high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » paper

Parallelizing a high-order WENO scheme for complicated flow structures on GPU and MIC

Liang Deng, Fang Wang, Han-Li Bai, Qing-Xin Xu

View

Download (PDF)

Tags: CUDA, Fluid dynamics, Intel Xeon Phi, nVidia, Tesla K20, Tesla M2050

August 18, 2015 by hgpu

Runtime Code Generation and Data Management for Heterogeneous Computing in Java

Juan Jose Fumero, Toomas Remmelg, Michel Steuwer, Christophe Dubach

View

Download (PDF)

Tags: AMD Radeon R9 295X2, ATI, Code generation, Computer science, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX Titan, OpenCL

August 18, 2015 by hgpu

RubiCL, a Library Providing Automatic Parallelisation on CPU and GPU devices

Aaron Cronin

View

Download (PDF)

Tags: Computer science, nVidia, nVidia GeForce GTX 670, OpenCL, Ruby

August 18, 2015 by hgpu

Optimizing OpenCL Local Work Group Size With Machine Learning

Markus Schlafli

View

Download (PDF)

Tags: ATI, ATI Radeon HD 7850, Computer science, Machine learning, OpenCL, Performance, PTX

August 18, 2015 by hgpu

A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels

Saeed Taheri, Apan Qasem, Martin Burtscher

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Performance, Tesla K20

August 14, 2015 by hgpu

MPC: A Massively Parallel Compression Algorithm for Scientific Data

Annie Yang, Hari Mukka, Farbod Hesaaraki, Martin Burtscher

View

Download (PDF)

Tags: Algorithms, Compression, Computer science, CUDA, nVidia, Tesla K40

August 14, 2015 by hgpu

Bufferless NOC Simulation of Large Multicore System on GPU Hardware

Navin Kumar, Aryabartta Sahu

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 690

August 14, 2015 by hgpu

Automatic classification of object code using machine learning

John Clemens

View

Download (PDF)

Tags: Computer science, CUDA, Machine learning, nVidia, Security

August 14, 2015 by hgpu

Processing Markov Logic Networks with GPUs

Carlos Alberto Martinez-Angeles, Ines Dutra, Vitor Santos Costa, Jorge Buenabad-Chavez

View

Download (PDF)

Tags: Computer science, CUDA, Databases, Machine learning, nVidia, Tesla K40

August 14, 2015 by hgpu

An Introduction to High Performance Computing on AWS

David Pellerin, Dougal Ballantyne, Adam Boeglin

View

Download (PDF)

Tags: Cloud, Cluster computing, Computer science, CUDA, OpenCL

August 13, 2015 by hgpu

Perception of Acoustical Spatial Attributes and Impression in Virtually Rendered Sound Field

Yukio Iwaya, Makoto Otani, Takao Tsuchiya, Yasushi Inoguchi

View

Download (PDF)

Tags: Acoustics, Computer science, CUDA, GPU cluster, nVidia, OpenMPI, Tesla M2075

August 13, 2015 by hgpu

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration

Yunjin Chen, Thomas Pock

View

Download (PDF)

Source codes

Tags: Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 780 Ti, Package, Signal denoising

August 13, 2015 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

Applications

Parallelizing a high-order WENO scheme for complicated flow structures on GPU and MIC

Runtime Code Generation and Data Management for Heterogeneous Computing in Java

RubiCL, a Library Providing Automatic Parallelisation on CPU and GPU devices

Optimizing OpenCL Local Work Group Size With Machine Learning

A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels

MPC: A Massively Parallel Compression Algorithm for Scientific Data

Bufferless NOC Simulation of Large Multicore System on GPU Hardware

Automatic classification of object code using machine learning

Processing Markov Logic Networks with GPUs

Perception of Acoustical Spatial Attributes and Impression in Virtually Rendered Sound Field

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration

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)