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OpenCL Based Digital Image Projection Acceleration

Bryan Michael Badalamenti

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Tags: Algorithms, Computer science, FPGA, Intel Xeon Phi, OpenCL, Tesla K10, Tesla K40, Thesis

September 24, 2015 by hgpu

Automatic OpenCL code generation for multi-device heterogeneous architectures

Pei Li, Elisabeth Brunet, Francois Trahay, Christian Parrot, Gael Thomas, Raymond Namyst

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Tags: Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro FX 5800, OpenCL

September 19, 2015 by hgpu

A Performance Comparison of Algebraic Multigrid Preconditioners on CPUs, GPUs, and Xeon Phis

K. Rupp, J. Weinbub, F. Rudolf, A. Morhammer, T. Grasser, A. Jungel

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Tags: AMD FirePro W9100, ATI, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, OpenMP, Performance, Tesla K20

September 9, 2015 by hgpu

Experimentation Procedure for Offloaded Mini-Apps Executed on Cluster Architectures with Xeon Phi Accelerators

Gary Lawson, Vaibhav Sundriyal, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, Molecular dynamics

September 9, 2015 by hgpu

Contributions to the Efficient Use of General Purpose Coprocessors: Kernel Density Estimation as Case Study

Unai Lopez Novoa

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Tags: Algorithms, ATI, ATI Radeon HD 6950, Computational Complexity, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 650, OpenCL, Thesis

September 8, 2015 by hgpu

Accelerated Deep Learning using Intel Xeon Phi

Andre Viebke

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Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Machine learning, Neural networks, OpenMP, Thesis

August 27, 2015 by hgpu

Parallelizing a high-order WENO scheme for complicated flow structures on GPU and MIC

Liang Deng, Fang Wang, Han-Li Bai, Qing-Xin Xu

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Tags: CUDA, Fluid dynamics, Intel Xeon Phi, nVidia, Tesla K20, Tesla M2050

August 18, 2015 by hgpu

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

Artur Malinowski

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Tags: Algorithms, Computer science, Intel Xeon Phi, Java

August 7, 2015 by hgpu

Flexible Linear Algebra Development and Scheduling with Cholesky Factorization

Azzam Haidar, Chongxiao Cao, Stanimire Tomov, Asim YarKhan, Piotr Luszczek, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, nVidia, Tesla M2090

August 5, 2015 by hgpu

Optimized Event-Driven Runtime Systems for Programmability and Performance

Sagnak Tasrlar

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Tags: Computer science, Intel Xeon Phi, OpenMP, Performance, Thesis

August 3, 2015 by hgpu

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla K20

July 30, 2015 by hgpu

Irregular algorithms on the Xeon Phi

Sander Lijbrink

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Tags: Algorithms, Computer science, Intel Xeon Phi, Matrix multiplication, OpenMP, Thesis

July 27, 2015 by hgpu

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

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

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

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

EnergyUCB-Bandit

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

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