high performance computing on graphics processing units: hgpu.org

hgpu.org » Intel Phi

Performance of Kepler GTX Titan GPUs and Xeon Phi System

Hwancheol Jeong, Weonjong Lee, Jeonghwan Pak, Kwang-jong Choi, Sang-Hyun Park, Jun-sik Yoo, Joo Hwan Kim, Joungjin Lee, Young Woo Lee

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Tags: Computational Physics, CUDA, Intel Phi, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan, Performance, Physics, Tesla K20

November 6, 2013 by hgpu

Use of Checkpoint-Restart for Complex HEP Software on Traditional Architectures and Intel MIC

Kapil Arya, Gene Cooperman, Andrea Dotti, Peter Elmer

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Tags: Computational Physics, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, Numerical Analysis, Physics

November 4, 2013 by hgpu

First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC

V. Halyo, P. LeGresley, P. Lujan, V. Karpusenko, A. Vladimirov

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Tags: Algorithms, Computational Physics, CUDA, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, nVidia, Physics, Tesla K20

October 29, 2013 by hgpu

An Empirical Study of Intel Xeon Phi

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu

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

October 25, 2013 by hgpu

SIMD Parallel Gibbs Sampling of Probabilistic Directed Acyclic Graphs

Alireza S. Mahani, Mansour T.A. Sharabiani

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Tags: Artificial intelligence, Bayesian, Intel Phi, Intel Xeon Phi, OpenMP, Statistics

October 22, 2013 by hgpu

Understanding the Costs of Many-Task Computing Workloads on Intel Xeon Phi Coprocessors

Jeffrey Johnson, Scott J. Krieder, Benjamin Grimmer, Justin M. Wozniaky, Michael Wildeyz, Ioan Raicu

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Tags: Computer science, Heterogeneous systems, Intel, Intel Phi

October 21, 2013 by hgpu

Heterogeneous Clustering with Homogeneous Code: Accelerate MPI Applications Without Code Surgery Using Intel Xeon Phi Coprocessors

Andrey Vladimirov and Vadim Karpusenko

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Tags: Heterogeneous systems, Intel Phi, Intel Xeon Phi, Monte Carlo simulation, MPI, Portability, Task scheduling

October 18, 2013 by Andrey Vladimirov

UPC on MIC: Early Experiences with Native and Symmetric Modes

M.Luo ,M. Li, A. Venkatesh, X. Lu, D. K. Panda

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Tags: Computer science, Intel Phi, Intel Xeon Phi, Programming Languages

October 9, 2013 by hgpu

Performance Portability Evaluation for OpenACC on Intel Knights Corner and Nvidia Kepler

Yichao Wang, Qiang Qin, Simon Chong Wee SEE, James Lin

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Tags: Computer science, Heterogeneous systems, Intel Phi, nVidia, OpenACC, Tesla K20

October 4, 2013 by hgpu

Kokkos: Enabling performance portability across manycore architectures

H. Carter Edwards, Christian R. Trott

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Source codes

Tags: Code generation, Computer science, CUDA, Intel Phi, Molecular dynamics, nVidia, Package, Tesla K20

September 17, 2013 by hgpu

A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers

Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba

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Source codes

Tags: Biomolecules, Boltzmann equation, Computational Physics, CUDA, Electrostatics, Intel Phi, nVidia, Package, Physics, Poisson Boltzmann, Python, Tesla C2075

September 17, 2013 by hgpu

Porting to the Intel Xeon Phi: Opportunities and Challenges

C. Rosales

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Intel Phi, MPI, nVidia, Optimization, Performance

September 15, 2013 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

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high performance computing on graphics processing units: hgpu.org

Performance of Kepler GTX Titan GPUs and Xeon Phi System

Use of Checkpoint-Restart for Complex HEP Software on Traditional Architectures and Intel MIC

First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC

An Empirical Study of Intel Xeon Phi

SIMD Parallel Gibbs Sampling of Probabilistic Directed Acyclic Graphs

Understanding the Costs of Many-Task Computing Workloads on Intel Xeon Phi Coprocessors

Heterogeneous Clustering with Homogeneous Code: Accelerate MPI Applications Without Code Surgery Using Intel Xeon Phi Coprocessors

UPC on MIC: Early Experiences with Native and Symmetric Modes

Performance Portability Evaluation for OpenACC on Intel Knights Corner and Nvidia Kepler

Kokkos: Enabling performance portability across manycore architectures

A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers

Porting to the Intel Xeon Phi: Opportunities and Challenges

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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