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GPU-Specfic Kalman Filtering and Retrodiction for Large-Scale Target Tracking

Sean Tager

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Tags: Algorithms, Computer science, Filtering, Thesis

October 13, 2013 by hgpu

Power Control for GPU Clusters in processing large-scale streams

Qingkui Chen, Haifeng Wang, Bocheng Liu

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Tags: Computer science, CUDA, Energy-efficient computing, GPU cluster, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480

October 13, 2013 by hgpu

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Jonathan Passerat-Palmbach

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Tags: Algorithms, Computer science, CUDA, Java, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Pseudo-random number generators, Software Engineering, Stochastic simulation, Tesla C2050, Tesla C2075, Tesla K20, Thesis

October 13, 2013 by hgpu

Simulating Active Membrane Systems Using GPUs

Miguel A. Martinez-del-Amor, Ignacio Perez-Hurtado, Mario J. Perez-Jimenez, Jose M. Cecilia, Gines D. Guerrero, Jose M. Garcia

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Tags: Computer science, CUDA, Membrane computing, nVidia, Tesla C1060

October 13, 2013 by hgpu

Characterizing the Challenges and Evaluating the Efficacy of a CUDA-to-OpenCL Translator

Mark Gardner, Paul Sathre, Wu-chun Feng, Gabriel Martinez

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL

October 13, 2013 by hgpu

Automatic run-time mapping of polyhedral computations to heterogeneous devices with memory-size restrictions

Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 680

October 12, 2013 by hgpu

Dandelion: a Compiler and Runtime for Heterogeneous Systems

Christopher J. Rossbach, Yuan Yu, Jon Currey, Jean-Philippe Martin, Dennis Fetterly

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20

October 12, 2013 by hgpu

A Parallel Intermediate Representation for Embedded Languages

Ivar Lang

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Tags: Computer science, Data parallelism, nVidia, OpenCL, Package, Tesla M2050, Thesis

October 10, 2013 by hgpu

Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures

Blesson Varghese, Andrew Rau-Chaplin

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Tags: Computational Engineering, Computer science, CUDA, Finance, nVidia, Risk Management, Tesla C2075

October 10, 2013 by hgpu

Scalable Fast Multipole Methods on Heterogeneous Architecture

Q. Hu

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Tags: Algorithms, Computer science, CUDA, Fast multipole method, Heterogeneous systems, N-body simulation, nVidia, nVidia GeForce 8800 GTX, Tesla C1060, Tesla C2050, Thesis

October 9, 2013 by hgpu

The GASPI API specification and its implementation GPI 2.0

D. Grünewald, C. Simmendinger

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Tags: Computer science, Intel Xeon Phi, MPI, Package, Programming Languages

October 9, 2013 by hgpu

UPC on MIC: Early Experiences with Native and Symmetric Modes

M.Luo ,M. Li, A. Venkatesh, X. Lu, D. K. Panda

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Tags: Computer science, Intel Phi, Intel Xeon Phi, Programming Languages

October 9, 2013 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

GPU-Specfic Kalman Filtering and Retrodiction for Large-Scale Target Tracking

Power Control for GPU Clusters in processing large-scale streams

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Simulating Active Membrane Systems Using GPUs

Characterizing the Challenges and Evaluating the Efficacy of a CUDA-to-OpenCL Translator

Automatic run-time mapping of polyhedral computations to heterogeneous devices with memory-size restrictions

Dandelion: a Compiler and Runtime for Heterogeneous Systems

A Parallel Intermediate Representation for Embedded Languages

Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures

Scalable Fast Multipole Methods on Heterogeneous Architecture

The GASPI API specification and its implementation GPI 2.0

UPC on MIC: Early Experiences with Native and Symmetric Modes

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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