high performance computing on graphics processing units: hgpu.org

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Scheduling processing of real-time data streams on heterogeneous multi-GPU systems

Uri Verner, Assaf Schuster, Avi Mendelson, Mark Silberstein

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Security, Task scheduling

July 24, 2012 by hgpu

Multi-level Parallelization of Advanced Video Coding on Hybrid CPU/GPU Platform

Svetislav Momcilovic, Nuno Roma, Leonel Sousa

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Tags: Algorithms, CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 580, Task scheduling, Video encoding

July 10, 2012 by hgpu

A Scheduling Framework for a Heterogeneous Parallel Architecture

Wei Zhang

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 1800, Task scheduling, Tesla C1060, Thesis

June 30, 2012 by hgpu

GPU-accelerated Model Checking of Periodic Self-Suspending Real-Time Tasks

Per-Erik Mahl, Tim Liberg

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 320 M, nVidia GeForce GTX 275, Task scheduling, Thesis

June 23, 2012 by hgpu

Fine-Grained Resource Sharing for Concurrent GPGPU Kernels

Chris Gregg, Jonathan Dorn, Kim Hazelwood, Kevin Skadron

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Tags: Computer science, nVidia, nVidia GeForce GTX 460, OpenCL, Programming techniques, Task scheduling

May 24, 2012 by hgpu

Programming and Scheduling Model for Supporting Heterogeneous Accelerators in Linux

Tobias Beisel, Tobias Wiersema, Christian Plessl, Andre Brinkmann

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Operating systems, Package, Task scheduling

May 19, 2012 by hgpu

Radiation Modeling Using the Uintah Heterogeneous CPU/GPU Runtime System

Alan Humphrey, Qingyu Meng, Martin Berzins, Todd Harman

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Tags: CUDA, Fluid dynamics, GPU cluster, Heterogeneous systems, MPI, nVidia, Partial differential equations, Pthreads, Task scheduling, Tesla M2090

May 11, 2012 by hgpu

Enabling task-level scheduling on heterogeneous platforms

Enqiang Sun, Dana Schaa, Richard Bagley, Norman Rubin, David Kaeli

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Tags: ATI, ATI FirePro V9800, ATI Radeon HD 6970, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, OpenCL, Task scheduling

May 9, 2012 by hgpu

Heterogeneous Task Scheduling for Accelerated OpenMP

Thomas R. W. Scogland, Barry Rountree, Wu-chun Feng, Bronis R. de Supinski

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Task scheduling, Tesla C2050

May 4, 2012 by hgpu

Automatic NUMA Characterization using Cbench

Ryan Braithwaite, Wu-chun Feng, Patrick McCormick

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Task scheduling, Tesla C2050

May 2, 2012 by hgpu

A Programmable Processing Array Architecture Supporting Dynamic Task Scheduling and Module-Level Prefetching

Junghee Lee, Hyung Gyu Lee, Soonhoi Ha, Jongman Kim, Chrysostomos Nicopoulos

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro NVS 295, Prefetch, Programming techniques, Task scheduling

April 5, 2012 by hgpu

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

Cedric Augonnet

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Tags: Cell processor, Computer science, CUDA, Databases, nVidia, nVidia Quadro FX 4600, nVidia Quadro FX 5800, Task scheduling, Tesla C2050, Tesla M2070, Tesla S1070, Thesis

March 29, 2012 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

Scheduling processing of real-time data streams on heterogeneous multi-GPU systems

Multi-level Parallelization of Advanced Video Coding on Hybrid CPU/GPU Platform

A Scheduling Framework for a Heterogeneous Parallel Architecture

GPU-accelerated Model Checking of Periodic Self-Suspending Real-Time Tasks

Fine-Grained Resource Sharing for Concurrent GPGPU Kernels

Programming and Scheduling Model for Supporting Heterogeneous Accelerators in Linux

Radiation Modeling Using the Uintah Heterogeneous CPU/GPU Runtime System

Enabling task-level scheduling on heterogeneous platforms

Heterogeneous Task Scheduling for Accelerated OpenMP

Automatic NUMA Characterization using Cbench

A Programmable Processing Array Architecture Supporting Dynamic Task Scheduling and Module-Level Prefetching

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

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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