high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Quadro GV100, Performance, Tesla A100

May 8, 2022 by hgpu

Analytical Performance Estimation during Code Generation on Modern GPUs

Dominik Ernst, Markus Holzer, Georg Hager, Matthias Knorr, Gerhard Wellein

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, OpenCL, Package, Tesla A100, Tesla V100

May 8, 2022 by hgpu

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

Chao Chen, Chris Porter, Santosh Pande

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, CUDA, nVidia, Package, Task scheduling, Tesla P100, Tesla V100

May 1, 2022 by hgpu

End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning

Yao Xiao, Guixiang Ma, Nesreen K. Ahmed, Mihai Capota, Theodore Willke, Shahin Nazarian, Paul Bogdan

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, OpenCL

May 1, 2022 by hgpu

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Pietro Ghiglio, Uwe Dolinsky, Mehdi Goli, Kumudha Narasimhan

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, OpenCL, Performance, performance portability, SYCL

May 1, 2022 by hgpu

Efficient Execution of OpenMP on GPUs

Joseph Huber, Melanie Cornelius, Giorgis Georgakoudis, Shilei Tian, Jose M Monsalve Diaz, Kuter Dinel, Barbara Chapman, Johannes Doerfert

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, HPC, LLVM, nVidia, OpenMP, Package, Tesla V100

May 1, 2022 by hgpu

Performance Comparison of Different OpenCL Implementations of LBM Simulation on Commodity Computer Hardware

Jelena Tekic, Predrag Tekic, Milos Rackovic

View

Download (PDF)

Tags: AMD Radeon R5 M420, AMD Radeon R5 M430, ATI, cfd, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 940, nVidia GeForce GTX 960, OpenCL, Performance, Tesla C2070

April 17, 2022 by hgpu

Explicit caching HYB: a new high-performance SpMV framework on GPGPU

Chong Chen

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, FEM, Finite element method, nVidia, Package, Sparse matrix, Tesla V100

April 17, 2022 by hgpu

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

Yannik Könneker

View

Download (PDF)

Source codes

Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1080, OpenACC, OpenCL, Package, Performance, Tesla V100, Thesis

April 17, 2022 by hgpu

Optimizing Performance and Energy Efficiency in Massively Parallel Systems

Raúl Nozal González

View

Download (PDF)

Source codes

Tags: AMD Radeon RX 5700 XT, ATI, Computer science, Heterogeneous systems, HPC, nVidia, nVidia GeForce GTX 950, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, SYCL, Tesla K20, Thesis

April 10, 2022 by hgpu

Performance Models for Heterogeneous Iterative Programs

Aparna Sasidharan

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla V100

April 10, 2022 by hgpu

Persistent Kernels for Iterative Memory-bound GPU Applications

Lingqi Zhang, Mohamed Wahib, Peng Chen, Jintao Meng, Xiao Wang, Satoshi Matsuoka

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla A100, Tesla V100

April 10, 2022 by hgpu

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

ThunderKittens: Tile primitives for speedy kernels

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

NVIDIA Nemotron Parse 1.1

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

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Analytical Performance Estimation during Code Generation on Modern GPUs

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Efficient Execution of OpenMP on GPUs

Performance Comparison of Different OpenCL Implementations of LBM Simulation on Commodity Computer Hardware

Explicit caching HYB: a new high-performance SpMV framework on GPGPU

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

Optimizing Performance and Energy Efficiency in Massively Parallel Systems

Performance Models for Heterogeneous Iterative Programs

Persistent Kernels for Iterative Memory-bound GPU Applications

Recent source codes

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

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

Most viewed papers (last 30 days)