Matmul

 matmul

SASS and GPU Microarchitecture

Unwrapping TPUs

PTX instructions to program Tensor Cores on the latest NVIDIA Blackwell GPUs

Performance hints from Jeff Dean and Sanjay Ghemawat

cs-6341-robotics

AI-ML-Roadmap-from-scratch?tab=readme-ov-file#module-4---machine-learning

Pen & Paper exercises for Machine Learning

ultrascale-playbook

Learnings from RL training of an LLM

Computer Systems architecture

 https://web.stanford.edu/class/ee282/index.html

telesens systolic-architectures

hazyresearch.stanford.edu

Computer achitecture

 Advanced Computer architecture and systems

scuttleblurb nvda1 CPU to GPU

Running MCP server in cloud

Model flops utilization

HW for deep learning

Data gated conventional MAC shown in the video.

DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING

deeplearningbook MIT

How to think about GPUs

The tweet

 scaling-book/gpus

Acknowledgements

https://cvw.cac.cornell.edu/gpu-architecture/gpu-memory/comparison_cpu_mem

floating point rounding mode

What every developer should know about GPU

MIT's Charles Leiserson lectures on how C code is compiled into assembly and machine code, leading to hardware-level execution.

What every programmer should know about memory

Different parallelism

Machine Learning Accelerators

Principles of Large-Scale Machine Learning

ML Resources

 tensortonic Learn machine learning algos through code

Good Staff Engineer

 Good Staff Engineer

Hardware acceleration

 Efficient Deep Learning Computing

6-172-performance-engineering-of-software-systems lecture-1-introduction-and-matrix-multiplication

deeplearning/performance/dl-performance-matrix-multiplication

why-gemm-is-at-the-heart-of-deep-learning

MIT introtodeeplearning - Jan 2026

arith24.arithsymposium.org/slides/s7-koenig.pdf

CS378 different labels of matrix multiplication

cs217.stanford.edu

Computer science CMU