high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

GPU-accelerated Bernstein-Bezier discontinuous Galerkin methods for wave problems

Jesse Chan, T. Warburton

View

Download (PDF)

Tags: Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX 980, OCCA

December 22, 2015 by hgpu

Performance and Productivity of Parallel Python Programming: A study with a CFD Test Case

Achim Basermann, Melven Rohrig-Zollner, Joachim Illmer

View

Download (PDF)

Tags: CUDA, Fluid dynamics, Fortran, nVidia, Performance, Python, Tesla C2075

December 22, 2015 by hgpu

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Dumitrel Loghin, Lavanya Ramapantulu, Oana Barbu, Yong Meng Teo

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 750 Ti, nVidia Jetson TK1

December 22, 2015 by hgpu

Parallel FDTD Arithmetic Simulation Based on Distributed Heterogeneous Cluster System

Feng Yuan, Tang Xiaobin, Gong Xiaoyan

View

Download (PDF)

Tags: Computer science, Electrodynamics, FDTD, Finite-difference time-domain, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenCL, Physics, Tesla K20, Tesla M2070

December 22, 2015 by hgpu

Autotuning Stencils Codes with Algorithmic Skeletons

Chris Cummins

View

Download (PDF)

Source codes

Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Package

December 19, 2015 by hgpu

Study, Modelling and Implementation of the Level Set Method Used in Micromachining Processes

Carles Montoliu Alvaro

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 560, nVidia GeForce GTX Titan, Thesis

December 19, 2015 by hgpu

Investigation of the SYCL for OpenCL Programming Model

Tim Beattie

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, OpenCL, Performance, Thesis

December 19, 2015 by hgpu

Efficient Query Processing in Co-Processor-accelerated Databases

Sebastian Bress

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 640, Package, Tesla C1060, Tesla C2050, Thesis

December 19, 2015 by hgpu

Challenges Adapting CUDA PIC Codes to multiple GPUs

Olav Emil Eiksund

View

Download (PDF)

Tags: Benchmarking, CUDA, Electrostatics, FFT, nVidia, nVidia GeForce GTX 980, OpenMP, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Tesla K20, Thesis

December 19, 2015 by hgpu

Origami: A Convolutional Network Accelerator

Lukas Cavigelli, David Gschwend, Christoph Mayer, Samuel Willi, Beat Muheim, Luca Benini

View

Download (PDF)

Tags: Algorithms, Artificial intelligence, ASIC, Computer science, Computer vision, Machine learning, Neural networks

December 15, 2015 by hgpu

A CUDA Kernel Scheduler Exploiting Static Data Dependencies

Eva Burrows

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Task scheduler

December 15, 2015 by hgpu

Run-time support for multi-level disjoint memory address spaces

Javier Bueno Hedo

View

Download (PDF)

Tags: Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 480, OmpSs, OpenMP, Thesis

December 15, 2015 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

GPU-accelerated Bernstein-Bezier discontinuous Galerkin methods for wave problems

Performance and Productivity of Parallel Python Programming: A study with a CFD Test Case

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Parallel FDTD Arithmetic Simulation Based on Distributed Heterogeneous Cluster System

Autotuning Stencils Codes with Algorithmic Skeletons

Study, Modelling and Implementation of the Level Set Method Used in Micromachining Processes

Investigation of the SYCL for OpenCL Programming Model

Efficient Query Processing in Co-Processor-accelerated Databases

Challenges Adapting CUDA PIC Codes to multiple GPUs

Origami: A Convolutional Network Accelerator

A CUDA Kernel Scheduler Exploiting Static Data Dependencies

Run-time support for multi-level disjoint memory address spaces

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

Most viewed papers (last 30 days)