Abstract
This chapter describes additional topics, such as design space exploration (DSE), hardware/software codesign, runtime adaptability, and performance/energy autotuning (offline and online). More specifically, this chapter provides a starting point for developers needing to apply these concepts, especially in the context of high-performance embedded computing. More specifically, this chapter explains how autotuning can assist developers to find the best compiler optimizations given the target objective (e.g., execution time reductions, energy savings), and how static and dynamic adaptability can be used to derive optimized code implementations. Furthermore, it covers simulated annealing, which is an important and easily implementable optimization technique that can be used in the context of DSE and offline autotuning. In addition, this chapter covers multiobjective optimizations and Pareto frontiers which we believe provides a foundation for readers and developers needing to deal with more complex DSE problems. Although we cannot possibly cover all of the aforementioned topics in detail due to their nature and complexity, we expect that this chapter provides a useful introduction to them, and as the final chapter to this book, that it brings interesting points of discussion on top of topics presented in previous chapters.
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