CUDA | Page 186 | hgpu.org

hgpu.org » CUDA

Analysis of GPU-based convolution for acoustic wave propagation modeling with finite differences: Fortran to CUDA-C step-by-step

Makoto Sadahiro

View

Download (PDF)

Tags: Acoustics, CUDA, Finite difference, Fortran, nVidia, Physics, Tesla K20, Thesis

September 13, 2014 by hgpu

Performance and Power Optimization of GPU Architectures for General-purpose Computing

Yue Wang

View

Download (PDF)

Tags: Computer science, CUDA, GPGPU-sim, Heterogeneous systems, nVidia, Thesis

September 13, 2014 by hgpu

Characterization and Analysis of Dynamic Parallelism in Unstructured GPU Applications

Jin Wang, Sudhakar Yalamanchili

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX Titan, Tesla K20, Tesla K40

September 11, 2014 by hgpu

Parallelized Seeded Region Growing using CUDA

Seongjin Park, Jeongjin Lee, Hyunna Lee, Juneseuk Shin, Jinwook Seo, Kyoung Ho Lee, Yeong-Gil Shin, Bohyoung Kim

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, HLSL, Medicine, nVidia, nVidia GeForce GTX 285

September 11, 2014 by hgpu

Enhancing R with Advanced Compilation Tools and Methods

Duncan Temple Lang

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Mathematical Software, nVidia, Programming Languages

September 11, 2014 by hgpu

Parallel Multi-dimensional Range Query Processing with R-Trees on GPU

Jinwoong Kim, Beomseok Nam

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Databases, Indexing, nVidia, Tesla M2090

September 9, 2014 by hgpu

Improvements to Physically Based Cloth Simulation

Tsz Ho Wong

View

Download (PDF)

Tags: 3D Graphics and Realism, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 780, Review, Thesis

September 9, 2014 by hgpu

A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen

View

Download (PDF)

Tags: Algorithms, Biology, CUDA, Genetics, Machine learning, Neuroscience, nVidia, nVidia GeForce GTX Titan, Statistics

September 9, 2014 by hgpu

Professional CUDA C Programming

John Cheng, Max Grossman, Ty McKercher

View

Download (PDF)

Tags: Algorithms, Book, Computer science, CUDA, Memory model, nVidia, Review

September 8, 2014 by hgpu

Document Classification Using KNN on GPU

S.G. Lade, Nikhil Vyawahare

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 520 M, Text mining

September 8, 2014 by hgpu

Sparse array representations and some selected array operations on GPUs

Hairong Wang

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 670, Sparse matrix, Thesis

September 8, 2014 by hgpu

A CUDA Back-End for the Equelle Compiler

Havard Heitlo Holm

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, CUDA, Differential equations, Finite volume method, Heterogeneous systems, nVidia, nVidia Quadro NVS 5200 M, Package, Partial differential equations, PDEs, Tesla K40, Thesis

September 8, 2014 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Analysis of GPU-based convolution for acoustic wave propagation modeling with finite differences: Fortran to CUDA-C step-by-step

Performance and Power Optimization of GPU Architectures for General-purpose Computing

Characterization and Analysis of Dynamic Parallelism in Unstructured GPU Applications

Parallelized Seeded Region Growing using CUDA

Enhancing R with Advanced Compilation Tools and Methods

Parallel Multi-dimensional Range Query Processing with R-Trees on GPU

Improvements to Physically Based Cloth Simulation

A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

Professional CUDA C Programming

Document Classification Using KNN on GPU

Sparse array representations and some selected array operations on GPUs

A CUDA Back-End for the Equelle Compiler

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

Most viewed papers (last 30 days)