high performance computing on graphics processing units: hgpu.org

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High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

Fumiya Kono

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Tags: AMD FirePro W8100, AMD FirePro W9100, AMD Radeon R9 280X, ATI, CUDA, Earth and Space Sciences, FPGA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Tesla K20, Thesis

December 9, 2018 by hgpu

Dense and sparse parallel linear algebra algorithms on graphics processing units

Alejandro Lamas Davina

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, MPI, nVidia, Sparse, Tesla K20, Tesla M2090, Thesis

November 25, 2018 by hgpu

OpenCL Performance Prediction using Architecture-Independent Features

Beau Johnston, Greg Falzon, Josh Milthorpe

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Tags: AMD FirePro S9150, AMD Radeon R9 290X, AMD Radeon R9 295X2, AMD Radeon RX 480, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, OpenCL, Package, performance portability, Tesla K20, Tesla K40

November 3, 2018 by hgpu

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Swen Boehm, Swaroop Pophale, Veronica G. Vergara Larrea, Oscar Hernandez

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenACC, OpenCL, OpenMP, performance portability, Tesla K20, Tesla V100

September 23, 2018 by hgpu

A deep learning approach to autonomous lunar landing

Ilaria Bloise, Marcello Orlandelli

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, LSTM, Machine learning, Neural networks, nVidia, RNN, Tesla K20, Thesis

September 16, 2018 by hgpu

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Mohammed Mahmoud, Mark Hoffmann, Hassan Reza

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Tags: Algorithms, Computer science, CUDA, nVidia, Sparse matrix, Tesla K20

September 9, 2018 by hgpu

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

Tomas Ohberg

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, OpenCL, Tesla K20, Thesis

August 26, 2018 by hgpu

GPU parallelization of a hybrid pseudospectral fluid turbulence framework using CUDA

Duane Rosenberg, Pablo D. Mininni, Raghu Reddy, Annick Pouquet

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Tags: cfd, CUDA, FFT, Fluid dynamics, MPI, Numerical simulation, nVidia, OpenMP, Tesla K20, Tesla P100

August 11, 2018 by hgpu

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

Sergey Khrapov, Alexander Khoperskov

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Tags: Astrophysics, CUDA, hydrodynamics, nVidia, OpenMP, Tesla K20, Tesla K40, Tesla K80

July 28, 2018 by hgpu

Improving tasks throughput on accelerators using OpenCL command concurrency

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

June 28, 2018 by hgpu

Fast Locality Sensitive Hashing for Beam Search on GPU

Xing Shi, Shizhen Xu, Kevin Knight

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data Structures and Algorithms, Hashing, NLP, nVidia, Tesla K20

June 9, 2018 by hgpu

clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization

Jing Chen, Jianbin Fanga, Weifeng Liub, Tao Tang, Canqun Yang

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Tags: AMD Radeon R9 Fury X, ATI, Computer science, Factorization, Linear Algebra, nVidia, OpenCL, Package, Tesla K20

June 2, 2018 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

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high performance computing on graphics processing units: hgpu.org

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

Dense and sparse parallel linear algebra algorithms on graphics processing units

OpenCL Performance Prediction using Architecture-Independent Features

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

A deep learning approach to autonomous lunar landing

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

GPU parallelization of a hybrid pseudospectral fluid turbulence framework using CUDA

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

Improving tasks throughput on accelerators using OpenCL command concurrency

Fast Locality Sensitive Hashing for Beam Search on GPU

clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization

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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