breandan (breandan) · GitHub
Research has begun to reveal many algorithms can be expressed as matrix multiplication, suggesting an unrealized connection between linear algebra and computer science. I speculate graphs are the missing piece of the puzzle. Graphs are not only useful as cognitive aides, but are suitable data structures for a wide variety of tasks, particularly on modern parallel processing hardware.
In this essay, I explore the virtues of graphs, algebra, types, and show how these concepts can help us reason about programs. I propose a computational primitive based on graph signal processing, linking software engineering, graphs, and linear algebra. Finally, I share my predictions for the path ahead, which I consider to be the start of an exciting new chapter in computing history.
n.b.: None of these ideas are mine alone. Shoulders of giants. Follow the links and use landscape mode for optimal reading experience.
breandan (breandan) · GitHub