high performance computing on graphics processing units: hgpu.org

Theses

hgpu.org » Applications

Auto-tuning Shallow water simulations on GPUs

Andre B. Amundsen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro 1800 M, Rendering, Thesis

February 19, 2015 by hgpu

Quadratic Pseudo-Boolean Optimization for Scene Analysis using CUDA

Andreas Bauer

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Tags: Algorithms, Computer science, Computer vision, CUDA, Image reconstruction, nVidia, nVidia GeForce GTX 450 Ti, Thesis

February 13, 2015 by hgpu

FSCL: Homogeneous programming, scheduling and execution on heterogeneous platforms

Gabriele Cocco

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Tags: ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, OpenCL, Package, Thesis

February 10, 2015 by hgpu

Analysis and Modeling of the Timing Behavior of GPU Architectures

Petros Voudouris

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, Thesis

February 10, 2015 by hgpu

Sparse Matrix-Vector Multiplication on GPU

Arash Ashari

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan, Sparse matrix, Tesla K10, Thesis

February 9, 2015 by hgpu

Extending the Gotran framework: LATEX and GPU acceleration

George Bahijevitch Bahij

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Tags: Computational modelling, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX Titan, ODEs, Ordinary differential equations, Python, Thesis

February 6, 2015 by hgpu

Nucleation Studies on Graphics Processing Units

Benjamin J. Block

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Tags: Anisotropy, ATI, ATI Radeon HD 6970, Biology, Chemistry, Differential equations, Ising model, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Physics, Tesla K20, Thesis

February 6, 2015 by hgpu

Pointer Analysis for Semi-Automatic Code Parallelizers

Ketan Sudhakar Vyavahare

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Tags: Code generation, Computer science, CUDA, OpenCL, Thesis

February 3, 2015 by hgpu

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

A Geetha Venkatesh

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Tags: Computer science, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Tesla C2070, Thesis

February 3, 2015 by hgpu

Performance Analysis and Optimization of Hermite Methods on NVIDIA GPUs Using CUDA

Evan T. Dye

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Tags: Algorithms, Computer science, CUDA, Differential equations, Mathematics, nVidia, nVidia GeForce GTX 560 Ti, Partial differential equations, PDEs, Thesis

February 2, 2015 by hgpu

Productive and Efficient Computational Science Through Domain-specific Abstractions

Florian Rathgeber

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Tags: Computer science, CUDA, Differential equations, FEM, Finite element method, nVidia, OpenCL, Partial differential equations, PDEs, Thesis

February 1, 2015 by hgpu

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

Edgar Isaac Hiroshi Leon Saiki

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Tags: Algorithms, Computer science, CUDA, Data mining, MapReduce, nVidia, nVidia GeForce GTX Titan, Tesla K20, Thesis

February 1, 2015 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

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

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high performance computing on graphics processing units: hgpu.org

Theses

Auto-tuning Shallow water simulations on GPUs

Quadratic Pseudo-Boolean Optimization for Scene Analysis using CUDA

FSCL: Homogeneous programming, scheduling and execution on heterogeneous platforms

Analysis and Modeling of the Timing Behavior of GPU Architectures

Sparse Matrix-Vector Multiplication on GPU

Extending the Gotran framework: LATEX and GPU acceleration

Nucleation Studies on Graphics Processing Units

Pointer Analysis for Semi-Automatic Code Parallelizers

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

Performance Analysis and Optimization of Hermite Methods on NVIDIA GPUs Using CUDA

Productive and Efficient Computational Science Through Domain-specific Abstractions

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

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

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

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