Adaptive optimization is a technique in computer science that performs dynamic recompilation of portions of a program based on the current execution profile. With a simple implementation, an adaptive optimizer may simply make a trade-off between Just-in-time compilation and interpreting instructions. At another level, adaptive optimization may take advantage of local data conditions to optimize away branches and to use inline expansion to decrease context switching. Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. ... In computer science, dynamic recompilation (sometimes abbreviated to dynarec) is a feature of some emulators and virtual machines, where the system may recompile some part of a program during execution. ... In computing, just-in-time compilation (JIT), also known as dynamic translation, is a technique for improving the performance of bytecode-compiled programming systems, by translating bytecode into native machine code at runtime. ... In-line expansion or inlining for short is a compiler optimization which expands a function call site into the actual implementation of the function which is called, rather than each call transferring control to a common piece of code. ...

Consider a hypothetical banking application that handles transactions one after another. These transactions may be checks, deposits, and a large number of more obscure transactions. When the program executes, the actual data may consist of clearing tens of thousands of checks without processing a single deposit and without processing a single check with a fraudulent account number. An adaptive optimizer would compile assembly code to optimize for this common case. If the system then started processing tens of thousands of deposits instead, the adaptive optimizer would recompile the assembly code to optimize the new common case. This optimization may include inlining code or moving error processing code to secondary cache.

In some systems, notably the Java Virtual Machine, execution over a range of bytecode instructions can be provably reversed. This allows an adaptive optimizer to make risky assumptions about the code. In the above example, the optimizer may assume all transactions are checks and all account numbers of valid. When these assumptions prove incorrect, the adaptive optimizer can 'unwind' to a valid state and then interpret the byte code instructions correctly. A Java Virtual Machine (JVM), originally developed by Sun Microsystems, is a virtual machine that executes Java bytecode. ... Java bytecode is the form of instructions that the Java virtual machine executes. ...

External links

• CiteSeer for "Adaptive Optimization in the Jalapeño JVM (2000)" by Matthew Arnold, Stephen Fink, David Grove, Michael Hind, Peter F. Sweeney. Contains links to the full paper in various formats.