high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Alexander Monakov, Anton Lokhmotov, Arutyun Avetisyan

Tags: Computer science, Partial differential equations, PDEs, Sparse matrix

April 21, 2011 by hgpu

Assessment of GPU computational enhancement to a 2D flood model

Alfred J. Kalyanapu, Siddharth Shankar, Eric R. Pardyjak, David R. Judi, Steven J. Burian

Tags: CUDA, Earth and Space Sciences, Fluid simulation, nVidia

April 21, 2011 by hgpu

Solving knapsack problems on GPU

V. Boyer, D. El Baz, M. Elkihel

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Optimization

April 21, 2011 by hgpu

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 9500 GTX, nVidia GeForce GTX 295, Sparse matrix

April 21, 2011 by hgpu

A performance prediction model for the CUDA GPGPU platform

Kishore Kothapalli, Rishabh Mukherjee, M. Suhail Rehman, Suryakant Patidar, P. J. Narayanan, Kannan Srinathan

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Performance

April 21, 2011 by hgpu

Fine-sorting One-dimensional Particle-In-Cell Algorithm with Monte-Carlo Collisions on a Graphics Processing Unit

Philipp Mertmann, Denis Eremin, Thomas Mussenbrock, Ralf Peter Brinkmann, Peter Awakowicz

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 480, Particle-in-cell methods, Physics, Plasma physics, Sorting

April 21, 2011 by hgpu

Exploring scalability of FIR filter realizations on Graphics Processing Units

J. Rebacz, E. Oruklu, J. Saniie

Tags: CUDA, Filtering, nVidia, Signal processing

April 20, 2011 by hgpu

Stream processing of moment invariants for real-time classifiers

C.H. Messom, A.L.C. Barczak

Tags: Algorithms, Computer science

April 20, 2011 by hgpu

Improving the performance of PIR Protocol in Outsourced Databases

S.B. Mane, P.K. Sinha

Tags: AES, Computer science, Security

April 20, 2011 by hgpu

A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis

T. Nagaoka, S. Watanabe

Tags: CUDA, Electrodynamics, FDTD, Finite-difference time-domain, Medicine, nVidia, Tesla C1060

April 20, 2011 by hgpu

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Wang Lei, Zhang Yunquan, Zhang Xianyi, Liu Fangfang

Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia GeForce GTX 295, Tesla C1060

April 20, 2011 by hgpu

Discrete-event Execution Alternatives on General Purpose Graphical Processing Units (GPGPUs)

Kalyan S. Perumalla

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Tags: Brook, Computer science, Diffusion equation, DirectX, Hardware Architecture, nVidia, nVidia GeForce 6800

April 20, 2011 by hgpu

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

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high performance computing on graphics processing units: hgpu.org

Applications

Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Assessment of GPU computational enhancement to a 2D flood model

Solving knapsack problems on GPU

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

A performance prediction model for the CUDA GPGPU platform

Fine-sorting One-dimensional Particle-In-Cell Algorithm with Monte-Carlo Collisions on a Graphics Processing Unit

Exploring scalability of FIR filter realizations on Graphics Processing Units

Stream processing of moment invariants for real-time classifiers

Improving the performance of PIR Protocol in Outsourced Databases

A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Discrete-event Execution Alternatives on General Purpose Graphical Processing Units (GPGPUs)

Recent source codes

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Most viewed papers (last 30 days)