high performance computing on graphics processing units: hgpu.org

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Panda: A Compiler Framework for Concurrent CPU-GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

Mohammed Sourouri, Scott B. Baden, Xing Cai

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Tags: Code generation, Compilers, Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenMP, Tesla K20

June 21, 2017 by hgpu

AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters

Xiaojue Zhu, Everett Phillips, Vamsi Spandan, John Donners, Gregory Ruetsch, Josh Romero, Rodolfo Ostilla-Monico, Yantao Yang, Detlef Lohse, Roberto Verzicco, Massimiliano Fatica, Richard J.A.M. Stevens

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Source codes

Tags: cfd, CUDA, Fluid dynamics, Fortran, GPU cluster, MPI, Navier-Stokes equations, NSEs, nVidia, Package, Tesla K20, Tesla P100, Tesla T40

May 6, 2017 by hgpu

Energy conservation techniques for GPU computing

Xinxin Mei

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 980, Task scheduling, Thesis

March 28, 2017 by hgpu

Optimization of Lattice Boltzmann Simulations on Heterogeneous Computers

E. Calore, A. Gabbana, S. F. Schifano, R. Tripiccione

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Tags: AMD FirePro W9100, ATI, Fluid dynamics, GPU cluster, Heterogeneous systems, Intel Xeon Phi, Lattice Boltzmann model, MPI, nVidia, OpenACC, OpenMP, Performance, performance portability, Tesla K80

March 20, 2017 by hgpu

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean

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Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, TensorFlow, Tesla K40

January 26, 2017 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

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Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Woohyuk Choi, Sumin Hong, Won-Ki Jeong

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Tags: Clustering, Computer science, CUDA, Distributed computing, DSL, GPU cluster, Image processing, MapReduce, nVidia, nVidia GeForce GTX Titan, Python, Rendering, Spark

November 8, 2016 by hgpu

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Tarun Beri

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Tags: Computer science, CUDA, FFT, GPU cluster, Linear Algebra, Matrix multiplication, nVidia, Package, Prefetch, Tesla M2070, Thesis

November 5, 2016 by hgpu

Accelerating High-Throughput Computing through OpenCL

Andrei Dafinoiu, Joshua Higgins, Violeta Holmes

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Tags: ATI, ATI Radeon HD 6570, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 970, OpenCL, Tesla C2050

June 28, 2016 by hgpu

Solver for Systems of Linear Equations with Infinite Precision on a GPU Cluster

Jiri Khun

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Tags: AMD Radeon HD 8750 M, ATI, Computer science, Extended precision, GPU cluster, Linear Algebra, Modular arithmetic, MPI, OpenCL

June 2, 2016 by hgpu

GPIC – GPU Power Iteration Cluster

Gustavo R.L Silva, Rafael R. Medeiros, Antonio P. Braga, Douglas A.G. Vieira

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Tags: Computer science, CUDA, GPU cluster, nVidia, Tesla K40

April 14, 2016 by hgpu

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Ashwin Trikuta Srinath

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Tags: Algorithms, Computer science, CUDA, Finite difference, Fluid dynamics, GPU cluster, Numerical simulation, nVidia, OpenMPI, Tesla K20, Tesla K40, Thesis

February 3, 2016 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

Panda: A Compiler Framework for Concurrent CPU-GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters

Energy conservation techniques for GPU computing

Optimization of Lattice Boltzmann Simulations on Heterogeneous Computers

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

How to scale distributed deep learning?

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Accelerating High-Throughput Computing through OpenCL

Solver for Systems of Linear Equations with Infinite Precision on a GPU Cluster

GPIC – GPU Power Iteration Cluster

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

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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