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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

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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