high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce RTX 3080 Ti

An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark

Jovan Ðukic, Marko Mišic

View

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce RTX 3080 Ti, OpenACC, OpenCL, OpenMP

November 12, 2023 by hgpu

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Aurélien François Gilbert Bloch

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, CUDA, Design space exploration, DSP, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3080 Ti, Package, Thesis

June 25, 2023 by hgpu

Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

JooYoung Park, DoangJoo Synn, XinYu Piao, Jong-Kook Kim

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 3080 Ti, Performance, Prefetch

October 16, 2022 by hgpu

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

Farui Wang, Weizhe Zhang, Shichao Lai, Meng Hao, Zheng Wang

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, nVidia GeForce RTX 3080 Ti, Package

January 9, 2022 by hgpu

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

View

Download (PDF)

Source codes

Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

An approach to performance portability through generic programming

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Kernel Tuner

GT4Py: GridTools for Python

miniBUDE: A BUDE virtual-screening benchmark, in many programming models

gossip: Efficient Communication Primitives for Multi-GPU Systems

AUCMEDI: a framework for Automated Classification of Medical Images

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

Most viewed papers (last 30 days)