Abstract: “Computation” has become a massive part of our daily lives: in science, a lot of experiments and analysis rely on massive computation, in AI we use vast resources to train and use massive models, and in engineering we use complex simulations and digital twins to increase efficiency and productivity. Under the assumption that computation is cheap, and time-to-result is the only relevant metric, we often use significant computational resources at low efficiency. In this talk, I argue this approach is an unacceptable waste of computing resources, and demonstrate we can do better! By means of a couple of case-studies, I will show how performance engineering can be used for zero-waste computing, proving how efficiency and time-to-result can be happily married. I will further propose a co-design methodology that leverages such performance engineering methods to enable the selection of algorithms _and_ their effective deployment on suitable infrastructure. The approach relies on design-space exploration, driven by efficient search methods and compositional performance models. I will conclude by reflecting on the next steps and open questions that need answers to make this co-design approach feasible and applicable for more applications and systems.

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