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Using the pyMIC Offload Module in PyFR

Michael Klemm, Freddie Witherden, Peter Vincent

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Tags: Computer science, CUDA, Fluid dynamics, Intel Xeon Phi, Navier-Stokes equations, NSEs, OpenCL, OpenMP, Package, Python

July 8, 2016 by hgpu

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

Michel Steuwer, Toomas Remmelg, Christophe Dubach

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Tags: ARM, ATI, ATI Radeon HD 7970, BLAS, Code generation, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan Black, OpenCL, performance portability

July 8, 2016 by hgpu

An Adaptive Multi-Spline Refinement Algorithm in Simulation Based Sailboat Trajectory Optimization Using Onboard Multi-Core Computer Systems

Roman Debski

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GT 650 M, OpenCL

July 5, 2016 by hgpu

Time Predictability of GPU Kernel on an HSA Compliant Platform

Marcus Larsson, Nandinbaatar Tsog

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Tags: ATI, Computer science, Heterogeneous systems, HSA, OpenCL, Thesis

July 5, 2016 by hgpu

OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

Fang Huang, Shuanshuan Bu, Jian Tao, Xicheng Tan

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Tags: Algorithms, Earth and Space Sciences, Geoscience, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

July 5, 2016 by hgpu

A Synchronization-Free Algorithm for Parallel Sparse Triangular Solves

Weifeng Liu, Ang Li, Jonathan Hogg, Iain S. Duff, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 Fury X, ATI, Computer science, CUDA, Linear Algebra, nVidia GeForce GTX Titan X, OpenCL, Package, Sparse direct solvers, Tesla K40

June 28, 2016 by hgpu

Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks

Daniel Chavez

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Tags: Computer science, CUDA, nVidia, nVidia Quadro 1000 M, OpenCL, Pthreads, Thesis

June 28, 2016 by hgpu

Accelerating High-Throughput Computing through OpenCL

Andrei Dafinoiu, Joshua Higgins, Violeta Holmes

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Tags: ATI, ATI Radeon HD 6570, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 970, OpenCL, Tesla C2050

June 28, 2016 by hgpu

Efficient and portable multi-tasking for heterogeneous systems

Christos Margiolas

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Tags: ATI, ATI Radeon HD 5970, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 580, OpenCL, Tesla K20, Thesis, Virtualization

June 22, 2016 by hgpu

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Maohua Zhu, Liu Liu, Chao Wang, Yuan Xie

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Tags: CNN, Computer science, CUDA, Deep learning, FPGA, MapReduce, Neural networks, nVidia, OpenCL, Tesla K40

June 22, 2016 by hgpu

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

Hugh Perkins

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Tags: Algorithms, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Package

June 21, 2016 by hgpu

Splotch: porting and optimizing for the Xeon Phi

Timothy Dykes, Claudio Gheller, Marzia Rivi, Mel Krokos

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Tags: Computer science, CUDA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Package, Tesla K20, Visualization

June 16, 2016 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Using the pyMIC Offload Module in PyFR

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

An Adaptive Multi-Spline Refinement Algorithm in Simulation Based Sailboat Trajectory Optimization Using Onboard Multi-Core Computer Systems

Time Predictability of GPU Kernel on an HSA Compliant Platform

OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

A Synchronization-Free Algorithm for Parallel Sparse Triangular Solves

Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks

Accelerating High-Throughput Computing through OpenCL

Efficient and portable multi-tasking for heterogeneous systems

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

Splotch: porting and optimizing for the Xeon Phi

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CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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