high performance computing on graphics processing units: hgpu.org

Programming

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Performance Portability Challenges for Fortran Applications

Abigail Hsu, David Neill Asanza, Joseph A. Schoonover, Zach Jibben, Neil N. Carlson, Robert Robey

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Tags: Computer science, CUDA, Finite difference, Fortran, nVidia, OpenMP, Package, performance portability, Tesla P100, Tesla V100

December 2, 2018 by hgpu

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

Charalampos Stylianopoulos, Linus Johansson, Oskar Olsson, Magnus Almgren

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Tags: ARM, Computer science, IoT, OpenCL, Package, Security

November 25, 2018 by hgpu

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

Linnan Wang, Wei Wu, Yiyang Zhao, Junyu Zhang, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, Rodrigo Fonseca

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Tags: Compression, Computer science, CUDA, Deep learning, FFT, GPU cluster, Neural networks, nVidia, Package, Tesla P100

November 25, 2018 by hgpu

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Enzo Rucci, Carlos Garcia, Guillermo Botella, Armando De Giusti, Marcelo Naiouf, Manuel Prieto-Matias

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Tags: Algorithms, Biology, FPGA, OpenCL, Package, Smith-Waterman algorithm

November 25, 2018 by hgpu

Modeling Deep Learning Accelerator Enabled GPUs

Md Aamir Raihan, Negar Goli, Tor Aamodt

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Tags: Computer science, CUDA, Deep learning, Hardware Architecture, Machine learning, nVidia, Tesla V100

November 25, 2018 by hgpu

Dense and sparse parallel linear algebra algorithms on graphics processing units

Alejandro Lamas Davina

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, MPI, nVidia, Sparse, Tesla K20, Tesla M2090, Thesis

November 25, 2018 by hgpu

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Chao Wu, Yaoxue Zhang, Yuezhi Zhou, Qiushi Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance

November 18, 2018 by hgpu

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

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Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

Guoping Long, Jun Yang, Kai Zhu, Wei Lin

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, nVidia, TensorFlow

November 18, 2018 by hgpu

StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

Gabor Racz, Istvan Szapudi, Laszlo Dobos, Istvan Csabai, Alexander S. Szalay

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Tags: Astrophysics, Cosmology, CUDA, MPI, N-body simulation, nVidia, OpenMP, Package, Tesla K80

November 18, 2018 by hgpu

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

Denis Demidov

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Tags: Computer science, CUDA, Differential equations, Mathematical Software, multigrid, nVidia, OpenCL, OpenMP, Package, Partial differential equations, PDEs, Tesla K80

November 18, 2018 by hgpu

Hashing, Caching, and Synchronization: Memory Techniques for Latency Masking Multithreaded Applications

Skyler Arron Windh

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Tags: Computer science, FPGA, Hashing, HLS, OpenCL, Thesis

November 11, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Performance Portability Challenges for Fortran Applications

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Modeling Deep Learning Accelerator Enabled GPUs

Dense and sparse parallel linear algebra algorithms on graphics processing units

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

Hashing, Caching, and Synchronization: Memory Techniques for Latency Masking Multithreaded Applications

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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