high performance computing on graphics processing units: hgpu.org

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Indigo: A Domain-Specific Language for Fast, Portable Image Reconstruction

Michael Driscoll, Benjamin Brock, Frank Ong, Jonathan Tamir, Hsiou-Yuan Liu, Michael Lustig, Armando Fox, Katherine Yelick

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Tags: CUDA, FFT, Image processing, Image reconstruction, Intel Xeon Phi, Magnetic resonance imaging, Microscopy, MRI, nVidia, nVidia GeForce GTX Titan X, Package

June 13, 2018 by hgpu

Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

Shizhao Chen, Jianbin Fang, Donglin Chen, Chuanfu Xu, Zheng Wang

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Tags: Computer science, Intel Xeon Phi, Machine learning, OpenMP, Performance, Sparse matrix

June 9, 2018 by hgpu

One machine, one minute, three billion tetrahedra

Celestin Marot, Jeanne Pellerin, Jean-Francois Remacle

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Tags: Computational Geometry, Computer science, Graphics, Intel Xeon Phi, Package

May 26, 2018 by hgpu

Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe

Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari, Evarist Fomenko, Eden Segal

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Tags: Caffe, Cloud, Computer science, Deep learning, Intel Xeon Phi, Neural networks, Package

May 26, 2018 by hgpu

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

Beau Johnston, Josh Milthorpe

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Tags: AMD FirePro S9150, AMD Radeon HD 7970, AMD Radeon R9 290X, AMD Radeon R9 Fury X, AMD Radeon RX 480, ATI, Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Tesla K20, Tesla K40

May 12, 2018 by hgpu

EngineCL: Usability and Performance in Heterogeneous Computing

Raul Nozal, Jose Luis Bosque, Ramon Beivide

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Tags: AMD Radeon R7, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, performance portability, Tesla K20

May 12, 2018 by hgpu

Auto-tuning Streamed Applications on Intel Xeon Phi

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

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Tags: Computer science, Intel Xeon Phi, Machine learning, Package, Performance

May 5, 2018 by hgpu

Towards a Unified CPU-GPU code hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures

Ludomir Oteski, Guillaume De Verdiere, Sylvain Contassot-Vivier, Stephane Vialle, Juliet Ryan

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Tags: Computer science, CUDA, Fluid dynamics, Intel Xeon Phi, nVidia, Tesla K20, Tesla P100

April 15, 2018 by hgpu

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Yusuke Nagasaka, Satoshi Matsuoka, Ariful Azad, Aydin Buluc

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Tags: Algorithms, Computer science, Intel Xeon Phi, Matrix multiplication, Sparse matrix

April 7, 2018 by hgpu

Optimization of Hierarchical Matrix Computation on GPU

Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota

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Tags: Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenMP, Tesla P100

March 25, 2018 by hgpu

The VOLNA-OP2 Tsunami Code (Version 1.0)

Istvan Z Reguly, Devaraj Gopinathan, Joakim H Beck, Michael B Giles, Serge Guillas, Frederic Dias

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Tags: CUDA, Earth and Space Sciences, Intel Xeon Phi, nVidia, Package, Tesla P100

March 22, 2018 by hgpu

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Moritz Kreutzer, Georg Hager, Dominik Ernst, Holger Fehske, Alan R. Bishop, Gerhard Wellein

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Tags: Chemistry, Computational Physics, Computer science, CUDA, Intel Xeon Phi, nVidia, OpenMP, Package, Performance, Physics, Quantum Physics, Tesla P100

March 10, 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

Indigo: A Domain-Specific Language for Fast, Portable Image Reconstruction

Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

One machine, one minute, three billion tetrahedra

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

EngineCL: Usability and Performance in Heterogeneous Computing

Auto-tuning Streamed Applications on Intel Xeon Phi

Towards a Unified CPU-GPU code hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Optimization of Hierarchical Matrix Computation on GPU

The VOLNA-OP2 Tsunami Code (Version 1.0)

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

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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