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    <title>GPU Optimization on KAD</title>
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      <title>Claude Optimizes 30&#43; Scientific Models as ScienceIDE Scales AI</title>
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&lt;p&gt;Claude Optimizes 30+ Scientific Models as ScienceIDE Scales AI&lt;/p&gt;&lt;/blockquote&gt;
&lt;p&gt;AI is moving beyond writing scientific code toward optimizing, validating, and learning from complex scientific software.&lt;/p&gt;</description>
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      <title>MegaMoE MegaKernel Architecture: Optimizing DeepSeek-V4 LLM Performance</title>
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      <pubDate>Sat, 16 May 2026 14:04:09 +0800</pubDate>
      
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&lt;p&gt;MegaMoE MegaKernel Architecture: Optimizing DeepSeek-V4 LLM Performance&lt;/p&gt;&lt;/blockquote&gt;
&lt;p&gt;The &lt;strong&gt;MegaMoE architecture&lt;/strong&gt; in DeepSeek-V4 addresses critical bottlenecks in large-scale Mixture-of-Experts (MoE) LLMs. Traditional expert parallelism suffers from high-latency inter-node communication via NVLink or RDMA, leaving GPU streaming multiprocessors (SMs) idle during matrix computation. MegaMoE introduces a &lt;strong&gt;unified MegaKernel&lt;/strong&gt; that combines advanced &lt;strong&gt;warp specialization&lt;/strong&gt; and &lt;strong&gt;symmetric memory pipelining&lt;/strong&gt; to overlap computation and communication, achieving &lt;strong&gt;up to 1.96x end-to-end performance improvement&lt;/strong&gt;.&lt;/p&gt;</description>
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