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cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Antti-Pekka Hynninen, Dmitry I. Lyakh

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Tesla K20, Tesla K40, Tesla M40, Tesla P100

May 6, 2017 by hgpu

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

Tatyana Dyakonova, Alexander Khoperskov, Sergey Khrapov

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Tags: cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, Tesla C2070, Tesla K20, Tesla K40, Tesla K80

May 6, 2017 by hgpu

GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

Zheyong Fan, Ville Vierimaa, Ari Harju

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Tags: Computational Physics, CUDA, nVidia, Package, Physics, Sparse matrix, Tesla K40

May 6, 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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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

TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems

Bita Darvish Rouhani, Azalia Mirhoseini, Farinaz Koushanfar

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Tags: ARM, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TK1, Performance

May 2, 2017 by hgpu

GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model

Michail Alvanos, Theodoros Christoudias

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Tags: Chemistry, Code generation, CUDA, Earth and Space Sciences, Fortran, nVidia, Package, Tesla M2070

May 2, 2017 by hgpu

Speeding up a few orders of magnitude the Jacobi method: high order Chebyshev-Jacobi over GPUs

J.E. Adsuara, I. Cordero-Carrion, P. Cerda-Duran, M.A. Aloy

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Tags: CUDA, Differential equations, Finite difference, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, Partial differential equations, PDEs, Tesla K40

May 2, 2017 by hgpu

Accelerating gravitational microlensing simulations using the Xeon Phi coprocessor

Bin Chen, Ronald Kantowski, Xinyu Dai, Eddie Baron, Paul Van der Mark

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Tags: Astrophysics, CUDA, Gravitation, Gravitational lensing, Intel Xeon Phi, nVidia, OpenMP, Tesla M2050

May 2, 2017 by hgpu

Deep Learning in the Automotive Industry: Applications and Tools

Andre Luckow, Matthew Cook, Nathan Ashcraft, Edwin Weill, Emil Djerekarov, Bennie Vorster

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia GRID K520, Performance

May 2, 2017 by hgpu

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, Jiaya Jia

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX Titan X

April 30, 2017 by hgpu

Automatic source code adaptation for heterogeneous platforms

Albert Saa-Garriga

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Tesla M2050, Thesis

April 30, 2017 by hgpu

Low-complexity Distributed Tomographic Backprojection for large datasets

Gilberto Martinez Jr., Janito V. Ferreira Filho, Eduardo X. Miqueles

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 740 M, nVidia GeForce GTX X, nVidia Tegra TX1, Package, Tomography

April 30, 2017 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

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

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

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

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

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high performance computing on graphics processing units: hgpu.org

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

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

TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems

GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model

Speeding up a few orders of magnitude the Jacobi method: high order Chebyshev-Jacobi over GPUs

Accelerating gravitational microlensing simulations using the Xeon Phi coprocessor

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

Automatic source code adaptation for heterogeneous platforms

Low-complexity Distributed Tomographic Backprojection for large datasets

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

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