Models for runtime optimization Free Breakout Session Jens, Thomas, Alex, Christoph

Goal Models for runtime optimization Idea: Utilize model at startup to limit search space

Model What is the model? – A prediction function like f(P1, P2, …, Pn) = ExecutionTime – P1 could be the number of cores – P2 could be memory footprint Application specific performance curves for parameter SoPeCo to derive these performance curves – New Strategy? Identify most relevant parameter

Imaginary Idea Professorial Anti Pattern: Alice in Wonderland Divide task in atomic blocks Thread Pool – Optimization through number of cores: Rule of thumb: 2* #threads Ease task for auto-tuner with profile e.g. application is data bound Only Resource Parameters? Limit degrees of freedom (through special programming language and compiler) IMAGINATION ;)

Predict Concurrency Which point in each parameter space is optimal w.r.t. performance of both applications Use only half of each parameter space – E.g. just half the number of cores Palladio Approach: Model for the whole system not just the single application – Predict concurrency through simulation (But not feasible for on-the-fly)

Parameter Variation Variation of parameter with biggest gradient – E.g. Number of Threads Performance depends on utilization of hardware – Idea: Each parameter is more or less important for performance – But: Conflicts by influencing other parameters – Solution: Analysis of sensitivity of each parameter – Which parameter influences the utilization of a resource most?

Sensitivity/Relevance Analysis Define e.g. min, max of possible values Use methods (e.g. Plackett-Burman, Nelder- Mead) to vary (intelligent) values in-between bounds (parameter space) to derive order Result: Most important parameters identified Refine model to decrease error with specific parameter values Goal: Avoid to evaluate whole parameter space

Does it pay off to the prediction? Roofline model? [Williams and Patterson] Problem with abstract measurements Split computation task into sub task – Will split resource demand which in consequence increases parallelism and leads to speedup (hopefully)

Random measurement Problem: – Non steady functions? Simplex – Problem: local maxima Global search methods – Not feasible