high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia A100, nVidia DGX-A100, Package, Tesla V100

July 11, 2021 by hgpu

Dynamic Adaptation Techniques and Opportunities to Improve HPC Runtimes

Mohammad Alaul Haque Monil

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

July 11, 2021 by hgpu

Opening the Black Box: Performance Estimation during Code Generation for GPUs

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

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, Performance, Stencil computation, Tesla V100

July 11, 2021 by hgpu

A Sorting Library for FPGA Implementation in OpenCL Programming

Ryohei Kobayashi, Kento Miura, Norihisa Fujita, Taisuke Boku, Toshiyuki Amagasa

View

Download (PDF)

Tags: Algorithms, Computer science, FPGA, nVidia, OpenCL, Sorting, Tesla V100

July 4, 2021 by hgpu

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Devi Abhiseshu Adurti, Mohit Battu

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 1050 Ti, Thesis

July 4, 2021 by hgpu

Sigmoid: An auto-tuned load balancing algorithm for heterogeneous systems

Borja Pérez, E. Stafford, J.L. Bosque, R. Beivide

View

Download (PDF)

Tags: Algorithms, Computer science, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX Titan Black, OpenCL, Tesla K20

June 27, 2021 by hgpu

Improving Performance and Energy Efficiency of Heterogeneous Systems with rCUDA

Javier Prades Gasulla

View

Download (PDF)

Tags: Cloud, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Tesla K40, Tesla P100, Thesis, Virtualization

June 27, 2021 by hgpu

Efficient heterogeneous matrix profile on a CPU + High Performance FPGA with integrated HBM

Jose Carlos Romero, Angeles Navarro, Antonio Vilches, Andrés Rodríguez, Francisco Corbera, Rafael Asenjo

View

Download (PDF)

Tags: Algorithms, Analytical model, Computer science, FPGA, Heterogeneous systems, OpenCL

June 27, 2021 by hgpu

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Boyuan Feng, Yuke Wang, Tong Geng, Ang Li, Yufei Ding

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 3090, Package, Precision, Python, Tesla A100

June 27, 2021 by hgpu

Lettuce: PyTorch-based Lattice Boltzmann Framework

Mario Christopher Bedrunka, Dominik Wilde, Martin Kliemank, Dirk Reith, Holger Foysi, Andreas Krämer

View

Download (PDF)

Source codes

Tags: Algorithms, Computational Physics, CUDA, Deep learning, Fluid dynamics, Lattice Boltzmann model, Machine learning, nVidia, nVidia GeForce RTX 2070, Package, Physics, Tesla V100

June 27, 2021 by hgpu

GPUAPI: Multi-level Chapel Runtime API for GPUs

Akihiro Hayashi, Sri Raj Paul, Vivek Sarkar

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, HIP, nVidia, OpenCL, Package, SYCL, Tesla V100

June 20, 2021 by hgpu

Study and evaluation of improved automatic GPU offloading method

Yoji Yamato

View

Download (PDF)

Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Quadro P 4000, OpenACC, OpenCL

June 20, 2021 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

Dynamic Adaptation Techniques and Opportunities to Improve HPC Runtimes

Opening the Black Box: Performance Estimation during Code Generation for GPUs

A Sorting Library for FPGA Implementation in OpenCL Programming

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Sigmoid: An auto-tuned load balancing algorithm for heterogeneous systems

Efficient heterogeneous matrix profile on a CPU + High Performance FPGA with integrated HBM

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Lettuce: PyTorch-based Lattice Boltzmann Framework

GPUAPI: Multi-level Chapel Runtime API for GPUs

Study and evaluation of improved automatic GPU offloading method

Recent source codes

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Most viewed papers (last 30 days)