Report Number: CS-TR-94-1520
Institution: Stanford University, Department of Computer Science
Title: Adaptive Optimization for SELF: Reconciling High Performance with Exploratory Programming
Author: Holzle, Urs
Date: August 1994
Abstract: Crossing abstraction boundaries often incurs a substantial run-time overhead in the form of frequent procedure calls. Thus, pervasive use of abstraction, while desirable from a design standpoint, may lead to very inefficient programs. Aggressively optimizing compilers can reduce this overhead but conflict with interactive programming environments because they introduce long compilation pauses and often preclude source-level debugging. Thus, programmers are caught on the horns of two dilemmas: they have to choose between abstraction and efficiency, and between responsive programming environments and efficiency. This dissertation shows how to reconcile these seemingly contradictory goals. Four new techniques work together to achieve this: - Type feedback achieves high performance by allowing the compiler to inline message sends based on information extracted from the runtime system. - Adaptive optimization achieves high responsiveness without sacrificing performance by using a fast compiler to generate initial code while automatically recompiling heavily used program parts with an optimizing compiler. - Dynamic deoptimization allows source-level debugging of optimized code by transparently recreating non-optimized code as needed. - Polymorphic inline caching speeds up message dispatch and, more significantly, collects concrete type information for the compiler. With better performance yet good interactive behavior, these techniques reconcile exploratory programming, ubiquitous abstraction, and high performance.