Computational Complexity

hgpu.org » Computational Complexity

A Review of the Parallelization Strategies for Iterative Algorithms

Xingxing Zhou, Ming Ling, Shidi Tang, YanXiang Zhu

View

Download (PDF)

Tags: Algorithms, Computational Complexity, Computer science, CUDA, Data parallelism, OpenCL

December 3, 2023 by hgpu

An OpenCL-Based FPGA Accelerator for Faster R-CNN

Jianjing An, Dezheng Zhang, Ke Xu, Dong Wang

View

Download (PDF)

Source codes

Tags: Computational Complexity, Computer science, Deep learning, Design space exploration, FPGA, Neural networks, nVidia, OpenCL, Package, RNN, Tesla K40

October 2, 2022 by hgpu

A Framework for Fast and Efficient Neural Network Compression

Hyeji Kim, Muhammad Umar Karim, Chong-Min Kyung

View

Download (PDF)

Tags: ARM, Caffe, Compression, Computational Complexity, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

December 9, 2018 by hgpu

A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Y. Huang, J. Shen, Z. Wang, M. Wen, C. Zhang

View

Download (PDF)

Tags: Computational Complexity, Computer science, Computer vision, Deep learning, FPGA, HLS, Neural networks

June 9, 2018 by hgpu

Improved Lossless Image Compression Model Using Coefficient Based Discrete Wavelet Transform

T. Velumani, S. Sukumaran

View

Download (PDF)

Tags: Compression, Computational Complexity, Discrete Wavelet Transform, Image processing, OpenCL, Probability

February 14, 2017 by hgpu

An N log N Parallel Fast Direct Solver for Kernel Matrices

Chenhan D. Yu, William B. March, George Biros

View

Download (PDF)

Source codes

Tags: Algorithms, Computational Complexity, Computer science, Factorization, Intel Xeon Phi, Machine learning, Package, Performance

January 16, 2017 by hgpu

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

View

Download (PDF)

Tags: 3D Graphics and Realism, CNN, Computational Complexity, CUDA, Deep learning, Fluid dynamics, Fluid simulation, Machine learning, nVidia, nVidia GeForce GTX Titan X

July 16, 2016 by hgpu

A Study of Parallel Sorting Algorithms Using CUDA and OpenMP

Hakan Gokahmetoglu

View

Download (PDF)

Tags: Algorithms, Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M, OpenMP, Sorting, Thesis

December 6, 2015 by hgpu

Contributions to the Efficient Use of General Purpose Coprocessors: Kernel Density Estimation as Case Study

Unai Lopez Novoa

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 6950, Computational Complexity, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 650, OpenCL, Thesis

September 8, 2015 by hgpu

Very Fast Non-Dominated Sorting

Czeslaw Smutnicki, Jaroslaw Rudy, Dominik Zelazny

View

Download (PDF)

Tags: Algorithms, Computational Complexity, Computer science, CUDA, nVidia, Sorting, Tesla K20

February 24, 2015 by hgpu

GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian

Sankha Baran Dutta

View

Download (PDF)

Tags: Ant colony optimization, Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, Thesis, Vehicular traffic

December 22, 2014 by hgpu

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Lokendra Singh Panwar

View

Download (PDF)

Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Computational Complexity, Computer science, CUDA, Finite difference, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenCL, Seismology, Tesla C1060, Tesla C2050, Thesis

October 29, 2014 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A Review of the Parallelization Strategies for Iterative Algorithms

An OpenCL-Based FPGA Accelerator for Faster R-CNN

A Framework for Fast and Efficient Neural Network Compression

A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Improved Lossless Image Compression Model Using Coefficient Based Discrete Wavelet Transform

An N log N Parallel Fast Direct Solver for Kernel Matrices

Accelerating Eulerian Fluid Simulation With Convolutional Networks

A Study of Parallel Sorting Algorithms Using CUDA and OpenMP

Contributions to the Efficient Use of General Purpose Coprocessors: Kernel Density Estimation as Case Study

Very Fast Non-Dominated Sorting

GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Recent source codes

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

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Most viewed papers (last 30 days)