compiler optimization

Anders Hejlsberg whiteboards how compilers are taught versus how modern ones are actually built. He explains the shift to compilers as services with incremental, error-tolerant, IDE-oriented design, as in Roslyn and TypeScript.

A long Mill Computing lecture on the Mill CPU's compiler toolchain, covering the specializer that targets family members, scheduling and latency passes, and the belt. It explains how the compiler handles a wide-issue, exposed-pipeline design.

The introductory lecture of Stephen Boyd's Stanford Convex Optimization I course (EE 364A). It outlines convex sets, functions and problems, and the course's coverage of least-squares, linear and semidefinite programming, duality and interior-point methods.

Casey Muratori's first refterm lecture lays out philosophies of optimization, distinguishing optimization from non-pessimization, which means avoiding needless work. It uses his fast terminal renderer project to show how fast computers really are.

Ian Piumarta's 2007 Stanford EE380 lecture on building a dynamic programming environment from the ground up, from the Viewpoints Research Institute's work. It covers the design of a language and system with a recursive design, parsing through code generation, and the COLA architecture.

Lecture 7A from MIT 6.001 (SICP) by Abelson and Sussman introduces the metacircular evaluator, a Scheme interpreter written in Scheme. It shows how evaluating a program is itself a program.

Lecture 1B of MIT's 6.001 Structure and Interpretation of Computer Programs (1986) with Gerald Sussman, covering procedures, processes and the substitution model. It includes the line about the power of naming things.

Lecture 7A of the 1986 MIT 6.001 SICP course, with Gerald Sussman presenting the metacircular evaluator, part 1. It shows how a Lisp evaluator can be written in Lisp itself.

Casey Muratori's University of Twente guest lecture is a case study in how straightforward code can cut thousands of lines and tens of seconds of runtime down to a few dozen lines running in under a second. It uses concrete examples to show why software is often an order of magnitude slower than it should be.

A lecture by Commodore Grace Hopper on the future of computing, posted by MIT Lincoln Laboratory. It includes her well-known nanosecond wire demonstration and humorous comparisons of device latencies.