high performance computing on graphics processing units: hgpu.org

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Performance potential for simulating spin models on GPU

Martin Weigel

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Tags: Computational Physics, CUDA, Disordered Systems and Neural Networks, Heisenberg model, High Energy Physics - Lattice, Ising model, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, Package, Physics, Statistical Mechanics, Tesla C1060

January 12, 2011 by hgpu

Understanding the design trade-offs among current multicore systems for numerical computations

Seunghwa Kang, David A. Bader, Richard W. Vuduc

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Tags: Cell processor, Computer science, CUDA, nVidia, Performance, Tesla C1060

January 11, 2011 by hgpu

A memory optimization technique for software-managed scratchpad memory in GPUs

Maryam Moazeni, Alex A. T. Bui, Majid Sarrafzadeh

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Tags: Computer science, CUDA, Memory, nVidia, nVidia Quadro FX 5600, Optimization

January 11, 2011 by hgpu

Optimization of tele-immersion codes

Albert Sidelnik, I-Jui Sung, Wanmin Wu, Maria Jesus Garzaran, Wen-mei Hwu, Klara Nahrstedt, David Padua, and Sanjay J. Patel

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 280, Optimization

January 11, 2011 by hgpu

Exploring new architectures in accelerating CFD for Air Force applications

Jack Dongarra, Shirley Moore, Gregory Peterson, Stanimire Tomov

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Tags: Algorithms, Fluid dynamics, FPGA, nVidia, nVidia Quadro FX 5600, Tesla T10P

January 11, 2011 by hgpu

An events based algorithm for distributing concurrent tasks on multi-core architectures

David W. Holmes, John R. Williams, Peter Tilke

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Tags: Algorithms, Computer science, Performance, Task scheduling

January 11, 2011 by hgpu

Efficient gradient-domain compositing using quadtrees

Aseem Agarwala

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Tags: Algorithms, Filtering, Image processing

January 11, 2011 by hgpu

SCELib3.0: The new revision of SCELib, the parallel computational library of molecular properties in the Single Center Approach

N. Sanna, I. Baccarelli, G. Morelli

Tags: Biology, Chemistry, CUDA, Molecular modeling, nVidia

January 11, 2011 by hgpu

iNFAnt: NFA pattern matching on GPGPU devices

Niccolo' Cascarano, Pierluigi Rolando, Fulvio Risso, Riccardo Sisto

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Tags: Computer science, CUDA, Networks, nVidia, nVidia GeForce GTX 260, String matching

January 11, 2011 by hgpu

Accelerating linpack with CUDA on heterogenous clusters

Massimiliano Fatica

Tags: Computer science, CUDA, GPU cluster, Linear Algebra, nVidia

January 11, 2011 by hgpu

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

Ros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen-mei W. Hwu

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Tags: Compilers, Computer science, CUDA, FPGA, High-level Languages

January 11, 2011 by hgpu

CUDA-based real time surgery simulation

Youquan Liu, Suvranu De

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Tags: CUDA, Medicine, nVidia, nVidia GeForce 8800 GTX, Surgical simulation

January 11, 2011 by hgpu

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

KISim: Kubernetes Intelligent Scheduling Simulator

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Efficient GPU Implementation of Multi-Precision Integer Division

exa-AMD: Exascale Accelerated Materials Discovery

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

ParEval: A Parallel Code Evaluation Benchmark

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

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high performance computing on graphics processing units: hgpu.org

Programming

Performance potential for simulating spin models on GPU

Understanding the design trade-offs among current multicore systems for numerical computations

A memory optimization technique for software-managed scratchpad memory in GPUs

Optimization of tele-immersion codes

Exploring new architectures in accelerating CFD for Air Force applications

An events based algorithm for distributing concurrent tasks on multi-core architectures

Efficient gradient-domain compositing using quadtrees

SCELib3.0: The new revision of SCELib, the parallel computational library of molecular properties in the Single Center Approach

iNFAnt: NFA pattern matching on GPGPU devices

Accelerating linpack with CUDA on heterogenous clusters

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

CUDA-based real time surgery simulation

Recent source codes

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

KISim: Kubernetes Intelligent Scheduling Simulator

Efficient GPU Implementation of Multi-Precision Integer Division

exa-AMD: Exascale Accelerated Materials Discovery

ParEval: A Parallel Code Evaluation Benchmark

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

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