CUDA | Page 181 | hgpu.org

Programming

A Fast Poisson Solver with Periodic Boundary Conditions for GPU Clusters in Various Configurations

Dale Nicholas Rattermann

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Tags: CUDA, FFT, Fluid dynamics, GPU cluster, Laplace and Poisson equation, nVidia, Poisson equation, Tesla M2050, Tesla M2070, Thesis

November 3, 2014 by hgpu

Bounds on the Energy Consumption of Computational Kernels

Andrew Gearhart

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Source codes

Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, Package, Tesla K20, Thesis

November 3, 2014 by hgpu

A GPU-based Framework for Real-time Free Viewpoint Television

Kyrylo Shegeda

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Tags: Computer science, CUDA, GLSL, nVidia, nVidia Quadro FX 1700, nVidia Quadro FX 5800, Rendering, Thesis

November 3, 2014 by hgpu

Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs

Wangdong Yang, Kenli Li, Zeyao Mo, Keqin Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Performance, Probability, Sparse matrix

November 3, 2014 by hgpu

Hierarchical Mapping Techniques for Signal Processing Systems on Parallel Platforms

Lai-Huei Wang

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Tags: Computer vision, CUDA, Design space exploration, DSP, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 680, Signal processing, Thesis

October 31, 2014 by hgpu

Sparse Recovery on GPUs: Accelerating the Iterative Soft-Thresholding Algorithm

Achal Shah, Angshul Majumdar

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Tags: Algorithms, CUDA, Machine learning, nVidia, nVidia GeForce GTX 680, Signal processing

October 29, 2014 by hgpu

Efficient Particle-Mesh Spreading on GPUs

Xiangyu Guo, Xing Liu, Peng Xu, Zhihui Du, Edmond Chow

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Tesla K40

October 29, 2014 by hgpu

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Lokendra Singh Panwar

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Computational Complexity, Computer science, CUDA, Finite difference, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenCL, Seismology, Tesla C1060, Tesla C2050, Thesis

October 29, 2014 by hgpu

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K10, Tesla K20

October 29, 2014 by hgpu

Testing and Exposing Weak Graphics Processing Unit Memory Models

Tyler Rey Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, PTX, Tesla C2075, Thesis

October 27, 2014 by hgpu

Parallel Finite Volume Algorithm on Graphic Processing Units (GPU)

Amirsaman Farrokhpanah, Hanif Montazeri, Javad Mostaghimi

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, Tesla K20

October 27, 2014 by hgpu

Bayesian Neural Networks in Data-Intensive High Energy Physics Applications

Michelle Perry

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Tags: Bayesian, CUDA, High Energy Physics - Phenomenology, Neural networks, nVidia, nVidia GeForce GT 320 M, Physics, Tesla M2050

October 27, 2014 by hgpu

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

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

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high performance computing on graphics processing units: hgpu.org

Programming

A Fast Poisson Solver with Periodic Boundary Conditions for GPU Clusters in Various Configurations

Bounds on the Energy Consumption of Computational Kernels

A GPU-based Framework for Real-time Free Viewpoint Television

Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs

Hierarchical Mapping Techniques for Signal Processing Systems on Parallel Platforms

Sparse Recovery on GPUs: Accelerating the Iterative Soft-Thresholding Algorithm

Efficient Particle-Mesh Spreading on GPUs

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Testing and Exposing Weak Graphics Processing Unit Memory Models

Parallel Finite Volume Algorithm on Graphic Processing Units (GPU)

Bayesian Neural Networks in Data-Intensive High Energy Physics Applications

Recent source codes

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

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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