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Reduced Vlasov-Maxwell simulations

Philippe Helluy, Laurent Navoret, Nhung Pham, Anais Crestetto

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Tags: Electrodynamics, Hyperbolic system, Maxwell's equations, OpenCL, Particle-in-cell methods

March 12, 2014 by hgpu

A GPU-based hyperbolic SVD algorithm

Vedran Novakovic, Sanja Singer

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Tags: Computer science, CUDA, Hyperbolic system, Mathematics, MPI, Numerical Analysis, nVidia, nVidia GeForce GTX 275, nVidia GeForce GTX 280, Tesla C1060

November 10, 2010 by hgpu

Parallel hyperbolic PDE simulation on clusters: Cell versus GPU

Scott Rostrup, Hans De Sterck

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Tags: Cell processor, CUDA, Hyperbolic PDEs, Hyperbolic system, Mathematics, nVidia, Package, Tesla C2050, Tesla T10

November 7, 2010 by hgpu

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

TRUST: a thermalhydraulic software package for CFD simulations

TRUST: the HPC open-source CFD platform – from CPU to GPU

Modular: The Modular Platform (includes MAX & Mojo)

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

Allo: Accelerator Design Language

Dato: A Task-Based Programming Model for Dataflow Accelerators

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

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