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Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

Leonardo Solis-Vasquez, Andreas F. Tillack, Diogo Santos-Martins, Andreas Koch, Stefano Forli

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Tags: Chemistry, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia A100, nVidia B200, nVidia H100, Package

October 26, 2025 by hgpu

FLASH: Fast All-to-All Communication in GPU Clusters

Yiran Lei, Dongjoo Lee, Liangyu Zhao, Daniar Kurniawan, Chanmyeong Kim, Heetaek Jeong, Changsu Kim, Hyeonseong Choi, Liangcheng Yu, Arvind Krishnamurthy, Justine Sherry, Eriko Nurvitadhi

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Tags: AMD Radeon Instinct MI300X, ATI, Computer science, GPU cluster, Heterogeneous systems, MPI, nVidia, nVidia A100, nVidia B200, nVidia H100

May 25, 2025 by hgpu

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

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

Agentic Code Optimization via Compiler-LLM Cooperation

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high performance computing on graphics processing units: hgpu.org

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

FLASH: Fast All-to-All Communication in GPU Clusters

Recent source codes

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

CUDAnalyst (CUDA + Analyst)

CodegenBench