high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Elastic pipeline: addressing GPU on-chip shared memory bank conflicts

Chunyang Gou, Georgi N. Gaydadjiev

Tags: Computer science, Memory, Memory model, Performance

August 24, 2011 by hgpu

JCudaMP: OpenMP/Java on CUDA

Georg Dotzler, Ronald Veldema, Michael Klemm

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce GTX 280, OpenMP, Programming Languages, Programming techniques

August 23, 2011 by hgpu

Implementing the PGI Accelerator model

Michael Wolfe

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization, Programming Languages, Tesla C1060, Tesla S1070

August 23, 2011 by hgpu

MDR: performance model driven runtime for heterogeneous parallel platforms

Jacques A. Pienaar, Anand Raghunathan, Srimat Chakradhar

Tags: Computer science, Heterogeneous systems, Performance, Programming techniques, Task scheduling

August 23, 2011 by hgpu

Bounding the effect of partition camping in GPU kernels

Ashwin M. Aji, Mayank Daga, Wu-chun Feng

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 280, Performance

August 23, 2011 by hgpu

Ypnos: declarative, parallel structured grid programming

Dominic A. Orchard, Max Bolingbroke, Alan Mycroft

View

Download (PDF)

Tags: Computer science, High-level Languages, Programming Languages

August 23, 2011 by hgpu

Fast parallel surface and solid voxelization on GPUs

Michael Schwarz, Hans-Peter Seidel

View

Download (PDF)

Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480

August 23, 2011 by hgpu

High performance content-based matching using GPUs

Alessandro Margara, Gianpaolo Cugola

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 460, Package, Performance, Programming techniques

August 23, 2011 by hgpu

Workload and network-optimized computing systems

D. P. LaPotin, S. Daijavad, C. L. Johnson, S. W. Hunter, K. Ishizaki, H. Franke, H. D. Achilles, D. P. Dumarot, N. A. Greco, B. Davari

Tags: Computer science, Performance, Programming techniques

August 23, 2011 by hgpu

Efficient implementation of GPGPU synchronization primitives on CPUs

Jayanth Gummaraju, Ben Sander, Laurent Morichetti, Benedict Gaster, Lee Howes

Tags: Computer science, OpenCL, Performance, Programming techniques

August 23, 2011 by hgpu

Performance Modelling and Traffic Characterisation of Optical Networks

Harry Mouchos, Athanasios Tsokanos and Demetres D. Kouvatsos

Tags: Computer science, CUDA, Network communications, Networks, nVidia

August 23, 2011 by hgpu

Auto-tuning 3-D FFT library for CUDA GPUs

Akira Nukada, Satoshi Matsuoka

Tags: Algorithms, Computer science, CUDA, FFT, Mathematical Software, nVidia, Performance

August 22, 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

Elastic pipeline: addressing GPU on-chip shared memory bank conflicts

JCudaMP: OpenMP/Java on CUDA

Implementing the PGI Accelerator model

MDR: performance model driven runtime for heterogeneous parallel platforms

Bounding the effect of partition camping in GPU kernels

Ypnos: declarative, parallel structured grid programming

Fast parallel surface and solid voxelization on GPUs

High performance content-based matching using GPUs

Workload and network-optimized computing systems

Efficient implementation of GPGPU synchronization primitives on CPUs

Performance Modelling and Traffic Characterisation of Optical Networks

Auto-tuning 3-D FFT library for CUDA GPUs

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)