high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

An OpenCL 3D FFT for Molecular Dynamics Simulations on Multiple FPGAs

Lawrence C. Stewart, Carlo Pasoe, Brian W. Sherman, Martin Herbordt, Vipin Sachdeva

View

Download (PDF)

Tags: Computer science, Electrostatics, FFT, FPGA, Molecular dynamics, OpenCL

October 4, 2020 by hgpu

LoopBench: An Evaluation of Loop Acceleration in Heterogeneous Systems

Saman Biookaghazadeh, Fengbo Ren, Ming Zhao

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, OpenCL, Package

October 4, 2020 by hgpu

Flexible Performant GEMM Kernels on GPUs

Thomas Faingnaert, Tim Besard, Bjorn De Sutter

View

Download (PDF)

Source codes

Tags: Computer science, CUBLAS, CUDA, Julia, Machine learning, Mathematical Software, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2080 Ti, Package, Performance

October 4, 2020 by hgpu

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Orestis Zachariadis, Nitin Satpute, Juan Gómez-Luna, Joaquín Olivares

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2070, nVidia Titan RTX, Package, Performance, Sparse matrix

October 4, 2020 by hgpu

Extending High-Level Synthesis for Task-Parallel Programs

Yuze Chi, Licheng Guo, Young-kyu Choi, Jie Wang, Jason Cong

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, FPGA, HLS, OpenCL, Package

September 27, 2020 by hgpu

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Mawussi Zounon, Nicholas J. Higham, Craig Lucas, Françoise Tisseur

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Factorization, Mixed precision, nVidia, Tesla P100, Tesla V100

September 27, 2020 by hgpu

Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments

Davide Russo

View

Download (PDF)

Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Thesis

September 27, 2020 by hgpu

Applications of Deep Neural Networks

Jeff Heaton

View

Download (PDF)

Source codes

Tags: Book, Computer science, Computer vision, Deep learning, Neural networks, NLP, Package, Python, Security

September 20, 2020 by hgpu

Designing Efficient Barriers and Semaphores for Graphics Processing Units

Rohan Mahapatra

View

Download (PDF)

Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, Thesis

September 20, 2020 by hgpu

WarpCore: A Library for fast Hash Tables on GPUs

Daniel Jünger, Robin Kobus, André Müller, Christian Hundt, Kai Xu, Weiguo Liu, Bertil Schmidt

View

Download (PDF)

Tags: Bioinformatics, Computer science, CUDA, Hashing, nVidia, nVidia Quadro GV100, Tesla V100

September 20, 2020 by hgpu

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Anirban Ghose, Siddharth Singh, Vivek Kulaharia, Lokesh Dokara, Srijeeta Maity, Soumyajit Dey

View

Download (PDF)

Source codes

Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 970, OpenCL, Package

September 20, 2020 by hgpu

Tools for GPU Computing–Debugging and Performance Analysis of Heterogenous HPC Applications

Michael Knobloch, Bernd Mohr

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, OpenCL, Performance, survey

September 13, 2020 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

An OpenCL 3D FFT for Molecular Dynamics Simulations on Multiple FPGAs

LoopBench: An Evaluation of Loop Acceleration in Heterogeneous Systems

Flexible Performant GEMM Kernels on GPUs

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Extending High-Level Synthesis for Task-Parallel Programs

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments

Applications of Deep Neural Networks

Designing Efficient Barriers and Semaphores for Graphics Processing Units

WarpCore: A Library for fast Hash Tables on GPUs

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Tools for GPU Computing–Debugging and Performance Analysis of Heterogenous HPC Applications

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Most viewed papers (last 30 days)