high performance computing on graphics processing units: hgpu.org

hgpu.org » Thesis

Parallelization of the Algorithm WHAM with NVIDIA CUDA

Nicolo' Savioli

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Tags: Algorithms, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GT 320 M, Physics, Thesis

February 2, 2014 by hgpu

Towards Efficient Risk Quantification-Using GPUs and Variance Reduction Technique

Shih Hau Tan

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Tags: CUDA, Finance, nVidia, nVidia GeForce GTX 680, Thesis

January 30, 2014 by hgpu

A Novel Graphical Processing Unit Method for Power Systems Security Analysis

Laurie Elizabeth Miller

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, Security, Sparse matrix, Thesis

January 30, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

Impact of communication times on mixed CPU/GPU applications scheduling using KAAPI

David Beniamine

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Tags: Computer science, CUDA, nVidia, nVidia Quadro NVS 300, Task scheduling, Tesla C2050, Thesis

January 29, 2014 by hgpu

Scheduling on Manycore and Heterogeneous Graphics Processors

Stanley Tzeng

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Thesis

January 28, 2014 by hgpu

Automatic Resource-Constrained Static Task Parallelization

Dounia Khaldi

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Tags: Algorithms, Code generation, Computer science, MPI, OpenCL, Task scheduling, Thesis

January 28, 2014 by hgpu

Performance-Correctness Challenges in Emerging Heterogeneous Multicore Processors

Vineeth Thamarassery Mekkat

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, nVidia GeForce 8600 GTS, Thesis

January 28, 2014 by hgpu

Autotuning Programs with Algorithmic Choice

Jason Ansel

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Source codes

Tags: Algorithms, ATI, ATI Radeon HD 6630 M, Computer science, nVidia, OpenCL, Package, Performance, Tesla C2070, Thesis

January 28, 2014 by hgpu

Low-latency Image Recognition with GPU-accelerated Convolutional Networks for Web-based Services

Fu Jie Huang

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Tags: Cloud, CUDA, Image processing, Image recognition, nVidia, nVidia GeForce GTX 560 Ti, Thesis

January 26, 2014 by hgpu

GPU Monte Carlo scatter calculations for Cone Beam Computed Tomography

Jonas Adler

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Tags: Computed tomography, CT, CUDA, Filtering, Medicine, nVidia, nVidia Quadro 4000, Thesis, Tomography

January 26, 2014 by hgpu

Performance Evaluation of the Intel Many Integrated Core Architecture for 3D Image Reconstruction in Computed Tomography

Johannes Hofmann

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Tags: Algorithms, Computed tomography, CT, CUDA, Image processing, Image reconstruction, Intel Xeon Phi, Medicine, nVidia, nVidia GeForce GTX 680, Thesis, Tomography

January 25, 2014 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

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

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

Parallelization of the Algorithm WHAM with NVIDIA CUDA

Towards Efficient Risk Quantification-Using GPUs and Variance Reduction Technique

A Novel Graphical Processing Unit Method for Power Systems Security Analysis

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Impact of communication times on mixed CPU/GPU applications scheduling using KAAPI

Scheduling on Manycore and Heterogeneous Graphics Processors

Automatic Resource-Constrained Static Task Parallelization

Performance-Correctness Challenges in Emerging Heterogeneous Multicore Processors

Autotuning Programs with Algorithmic Choice

GPU Monte Carlo scatter calculations for Cone Beam Computed Tomography

Performance Evaluation of the Intel Many Integrated Core Architecture for 3D Image Reconstruction in Computed Tomography

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

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

Most viewed papers (last 30 days)