The end of the economical version of Moore's Law (exponential decrease in transistor unit costs), the advent of metered cloud computing, and the shift of the economy toward digital goods and SaaS-based business models has shifted performance engineering from the fringes ("CPU progress will fix it") to center stage: Gross margins and through that, company value is related to efficient delivery of compute-intensive services.

This talk recapitulates lessons from looking at performance and efficiency analysis of large-scale compute. Lessons include:

• Technical: How language design choices have direct implications on performance issues
• Historical: How the evolution of hardware leads to software that is often ill-suited for the performance geometry of the underlying machine
• Organizational: How Google's monorepo culture vs. Amazon's two-pizza-team culture impacts code efficiency
• Mathematical: Why statistical variance is your enemy, but really hard to control

The talk concludes with some thoughts on the inadequacy of existing tooling and where things could and should improve.