<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Shivam Pandey — Writing</title><description>Technical notes on learning systems, GPU kernels, embodied intelligence, and research engineering.</description><link>https://shivampr21.com/</link><language>en-gb</language><item><title>FlashAttention Kernel: Backward Pass (Parallelism)</title><link>https://shivampr21.com/posts/flash-bwd-pll-14-4-2025-kernelized/</link><guid isPermaLink="true">https://shivampr21.com/posts/flash-bwd-pll-14-4-2025-kernelized/</guid><description>Mapping the backward pass into dependency graphs, loop-carried constraints, and parallel execution.</description><pubDate>Mon, 14 Apr 2025 00:00:00 GMT</pubDate><category>FlashAttention</category><category>Transformers</category><category>Attention</category><category>Compute</category><category>Autograd</category><category>Parallelism</category><category>CUDA</category></item><item><title>FlashAttention Kernel: Forward Pass (Parallelism)</title><link>https://shivampr21.com/posts/flash-fwd-pll-14-4-2025-kernelized/</link><guid isPermaLink="true">https://shivampr21.com/posts/flash-fwd-pll-14-4-2025-kernelized/</guid><description>A dependency-led path from the forward-pass equations toward the CUDA programming model.</description><pubDate>Mon, 14 Apr 2025 00:00:00 GMT</pubDate><category>FlashAttention</category><category>Transformers</category><category>Attention</category><category>Compute</category><category>Autograd</category><category>Parallelism</category><category>CUDA</category></item><item><title>FlashAttention Kernel: Forward Pass (MATH)</title><link>https://shivampr21.com/posts/flash-30-3-2025-kernelized/</link><guid isPermaLink="true">https://shivampr21.com/posts/flash-30-3-2025-kernelized/</guid><description>A first-principles derivation of the online, numerically stable FlashAttention forward pass.</description><pubDate>Sun, 30 Mar 2025 00:00:00 GMT</pubDate><category>FlashAttention</category><category>Transformers</category><category>Attention</category><category>Compute</category><category>Autograd</category><category>MATH</category></item><item><title>FlashAttention Kernel: Backward Pass (MATH)</title><link>https://shivampr21.com/posts/flash-bwd-30-3-2025-kernelized/</link><guid isPermaLink="true">https://shivampr21.com/posts/flash-bwd-30-3-2025-kernelized/</guid><description>Deriving attention gradients from tensor differentiation to a computable backward pass.</description><pubDate>Sun, 30 Mar 2025 00:00:00 GMT</pubDate><category>FlashAttention</category><category>Transformers</category><category>Attention</category><category>Compute</category><category>Autograd</category><category>MATH</category></item><item><title>Max Kernel: Forward and Backward Pass (MATH)</title><link>https://shivampr21.com/posts/max-30-3-2025-kernelized/</link><guid isPermaLink="true">https://shivampr21.com/posts/max-30-3-2025-kernelized/</guid><description>The forward and backward mathematics of a max-reduction kernel using indexed tensor notation.</description><pubDate>Sun, 30 Mar 2025 00:00:00 GMT</pubDate><category>AI</category><category>Compute</category><category>Autograd</category><category>MATH</category></item><item><title>SoftMax Kernel: Forward and Backward Pass (MATH)</title><link>https://shivampr21.com/posts/softmax-30-3-2025-kernelized/</link><guid isPermaLink="true">https://shivampr21.com/posts/softmax-30-3-2025-kernelized/</guid><description>A compact derivation of softmax forward and backward operations for later kernel work.</description><pubDate>Sun, 30 Mar 2025 00:00:00 GMT</pubDate><category>AI</category><category>Compute</category><category>Autograd</category><category>MATH</category></item></channel></rss>