high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

Towards global composition of performance-aware components for GPU-based systems

Usman Dastgeer, Christoph Kessler

View

Download (PDF)

Tags: Computer science, CUDA, Flow analysis, Heterogeneous systems, nVidia, Task scheduling, Tesla C2050

December 20, 2013 by hgpu

Optimizing GPU to GPU Communication on Cray XK7

Jeff M. Larkin

View

Download (PDF)

Tags: Computer science, CUDA, MPI, nVidia, OpenACC, Tesla K20

December 19, 2013 by hgpu

A Data-Driven Model for Anisotropic Heterogeneous Subsurface Scattering

Ying Song, Wencheng Wang

View

Download (PDF)

Tags: 3D Graphics and Realism, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 480

December 19, 2013 by hgpu

A Two-stage Query by Singing/Humming System on GPU

Wei-Tsa Kao, Chung-Che Wang, Kaichun K. Chang, Jyh-Shing Roger Jang, Wenshan Liou

View

Download (PDF)

Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 660 Ti

December 19, 2013 by hgpu

Heterogeneous Programming with Single Operation Multiple Data

Herve Paulino, Eduardo Marques

View

Download (PDF)

Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, Java, nVidia, nVidia GeForce GT 320 M, Tesla C2050

December 19, 2013 by hgpu

Tesla vs. Xeon Phi vs. Radeon A Compiler Writer’s Perspective

Brent Leback, Douglas Miles, Michael Wolfe

View

Download (PDF)

Tags: ATI, Compilers, Computer science, Intel Xeon Phi, nVidia, OpenCL, Performance, Tesla

December 18, 2013 by hgpu

Data Structures for Task-based Priority Scheduling

Martin Wimmer, Daniel Cederman, Francesco Versaci, Jesper Larsson Traff, Philippas Tsigas

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, Intel Xeon Phi, Package, Task scheduling

December 17, 2013 by hgpu

Development methodologies for GPU and cluster of GPUs

Sylvain Contassot-Vivier, Stephane Vialle, Jens Gustedt

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580

December 17, 2013 by hgpu

Parallel Firewalls on General-Purpose Graphics Processing Units

Kamal Chandra Reddy, Ankit Tharwani, Ch.Vamshi Krishna, Lakshminarayanan.V

View

Download (PDF)

Tags: Computer science, CUDA, Databases, Network communications, nVidia, nVidia Quadro FX 1700, Security

December 17, 2013 by hgpu

Ray-Traced Collision Detection: Interpenetration Control and Multi-GPU Performance

Francois Lehericey, Valerie Gouranton, Bruno Arnaldi

View

Download (PDF)

Tags: Algorithms, Collision detection, Computer science, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 660, OpenCL, Raytracing

December 16, 2013 by hgpu

A Cloud Computing Service Architecture of a Parallel Algorithm Oriented to Scientific Computing with CUDA and Monte Carlo

Ji Yimu, Kuang Zizhuo, Pan Qiao Yu, Sun Yanpeng, Kang Jiangbang, Huang Wei

View

Download (PDF)

Tags: Algorithms, Cloud, Computer science, CUDA, Monte Carlo simulation, nVidia

December 16, 2013 by hgpu

A GPU Implementation of Dynamic Programming for the Optimal Polygon Triangulation

Yasuaki Ito, Koji Nakano

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Programming techniques

December 15, 2013 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Towards global composition of performance-aware components for GPU-based systems

Optimizing GPU to GPU Communication on Cray XK7

A Data-Driven Model for Anisotropic Heterogeneous Subsurface Scattering

A Two-stage Query by Singing/Humming System on GPU

Heterogeneous Programming with Single Operation Multiple Data

Tesla vs. Xeon Phi vs. Radeon A Compiler Writer’s Perspective

Data Structures for Task-based Priority Scheduling

Development methodologies for GPU and cluster of GPUs

Parallel Firewalls on General-Purpose Graphics Processing Units

Ray-Traced Collision Detection: Interpenetration Control and Multi-GPU Performance

A GPU Implementation of Dynamic Programming for the Optimal Polygon Triangulation

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

Most viewed papers (last 30 days)