- Things that everyone needs to know - Cheat sheet for class "Hardware Accelerators for Machine Learning".
- Google AI's CO2 Footprint - Article by David Patterson, 2017 ACM A.M Turing Award winner.
- Scalable Dataframe Systems - Modin is a very easy to use library that addresses Pandas' scalability issues.
- KMN - The power of choice in data-aware cluster scheduling.
- BytePS - Effective utilization of both CPU and GPU resources during DL training.
- Omega - Flexible, scalable shared-state schedulers for large compute clusters.
- Ghostor - Secure data-sharing system from decentralized trust.
- OSTEP - Free Operating Systems book from professors at UW Madison.
- Light Node - How to interact with Ethereum network using just 400MB of storage.
- Non-Blocking Concurrent Queue Algorithms - Implementation of concurrent queues without lock.
- Sorting a million 32-bit integers in 2MB of RAM using Python - Efficient implementation of external sorting in Python.
- Visualizing the load balancing problem - Excellent load balancing playground at the end of the post.
- What Color is Your Function? - An interesting take on async functions.
- Understanding the Python GIL - A talk by David Beazley.
- Python Concurrency - When to use threads, when to use processes, and when to use async.
- A Guide to the Go Garbage Collector - Visualization of trade-off between GC CPU and memory.
