*time Optimization Heiko, Diego, Thomas, Kevin, Andreas, Jens

Question How can design time help to reduce the complexity of runtime optimization? Curse of dimensionality During design-time the possible space for optimization is restricted Currently: – Start from a sequential solution – Stepwise increase Narrowing down the search space in each phase

Different Phases Design Time Implementation time Runtime

Design Time Architecture Design Optimization Variation Points – Component Allocation, Replication, Implementation – Hardware #cores, cpu speed, scheduling algorithm – Component configuration Goal: – Find good solution w.r.t. to different criteria Result  a set of solutions (Pareto Front) that are optimal w.r.t. different criteria  Performance, reliability, cost, energy efficiency (interesting since it is hard to optimize  Only valid for the assumptions made by the optimization

Implementation / Compilation Time Input Parameters – HW Properties #of cores, cache size Variation Points – Tiling for loop iterations – reorder of instructions Goal – Minimize execution time or minimum amount of communication Means: – Smart compilation – Manual – Measurement Result: – A program with several parameters that can be changed at runtime (#of threads, blocksize…) – Parameterized schedule – Model that describes the effect of parameters on execution time

Runtime Variation Points – # of used threads – Memory footprint – Application specific parameters Means – Search-based optimization – Combine with prediction to assess the effect of parameter changes Result – Parameter Values

Interaction of Phases Feedback cycles #cores & #of threads used How do these interaction affect the prediction & optimization process?

That’s it.

Expectation How to use model prediction for runtime optimization? – Block Size  HW-dependent? How to do good task scheduling? – Adaptive translation & execution Good starting point for search? – Preliminary measurement Multicriteria optimization at runtime? Extending design-time optimization towards runtime? Parameter- space exploration How can design time help to reduce the complexity of runtime optimization? – Curse of dimensionality – During design-time the possible space for optimization is restircted – Currently: Start from a sequential solution Stepwise increase Narrowing down the search space in each phase