<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:media="http://search.yahoo.com/mrss/">
  <channel>
    <title>GitHub Cuda Weekly Trending</title>
    <description>Weekly Trending of Cuda in GitHub</description>
    <pubDate>Thu, 16 Jul 2026 01:43:16 GMT</pubDate>
    <link>http://mshibanami.github.io/GitHubTrendingRSS</link>
    
    <item>
      <title>thu-ml/SageAttention</title>
      <link>https://github.com/thu-ml/SageAttention</link>
      <description>&lt;p&gt;[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/54c2d13ed8d827842049eda16f16791ff1f008dc1086be078e8a7ad8b47789e6/thu-ml/SageAttention" medium="image" />
      
    </item>
    
    <item>
      <title>deepseek-ai/DeepEP</title>
      <link>https://github.com/deepseek-ai/DeepEP</link>
      <description>&lt;p&gt;DeepEP: an efficient expert-parallel communication library&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/9ce1575118582498c4bf406fabcaeeae8c16b7eb51467a2c1285ea1d79e640d6/deepseek-ai/DeepEP" medium="image" />
      
    </item>
    
    <item>
      <title>karpathy/llm.c</title>
      <link>https://github.com/karpathy/llm.c</link>
      <description>&lt;p&gt;LLM training in simple, raw C/CUDA&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/9c5367415be5e225b26a8f9dfe79720cf2bf91b5ad2ddfc39ed1264f412cda88/karpathy/llm.c" medium="image" />
      
    </item>
    
    <item>
      <title>deepseek-ai/DeepGEMM</title>
      <link>https://github.com/deepseek-ai/DeepGEMM</link>
      <description>&lt;p&gt;DeepGEMM: clean and efficient BLAS kernel library on GPU&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/9fc64164ffc8e53b853d03df23ce97f7b5a403f4f649cfe98781fbff02ab3426/deepseek-ai/DeepGEMM" medium="image" />
      
    </item>
    
    <item>
      <title>NVIDIA/nccl-tests</title>
      <link>https://github.com/NVIDIA/nccl-tests</link>
      <description>&lt;p&gt;NCCL Tests&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/2e6290540c9242e28fc04d9296cf8c0947471ab23fa90bfe222219f5a54d9b1f/NVIDIA/nccl-tests" medium="image" />
      
    </item>
    
    <item>
      <title>NVIDIA/cub</title>
      <link>https://github.com/NVIDIA/cub</link>
      <description>&lt;p&gt;[ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://repository-images.githubusercontent.com/8225159/68a74e00-557d-11eb-8f63-2cdf2ea55052" medium="image" />
      
    </item>
    
    <item>
      <title>NVlabs/instant-ngp</title>
      <link>https://github.com/NVlabs/instant-ngp</link>
      <description>&lt;p&gt;Instant neural graphics primitives: lightning fast NeRF and more&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://repository-images.githubusercontent.com/444886996/0874cd2d-cff7-4707-9bf4-8caf0ab433bb" medium="image" />
      
    </item>
    
    <item>
      <title>Dao-AILab/causal-conv1d</title>
      <link>https://github.com/Dao-AILab/causal-conv1d</link>
      <description>&lt;p&gt;Causal depthwise conv1d in CUDA, with a PyTorch interface&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/25f7ed92e539530e1f31f986b91ac0a2cdffbc310b484c4482ad262da7e35af9/Dao-AILab/causal-conv1d" medium="image" />
      
    </item>
    
    <item>
      <title>rahul-goel/fused-ssim</title>
      <link>https://github.com/rahul-goel/fused-ssim</link>
      <description>&lt;p&gt;Lightning fast differentiable SSIM.&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/f53c3dfb59cdece3d11d22b4f9668b150254945b126c1435483472d8b76f1193/rahul-goel/fused-ssim" medium="image" />
      
    </item>
    
    <item>
      <title>NVIDIA/cuopt</title>
      <link>https://github.com/NVIDIA/cuopt</link>
      <description>&lt;p&gt;GPU accelerated decision optimization&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/7af4f1241c791e5e9ac5f263330db4344b32d3a01c1709ec58faa6be641b9033/NVIDIA/cuopt" medium="image" />
      
    </item>
    
    <item>
      <title>brucefan1983/GPUMD</title>
      <link>https://github.com/brucefan1983/GPUMD</link>
      <description>&lt;p&gt;Graphics Processing Units Molecular Dynamics&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/df1e40a5ad031a834fada8d8d540b156705d219fd1baaeaec7e2d5bced13880f/brucefan1983/GPUMD" medium="image" />
      
    </item>
    
    <item>
      <title>NVIDIA/cuvs</title>
      <link>https://github.com/NVIDIA/cuvs</link>
      <description>&lt;p&gt;cuVS - a library for vector search and clustering on the GPU&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/c50e3c69e7d8a4c64314d3d1f1b6d044e6b2e2084eb756550c3715b90b797254/NVIDIA/cuvs" medium="image" />
      
    </item>
    
    <item>
      <title>NVIDIA/nvbench</title>
      <link>https://github.com/NVIDIA/nvbench</link>
      <description>&lt;p&gt;CUDA Kernel Benchmarking Library&lt;/p&gt;&lt;hr&gt;</description>
      
      <media:content url="https://opengraph.githubassets.com/87a8017ef03c49406764100bf8d85130cbc119245942d75f479e555fffe0f686/NVIDIA/nvbench" medium="image" />
      
    </item>
    
  </channel>
</rss>
