\(\rightarrow\) Some CUDA/C++ learning notes:

• GPU architecture and warp scheduling.
• Occupancy, Compute intensity, and Tiling.
• DRAM banks and why it matters for code optimization.

\(\rightarrow\) Random C++:

• Loading MNIST dataset in C++

\(\rightarrow\) Technical

• Handling checkpoints from terminal - some useful tricks.
• Quick reset of my compute pod.
• Speed up your migration to VIM.

\(\rightarrow\) LLMs

• Some trends to speed up autoregressive inference of LLMs (unifinished).

\(\rightarrow\) Math:

• Matrix Differential Calculus.
• Convergence Rates of SGD in convex and non-convex cases.

Some GitHub repos:

• Training ML models with CUDA/C++: \(\rightarrow\) Github link.
• GPT2 factorized with (multiple) Kronecker Factors: \(\rightarrow\) Github link.
• Backpropagation from scratch \(\rightarrow\) Github link.
• Randomized Algorithms \(\rightarrow\) Github link.
• Reinforcement Learning \(\rightarrow\) Github link.

Bio/Contact:

• I’m based in Passau, Germany (I’m also based).
• I graduated with a software engineering master’s degree in 2019.
• I’m currently pursuing a second Master’s degree, and I’m a part-time research assistant @UniPassau.
• Yes, I like a lot of bullet points, and you?
• Gmail: ayoub.benayad.467; LinkedIn; Instagram.