high performance computing on graphics processing units: hgpu.org

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Collision for 75-step SHA-1: Intensive Parallelization with GPU

E.A. Grechnikov, A.V. Adinetz

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Tags: Computer science, nVidia, OpenCL, Package, Security, Tesla M2050, Tesla X2070

December 18, 2011 by hgpu

Leveraging Parallelism with CUDA and OpenCL

S. Park, D. Shires, J. Ross, D. Richie

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Tags: AMD FireStream 9250, ATI, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Programming techniques, Tesla C1060

December 18, 2011 by hgpu

Efficient Computational Methods for Uncertainty Quantification of Large Systems

Gaurav Gaurav

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Tags: Computer science, CUDA, Nonlinear analysis, nVidia, Tesla C1060, Thesis

December 18, 2011 by hgpu

The MOSIX Cluster Operating System for High-Performance Computing on Linux Clusters, Multi-Clusters, GPU Clusters and Clouds

A. Barak, A. Shiloh

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Tags: Cloud, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 480, OpenCL, Operating systems, Package

December 18, 2011 by hgpu

Parallel paradigms in optimal structural design

Van Huyssteen, Salomon Stephanus

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Thesis

December 18, 2011 by hgpu

Massively Parallel Logic Simulation with GPUs

Yuhao Zhu, Bo Wang, Yangdong Deng

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Tags: Algorithms, Computer science, CUDA, Electronic design automation, nVidia, nVidia GeForce GTX 280, Optimization, Performance

December 17, 2011 by hgpu

Extendable pattern-oriented optimization directives

Huimin Cui, Jingling Xue, Lei Wang, Yang Yang, Xiaobing Feng, Dongrui Fan

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Optimization

December 17, 2011 by hgpu

Quartile and Outlier Detection on Heterogeneous Clusters Using Distributed Radix Sort

Kyle L. Spafford, Jeremy S. Meredith, Jeffrey S. Vetter

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Sorting, Tesla M2070

December 17, 2011 by hgpu

Parallel Mining of Neuronal Spike Streams on Graphics Processing Units

Yong Cao, Debprakash Patnaik, Sean Ponce, Jeremy Archuleta, Patrick Butler, Wu-chun Feng, Naren Ramakrishnan

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Tags: Algorithms, Computer science, CUDA, Data mining, Neuroscience, nVidia, nVidia GeForce GTX 280

December 17, 2011 by hgpu

Code Optimization Techniques for Graphics Processing Units

Fernando Magno Quintao Pereira

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Tags: Algorithms, Book, Code generation, Computer science, CUDA, nVidia, nVidia GeForce 9400 M, Optimization, Package, Programming techniques, PTX, Tutorial

December 17, 2011 by hgpu

Customizable Memory Schemes for Data Parallel Accelerators

Chunyang Gou

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques, Thesis

December 17, 2011 by hgpu

A Comparison of Modern GPU and CPU Architectures: And the Common Convergence of Both

Jonathan Palacios, Josh Triska

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Tags: Computer science, Hardware Architecture, nVidia, Overview, Performance

December 17, 2011 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

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

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high performance computing on graphics processing units: hgpu.org

Collision for 75-step SHA-1: Intensive Parallelization with GPU

Leveraging Parallelism with CUDA and OpenCL

Efficient Computational Methods for Uncertainty Quantification of Large Systems

Parallel paradigms in optimal structural design

Massively Parallel Logic Simulation with GPUs

Extendable pattern-oriented optimization directives

Quartile and Outlier Detection on Heterogeneous Clusters Using Distributed Radix Sort

Parallel Mining of Neuronal Spike Streams on Graphics Processing Units

Code Optimization Techniques for Graphics Processing Units

Customizable Memory Schemes for Data Parallel Accelerators

A Comparison of Modern GPU and CPU Architectures: And the Common Convergence of Both

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

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

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