high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

Fast Polynomial Approximation Acceleration on the GPU

Lumir Janosek, Martin Nemec

View

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9600 GT, nVidia GeForce 9800 GTX, nVidia GeForce GTS 450

February 12, 2012 by hgpu

Face Detection CUDA Accelerating

Jaromir Krpec, Martin Nemec

View

Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia

February 12, 2012 by hgpu

Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA

Sabine McConnell, Robert Sturgeon, Gregory Henry, Andrew Mayne, Richard Hurley

View

Tags: Algorithms, Computer science, CUDA, Data mining, GPU cluster, nVidia, nVidia GeForce GT 220, OpenCL, Self-organizing map, Tesla S1070

February 11, 2012 by hgpu

Customizing Instruction Set Extensible Reconfigurable Processors using GPUs

Unmesh D. Bordoloi, Bharath Suri, Swaroop Nunna, Samarjit Chakraborty, Petru Eles, Zebo Peng

View

Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, OpenCL, Optimization, Tesla M2050

February 10, 2012 by hgpu

Implementing Molecular Dynamics on Hybrid High Performance Computers – Particle-Particle Particle-Mesh

W. Michael Brown, Axel Kohlmeyer, Steven J. Plimpton, Arnold N. Tharrington

View

Tags: Algorithms, Chemistry, CUDA, Electrostatics, GPU cluster, Heterogeneous systems, Molecular dynamics, nVidia, OpenCL, OpenMPI, Package, Physics, Tesla M2070

February 10, 2012 by hgpu

Accelerating H.264 Advanced Video Coding with GPU/CUDA Technology

Christopher Rivere Escobedo

View

Tags: Compression, CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GT 220, Thesis, Video encoding

February 9, 2012 by hgpu

Parallel Semi-Implicit Time Integrators

B. Ong, A. Melfi, A. Christlieb

View

Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Partial differential equations, PDEs, Tesla C1060

February 9, 2012 by hgpu

The Boat Hull Model: Adapting the Roofline Model to Enable Performance Prediction for Parallel Computing

Cedric Nugteren, Henk Corporaal

View

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, Performance

February 9, 2012 by hgpu

CudaRF: A CUDA-based Implementation of Random Forests

Hakan Grahn, Niklas Lavesson, Mikael Hellborg Lapajne, Daniel Slat

View

Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Machine learning, nVidia, nVidia GeForce GT 220

February 9, 2012 by hgpu

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units

Jun Igarashi, Osamu Shouno, Tomoki Fukai, Hiroshi Tsujino

View

Tags: Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 280, Visualization

February 9, 2012 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

Yongpeng Zhang, Frank Mueller

View

Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Nearest neighbour, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 8, 2012 by hgpu

The PEPPHER Approach to Programmability and Performance Portability for Heterogeneous many-core Architectures

Siegfried Benkner, Sabri Pllana, Jesper Larsson Traff, Philippas Tsigas, Andrew Richards, Raymond Namyst, Beverly Bachmayer, Christoph Kessler, David Moloney, Peter Sanders

View

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Performance, Tesla C1060

February 8, 2012 by hgpu

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

INT v.s. FP: A framework to compare low-bit integer and float-point formats

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

See all packages

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: