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STT-RAM for Shared Memory in GPUs

Prateeksha Satyamoorthy

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 285, OpenCL, Thesis

October 23, 2011 by hgpu

Accelerating Component-Based Dataflow Middleware with Adaptivity and Heterogeneity

Timothy D. R. Hartley

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro FX 5600, Optimization, Performance, Thesis

October 22, 2011 by hgpu

Implementing a Preconditioned Iterative Linear Solver Using Massively Parallel Graphics Processing Units

Amirhassan Asgari Kamiabad

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Power Systems Simulation, Thesis

October 22, 2011 by hgpu

GPU performance prediction using parametrized models

Andreas Resios

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, Optimization, Performance, Thesis

October 22, 2011 by hgpu

CUDA Application Design and Development

Rob Farber

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Tags: Book, Computer science, CUDA, nVidia, Review, Software Engineering

October 22, 2011 by hgpu

Hardware Transactional Memory for GPU Architectures

Wilson W. L. Fung, Inderpreet Singh, Andrew Brownsword, Tor M. Aamodt

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Tags: Bloom filter, Computer science, CUDA, Memory, nVidia, nVidia Quadro FX 5800, OpenCL, Performance, Programming techniques

October 22, 2011 by hgpu

Low-Impact Profiling of Streaming, Heterogeneous Applications

Joseph Marion Lancaster

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Tags: Algorithms, Computer science, Heterogeneous systems, Optimization, Performance, Thesis

October 22, 2011 by hgpu

Parallel Compression Checkpointing for Socket-Level Heterogeneous Systems

Yongpeng Liu, Hong Zhu, Yongyan Liu, Feng Wang, Baohua Fan

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Tags: Algorithms, ATI, ATI Radeon HD 4870, Compression, Computer science, Heterogeneous systems, OpenCL

October 22, 2011 by hgpu

Concurrent Algorithms and Data Structures for Many-Core Processors

Daniel Cederman

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, Performance, Sorting, Thesis

October 21, 2011 by hgpu

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Przemyslaw Stpiczynski

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Tags: Algorithms, BLAS, Computer science, CUDA, Hybrid computing, Linear Algebra, nVidia, Tesla C2050

October 21, 2011 by hgpu

Analysis and Implementation of eSTREAM and SHA-3 Cryptographic Algorithms

Deian Stefan

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Optimization, Security, Stream Ciphers, Thesis

October 21, 2011 by hgpu

OpenMP for Accelerators

James C. Beyer, Eric J. Stotzer, Alistair Hart, Bronis R. de Supinski

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Tags: Computer science, CUDA, DSP, Fortran, nVidia, OpenMP, Tesla C2050

October 21, 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

STT-RAM for Shared Memory in GPUs

Accelerating Component-Based Dataflow Middleware with Adaptivity and Heterogeneity

Implementing a Preconditioned Iterative Linear Solver Using Massively Parallel Graphics Processing Units

GPU performance prediction using parametrized models

CUDA Application Design and Development

Hardware Transactional Memory for GPU Architectures

Low-Impact Profiling of Streaming, Heterogeneous Applications

Parallel Compression Checkpointing for Socket-Level Heterogeneous Systems

Concurrent Algorithms and Data Structures for Many-Core Processors

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Analysis and Implementation of eSTREAM and SHA-3 Cryptographic Algorithms

OpenMP for Accelerators

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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