, Heterogeneous systems, nVidia, Optimization, Task scheduling, Tesla C2050

November 1, 2011 by hgpu

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

Alin Murarasu, Josef Weidendorfer, Arndt Bode

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Tags: Compression, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Task scheduling, Tesla C2050

October 31, 2011 by hgpu

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Mark Silberstein, Assaf Schuster, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Optimization, Package, Performance, Probability, Task scheduling, Tesla C2050

October 5, 2011 by hgpu

A New Class of Parallel Scheduling Algorithms

Wojciech Bozejko

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Tags: Algorithms, Book, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Programming techniques, Task scheduling, Tesla C1060, Tesla C2050

October 2, 2011 by hgpu

PGEM: Preemptive GPGPU Execution Model for Runtime Engines

Shinpei Kato, Karthik Lakshmanan, Aman Kumar, Mihir Kelkar, Yutaka Ishikawa, Ragunathan (Raj) Rajkumar

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Performance, Task scheduling

September 26, 2011 by hgpu

Dynamic Data Structures for Taskgraph Scheduling Policies with Applications in OpenCL Accelerators

Jakub Marecek, Andrew J. Parkes, Edmund K. Burke, Robert Elliot, Hedley Francis, Anton Lokhmotov

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Tags: Computer science, Data Structures and Algorithms, OpenCL, Task scheduling

September 24, 2011 by hgpu

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

Mark Joselli, Marcelo Zamith, Esteban Clua, Anselmo Montenegro, Regina Leal-Toledo, Aura Conci, Paulo Pagliosa, Luis Valente, Bruno Feijo

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Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce 8200 M, nVidia GeForce 8400 GS, nVidia GeForce 8800 GTS, Performance, Task scheduling

September 19, 2011 by hgpu

Processing data streams with hard real-time constraints on heterogeneous systems

Uri Verner, Assaf Schuster, Mark Silberstein

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Tags: AES, Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Performance, Security, Task scheduling

September 15, 2011 by hgpu

TimeGraph: GPU scheduling for real-time multi-tasking environments

Shinpei Kato, Karthik Lakshmanan, Ragunathan Rajkumar, Yutaka Ishikawa

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenGL, Operating systems, Package, Real-time graphics, Task scheduling

September 11, 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

Automated architecture-aware mapping of streaming applications onto GPUs

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA

An Implementation of Differential Evolution for Independent Tasks Scheduling on GPU

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

A New Class of Parallel Scheduling Algorithms

PGEM: Preemptive GPGPU Execution Model for Runtime Engines

Dynamic Data Structures for Taskgraph Scheduling Policies with Applications in OpenCL Accelerators

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

Processing data streams with hard real-time constraints on heterogeneous systems

TimeGraph: GPU scheduling for real-time multi-tasking environments

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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