high performance computing on graphics processing units: hgpu.org

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Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao

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Tags: Algorithms, Compression, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package

August 19, 2018 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Daniel Hanlon, Hamidreza Khalighzadeh, Ravi Reddy Manumachu, Alexey Lastovetsky

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Tags: Cloud, Computer science, CUDA, FPGA, Hybrid computing, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package

August 19, 2018 by hgpu

GPU parallelization of a hybrid pseudospectral fluid turbulence framework using CUDA

Duane Rosenberg, Pablo D. Mininni, Raghu Reddy, Annick Pouquet

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Tags: cfd, CUDA, FFT, Fluid dynamics, MPI, Numerical simulation, nVidia, OpenMP, Tesla K20, Tesla P100

August 11, 2018 by hgpu

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro

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Tags: Computer science, CUDA, Deep learning, LSTM, Mixed precision, Neural networks, nVidia, Package, Tesla V100, Torch

August 11, 2018 by hgpu

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, OpenMPI, Package

August 11, 2018 by hgpu

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Francisco M. Castro, Nicolas Guil, Manuel J. Marin-Jimenez, Jesus Perez-Serrano, Manuel Ujaldon

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Tags: Artificial intelligence, CNN, Computer science, Computer vision, CUDA, Deep learning, Energy-efficient computing, Neural networks, nVidia, nVidia GeForce GTX Titan X

August 5, 2018 by hgpu

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Rohan Garg, Apoore Mohan, Michael Sullivan, Gene Cooperman

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla P100

August 5, 2018 by hgpu

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

Sergey Khrapov, Alexander Khoperskov

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Tags: Astrophysics, CUDA, hydrodynamics, nVidia, OpenMP, Tesla K20, Tesla K40, Tesla K80

July 28, 2018 by hgpu

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Xiaowang Zhang, Mingyue Zhang, Peng Peng, Jiaming Song, Zhiyong Feng, Lei Zou

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Tags: Computer science, CUDA, Databases, nVidia, Sparse matrix, Tesla M40

July 28, 2018 by hgpu

Block-Size Independence for GPU Programs

Rajeev Alur, Joseph Devietti, Nimit Singhania

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

July 28, 2018 by hgpu

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

Gurbinder Gill, Roshan Dathathri, Loc Hoang, Andrew Lenharth, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, Tesla K80

July 21, 2018 by hgpu

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

Andrew Gloster, Lennon O Naraigh, Khang Ee Pang

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Tags: Computational Physics, CUDA, Differential equations, nVidia, nVidia GeForce GTX Titan X, Package, Partial differential equations, PDEs, Physics

July 21, 2018 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

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

GPU parallelization of a hybrid pseudospectral fluid turbulence framework using CUDA

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Block-Size Independence for GPU Programs

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

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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