high performance computing on graphics processing units: hgpu.org

hgpu.org » Optimization

A Fast and Efficient Simulation Framework for Modeling Heat Transport

Joshua Gordon Clark

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Tags: CUDA, nVidia, nVidia GeForce GT 330 M, Optimization, Physics, Raytracing, Thesis, Visualization

February 29, 2012 by hgpu

An improved implementation of Preconditioned Conjugate Gradient Method on GPU

Yechen Gui, Guijuan Zhang

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Tags: Algorithms, CUDA, Laplace and Poisson equation, nVidia, Optimization, Physics, Poisson equation, Sparse matrix

February 27, 2012 by hgpu

Reuse and Refactoring of GPU Kernels to Design Complex Applications

Santonu Sarkar, Sayantan Mitra, Ashok Srinivasan

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Tags: Computer science, CUDA, FFT, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Optimization, Software Engineering, Tesla S1050

February 24, 2012 by hgpu

A Code Optimization Framework for Performance Portability of GPU Kernels onto Custom Accelerators

Alexandros Papakonstantinou, Deming Chen, Wen-mei Hwu

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Optimization

February 20, 2012 by hgpu

Performance Portability with the Chapel Language

Albert Sidelnik, Saeed Maleki, Bradford L. Chamberlain, Maria J. Garzaran, David Padua

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 280, Optimization

February 18, 2012 by hgpu

Skeleton-based Automatic Parallelization of Image Processing Algorithms for GPUs

Cedric Nugteren, Henk Corporaal, Bart Mesman

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Tags: Algorithms, Code generation, CUDA, Image processing, nVidia, nVidia GeForce GTX 470, Optimization

February 17, 2012 by hgpu

Comparing Many-Core Accelerator Frameworks

G. Haase, A. Kucher

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Optimization

February 15, 2012 by hgpu

Accurate real-time stereo correspondence using intra- and inter-scanline optimization

Li Yao, Dong-xiao Li, Jing Zhang, Liang-hao Wang, Ming Zhang

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia Quadro FX 4800, Optimization, Stereo vision

February 15, 2012 by hgpu

A Scalable GPU-based Approach to Accelerate the Multiple-Choice Knapsack Problem

Bharath Suri, Unmesh D. Bordoloi, Petru Eles

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Tags: Computer science, CUDA, nVidia, Optimization, Tesla M2050

February 13, 2012 by hgpu

FPGA accelerated 3D reconstruction using compressive sensing

Jianwen Chen, Jason Cong, Ming Yan, Yi Zou

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Tags: Algorithms, Computer science, CUDA, FPGA, Medicine, nVidia, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tomography

February 12, 2012 by hgpu

SPIRE, a Sequential to Parallel Intermediate Representation Extension

Dounia Khaldi, Pierre Jouvelot, Corinne Ancourt, Francois Irigoin

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Tags: Compilers, Computer science, OpenCL, Optimization

February 11, 2012 by hgpu

Automatic Performance Optimization in ViennaCL for GPUs

Karl Rupp, Josef Weinbub, Florian Rudolf

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Source codes

Tags: Algorithms, ATI, ATI Radeon HD 5850, Computer science, Linear Algebra, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 470, OpenCL, Optimization, Package, Performance, Programming techniques

February 10, 2012 by hgpu

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

A Fast and Efficient Simulation Framework for Modeling Heat Transport

An improved implementation of Preconditioned Conjugate Gradient Method on GPU

Reuse and Refactoring of GPU Kernels to Design Complex Applications

A Code Optimization Framework for Performance Portability of GPU Kernels onto Custom Accelerators

Performance Portability with the Chapel Language

Skeleton-based Automatic Parallelization of Image Processing Algorithms for GPUs

Comparing Many-Core Accelerator Frameworks

Accurate real-time stereo correspondence using intra- and inter-scanline optimization

A Scalable GPU-based Approach to Accelerate the Multiple-Choice Knapsack Problem

FPGA accelerated 3D reconstruction using compressive sensing

SPIRE, a Sequential to Parallel Intermediate Representation Extension

Automatic Performance Optimization in ViennaCL for GPUs

Recent source codes

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

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

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