high performance computing on graphics processing units: hgpu.org

hgpu.org » Programming Languages

Automatic Kernel Generation for Volta Tensor Cores

Somashekaracharya G. Bhaskaracharya, Julien Demouth, Vinod Grover

View

Tags: Compilers, Computer science, CUBLAS, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia Quadro GV100, Performance, Programming Languages, PTX

June 28, 2020 by hgpu

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

Jianbin Fang, Chun Huang, Tao Tang, Zheng Wang

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Programming Languages, Review

May 17, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels, Rastislav Bodik, Vinod Grover

View

Download (PDF)

Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce RTX 2080 Ti, nVidia Quadro GV100, Programming Languages

March 22, 2020 by hgpu

Memory-Efficient Object-Oriented Programming on GPUs

Matthias Springer

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 940 M, nVidia GeForce GTX 980, nVidia GeForce GTX Titan XP, Package, Programming Languages, Thesis

August 25, 2019 by hgpu

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

Ulysse Beaugnon, Basile Clément, Nicolas Tollenaere, Albert Cohen

View

Download (PDF)

Tags: Analytical model, Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro K4000, Programming Languages

April 14, 2019 by hgpu

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek

View

Download (PDF)

Tags: Algorithms, Code generation, Compilers, Computer science, Deep learning, HLS, LLVM, Programming Languages

January 20, 2019 by hgpu

Automatic acceleration of Numpy applications on GPUs and multicore CPUs

Mahesh Ravishankar, Vinod Grover

View

Download (PDF)

Tags: Computer science, nVidia, Programming Languages

January 20, 2019 by hgpu

Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)

Patrick Diehl, Madhavan Seshadri, Thomas Heller, Hartmut Kaiser

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, Package, Programming Languages, Tesla K40, Tesla K80

November 3, 2018 by hgpu

SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs

Matthias Springer

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Programming Languages

September 23, 2018 by hgpu

Neural Code Comprehension: A Learnable Representation of Code Semantics

Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler

View

Download (PDF)

Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, CUDA, Deep learning, LLVM, LSTM, Machine learning, NLP, nVidia, nVidia GeForce GTX 970, OpenCL, Programming Languages, RNN

June 20, 2018 by hgpu

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

View

Download (PDF)

Tags: Compilers, Computer science, DSL, FPGA, Matrix multiplication, nVidia, OpenMPI, Performance, Programming Languages, PTX, Tesla K40

March 3, 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Automatic Kernel Generation for Volta Tensor Cores

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Memory-Efficient Object-Oriented Programming on GPUs

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Automatic acceleration of Numpy applications on GPUs and multicore CPUs

Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)

SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs

Neural Code Comprehension: A Learnable Representation of Code Semantics

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

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

Most viewed papers (last 30 days)