high performance computing on graphics processing units: hgpu.org

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GPGPU and MIC in Accelerated Cluster for Remote Sensed Image Processing Software

Olivier MELET, Toon Huysmans, Michel Hummel, Pierre-Marie Brunet

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Tags: CUDA, Image processing, Intel Xeon Phi, nVidia, nVidia GeForce GTX 480, OpenCL, Tesla K20

February 3, 2015 by hgpu

The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing

Blesson Varghese

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Tags: Computational Engineering, Computer science, CUDA, Finance, Intel Xeon Phi, nVidia, Performance, Tesla C2075

January 28, 2015 by hgpu

Methods and Metrics for Fair Server Assessment under Real-Time Financial Workloads

Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed, Ivor Spence, Richard Faloon, Dimitrios S. Nikolopoulos

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Tags: ARM, Computer science, Finance, Intel Xeon Phi, Option pricing

January 5, 2015 by hgpu

PyFAI: a Python library for high performance azimuthal integration on GPU

Jerome Kieffer, Giannis Ashiotis

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Source codes

Tags: Astrophysics, ATI, ATI FirePro V7800, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, OpenCL, Package, PyOpenCL, Python, Tesla K20

December 22, 2014 by hgpu

Easy-to-Use On-the-Fly Binary Program Acceleration on Many-Cores

Marvin Damschen, Christian Plessl

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Source codes

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

December 15, 2014 by hgpu

Lattice QCD with Domain Decomposition on Intel Xeon Phi Co-Processors

Simon Heybrock, Balint Joo, Dhiraj D. Kalamkar, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Tilo Wettig, Pradeep Dubey

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Tags: Algorithms, Computational Physics, High Energy Physics - Lattice, Intel Xeon Phi, Performance, Physics, QCD

December 9, 2014 by hgpu

Heterogeneous High Throughput Scientific Computing with APM X-Gene and Intel Xeon Phi

David Abdurachmanov, Brian Bockelman, Peter Elmer, Giulio Eulisse, Robert Knight, Shahzad Muzaffar

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Tags: ARM, Computational Physics, Computer science, Distributed computing, Heterogeneous systems, High Energy Physics - Experiment, Intel Xeon Phi, Physics

December 5, 2014 by hgpu

pyMIC: A Python Offload Module for the Intel Xeon Phi Coprocessor

Michael Klemm, Jussi Enkovaara

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Tags: Computer science, Intel Xeon Phi, Python

December 1, 2014 by hgpu

Conjugate gradient solvers on Intel Xeon Phi and NVIDIA GPUs

O. Kaczmarek, C. Schmidt, P. Steinbrecher, M. Wagner

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Tags: Computational Physics, Conjugate gradient solver, CUDA, High Energy Physics - Lattice, Intel Xeon Phi, Mathematical Software, nVidia, Physics, Tesla K20, Tesla K40

November 18, 2014 by hgpu

The Implementation of a Real-Time Polyphase Filter

Karel Adamek, Jan Novotny, Wes Armour

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Tags: CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 750 Ti, Performance, Signal processing, Tesla K40

November 16, 2014 by hgpu

Code Optimization on Kepler GPUs and Xeon Phi

Yong-Chull Jang, Hwancheol Jeong, Jangho Kim, Weonjong Lee, Jeonghwan Pak, Yuree Chung (SWME Collaboration)

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Tags: CUDA, High Energy Physics - Lattice, Intel Xeon Phi, Maxwell's equations, MPI, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, OpenMP, Performance, Physics, QCD, Tesla K40

November 12, 2014 by hgpu

Finite Element Numerical Integration on Xeon Phi coprocessor

Filip Kruzel, Krzysztof Banas

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Tags: Algorithms, Computer science, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

October 31, 2014 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

GPGPU and MIC in Accelerated Cluster for Remote Sensed Image Processing Software

The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing

Methods and Metrics for Fair Server Assessment under Real-Time Financial Workloads

PyFAI: a Python library for high performance azimuthal integration on GPU

Easy-to-Use On-the-Fly Binary Program Acceleration on Many-Cores

Lattice QCD with Domain Decomposition on Intel Xeon Phi Co-Processors

Heterogeneous High Throughput Scientific Computing with APM X-Gene and Intel Xeon Phi

pyMIC: A Python Offload Module for the Intel Xeon Phi Coprocessor

Conjugate gradient solvers on Intel Xeon Phi and NVIDIA GPUs

The Implementation of a Real-Time Polyphase Filter

Code Optimization on Kepler GPUs and Xeon Phi

Finite Element Numerical Integration on Xeon Phi coprocessor

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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