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Programming Models and Scheduling Techniques for Heterogeneous Architectures

Judit Planas Carbonell

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OmpSs, Task scheduling, Tesla K20, Tesla K40, Tesla M2090, Thesis

January 4, 2016 by hgpu

Large-Scale Compute-Intensive Analysis via a Combined In-Situ and Co-Scheduling Workflow Approach

Christopher Sewell, Katrin Heitmann, Hal Finkel, George Zagaris, Suzanne T. Parete-Koon, Patricia K. Fasel, Adrian Pope, Nicholas Frontiere, Li-ta Lo, Bronson Messer, Salman Habib, James Ahrens

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Tags: Computer science, GPU cluster, N-body simulation, nVidia, OpenCL, Task scheduling, Tesla K20

December 12, 2015 by hgpu

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Linchuan Chen

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, Matrix multiplication, MPI, nVidia, Sparse matrix, Task scheduling, Tesla M2070, Thesis

October 13, 2015 by hgpu

Scheduling for new computing platforms with GPUs

Florence Monna

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Tags: Computer science, Data Structures and Algorithms, nVidia, OpenCL, Task scheduling, Tesla C2050, Thesis

August 24, 2015 by hgpu

Locality Aware Work-Stealing Based Scheduling in Hybrid CPU-GPU

A. Tarun Beri, B. Sorav Bansal, C. Subodh Kumar

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Tags: Computer science, CUDA, Distributed computing, nVidia, Task scheduling, Tesla M2070

August 21, 2015 by hgpu

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Terry Cojean, Abdou Guermouche, Andra Hugo, Raymond Namyst, Pierre-Andre Wacrenier

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, OpenMP, Task scheduling, Tesla K40

August 5, 2015 by hgpu

Real-Time Scheduling for GPUs with Applications in Advanced Automotive Systems

Glenn A. Elliott

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Tags: Computer science, Computer vision, nVidia, nVidia Quadro K5000, Task scheduling, Thesis

August 3, 2015 by hgpu

Multi-GPU Parallel Computing and Task Scheduling under Virtualization

Yujie Zhang, Jiabin Yuan, Xiangwen Lu, Xingfang Zhao

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

August 3, 2015 by hgpu

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Peng Zhang, Yuxiang Gao, Meikang Qiu

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Tags: Algorithms, Computer science, CUDA, Matrix inversion, Matrix multiplication, nVidia, Task scheduling, Tesla K40

August 3, 2015 by hgpu

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

Yuki Tsujita, Toshio Endo

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Tags: Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Task scheduling, Tesla K20

March 25, 2015 by hgpu

A Graph-Partition-Based Scheduling Policy for Heterogeneous Architectures

Hao Wu, Daniel Lohmann, Wolfgang Schroder-Preikschat

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

February 27, 2015 by hgpu

Multicore Scheduling of Parallel Real-Time Tasks with Multiple Parallelization Options

Jihye Kwon, Kang-Wook Kim, Sangyoun Paik, Jihwa Lee, Chang-Gun Lee

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Tags: Algorithms, Computer science, OpenCL, Task scheduling

December 18, 2014 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Large-Scale Compute-Intensive Analysis via a Combined In-Situ and Co-Scheduling Workflow Approach

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Scheduling for new computing platforms with GPUs

Locality Aware Work-Stealing Based Scheduling in Hybrid CPU-GPU

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Real-Time Scheduling for GPUs with Applications in Advanced Automotive Systems

Multi-GPU Parallel Computing and Task Scheduling under Virtualization

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

A Graph-Partition-Based Scheduling Policy for Heterogeneous Architectures

Multicore Scheduling of Parallel Real-Time Tasks with Multiple Parallelization Options

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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