high performance computing on graphics processing units: hgpu.org

hgpu.org » performance portability

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Tuowen Zhao, Samuel Williams, Mary Hall, Hans Johansen

View

Download (PDF)

Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, performance portability, Stencil computation, Tesla P100

December 16, 2018 by hgpu

Performance Portability Challenges for Fortran Applications

Abigail Hsu, David Neill Asanza, Joseph A. Schoonover, Zach Jibben, Neil N. Carlson, Robert Robey

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Finite difference, Fortran, nVidia, OpenMP, Package, performance portability, Tesla P100, Tesla V100

December 2, 2018 by hgpu

OpenCL Performance Prediction using Architecture-Independent Features

Beau Johnston, Greg Falzon, Josh Milthorpe

View

Download (PDF)

Source codes

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

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX Titan X, Optimization, Package, performance portability

October 13, 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

View

Download (PDF)

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

September 23, 2018 by hgpu

EngineCL: Usability and Performance in Heterogeneous Computing

Raul Nozal, Jose Luis Bosque, Ramon Beivide

View

Download (PDF)

Tags: AMD Radeon R7, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, performance portability, Tesla K20

May 12, 2018 by hgpu

HPVM: Heterogeneous Parallel Virtual Machine

Maria Kotsifakou, Prakalp Srivastava, Matthew D. Sinclair, Rakesh Komuravelli, Vikram Adve, Sarita Adve

View

Download (PDF)

Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability, Virtualization

March 17, 2018 by hgpu

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, performance portability, TensorFlow, Tesla K80

February 15, 2018 by hgpu

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

Thomas L. Falch, Anne C. Elster

View

Download (PDF)

Source codes

Tags: ATI, ATI Radeon HD 7970, Compilers, Heterogeneous systems, Image processing, Machine learning, nVidia, nVidia GeForce GTX 960, OpenCL, Package, performance portability, Tesla K40

January 13, 2018 by hgpu

High Performance Stencil Code Generation with Lift

Bastian Hagedorn, Larisa Stoltzfus, Michel Steuwer, Sergei Gorlatch, Christophe Dubach

View

Download (PDF)

Source codes

Tags: ARM, ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Linear Algebra, nVidia, OpenCL, Package, performance portability, Scala, Tesla K20

January 13, 2018 by hgpu

Strategy Preserving Compilation for Parallel Functional Code

Robert Atkey, Michel Steuwer, Sam Lindley, Christophe Dubach

View

Download (PDF)

Tags: ATI, ATI Radeon 7970, Code generation, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, performance portability

October 29, 2017 by hgpu

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

Li-Wen Chang

View

Download (PDF)

Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, OpenCL, performance portability, Tesla C2050, Tesla K20, Thesis

October 15, 2017 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

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Performance Portability Challenges for Fortran Applications

OpenCL Performance Prediction using Architecture-Independent Features

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

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

EngineCL: Usability and Performance in Heterogeneous Computing

HPVM: Heterogeneous Parallel Virtual Machine

TVM: End-to-End Optimization Stack for Deep Learning

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

High Performance Stencil Code Generation with Lift

Strategy Preserving Compilation for Parallel Functional Code

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

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)