high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

The performances of R GPU implementations of the GMRES method

Bogdan Oancea, Richard Pospisil

View

Download (PDF)

Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 840 M, OpenCL, Performance, R

February 17, 2018 by hgpu

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Philippe Tillet, David Cox

View

Download (PDF)

Source codes

Tags: Auto-Tuning, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980 Ti, OpenCL, Package, Performance, PTX, Tesla P100

February 17, 2018 by hgpu

Transforming and Optimizing Irregular Applications for Parallel Architectures

Jing Zhang

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Performance, Tesla K20, Thesis

February 17, 2018 by hgpu

Improving Locality of Unstructured Mesh Algorithms on GPUs

Andras Attila Sulyok, Gabor Daniel Balogh, Istvan Zoltan Reguly, Gihan R. Mudalige

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Finite element method, Mathematical Software, nVidia, Performance, Tesla P100

February 15, 2018 by hgpu

Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, Package, Performance

February 9, 2018 by hgpu

A Survey on Compiler Autotuning using Machine Learning

Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, Cristina Silvano

View

Download (PDF)

Tags: Auto-Tuning, Compilers, Computer science, Machine learning, Performance

January 28, 2018 by hgpu

Improving Communication Performance in GPU-Accelerated HPC Clusters

Iman Faraji

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, GPU cluster, MPI, nVidia, Package, Performance, Thesis

January 28, 2018 by hgpu

Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Hongyang Sun, Redouane Elghazi, Ana Gainaru, Guillaume Aupy, Padma Raghavan

View

Download (PDF)

Tags: Computer science, Intel Xeon Phi, Performance, Task scheduling

January 20, 2018 by hgpu

Comprehensive Optimization of Parametric Kernels for Graphics Processing Units

Xiaohui Chen, Marc Moreno-Maza, Jeeva Paudel, Ning Xie

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, LLVM, nVidia, Package, Performance, PTX, Symbolic Computation, Tesla M2050

January 20, 2018 by hgpu

Evaluation of Machine Learning Fameworks on Finis Terrae II

Andres Gomez Tato

View

Download (PDF)

Tags: Algorithms, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Performance, Tesla K80

January 20, 2018 by hgpu

Analysing the Performance of GPU Hash Tables for State Space Exploration

Nathan Cassee, Anton Wijs

View

Download (PDF)

Tags: Computer science, CUDA, Design space exploration, Hashing, nVidia, nVidia GeForce GT 750 M, Performance

January 6, 2018 by hgpu

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

Amrita Mathuriya, Thorsten Kurth, Vivek Rane, Mustafa Mustafa, Lei Shao, Debbie Bard, Prabhat, Victor W Lee

View

Download (PDF)

Tags: Benchmarking, Computer science, Deep learning, Intel Xeon Phi, Neural networks, Performance, TensorFlow

January 6, 2018 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

The performances of R GPU implementations of the GMRES method

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Transforming and Optimizing Irregular Applications for Parallel Architectures

Improving Locality of Unstructured Mesh Algorithms on GPUs

Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach

A Survey on Compiler Autotuning using Machine Learning

Improving Communication Performance in GPU-Accelerated HPC Clusters

Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Comprehensive Optimization of Parametric Kernels for Graphics Processing Units

Evaluation of Machine Learning Fameworks on Finis Terrae II

Analysing the Performance of GPU Hash Tables for State Space Exploration

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Most viewed papers (last 30 days)