Most high-level programming languages run on top of a virtual machine (VM) to abstract away from the underlying hardware. To reach high-performance, the VM typically relies on an optimising just-in-time compiler (JIT), which speculates on the program behavior based on its first runs to generate at runtime efficient machine code and speed-up the program execution. As multiple runs are required to speculate correctly on the program behavior, such a VM requires a certain amount of time at start-up to reach peak performance. The optimising JIT itself is usually compiled ahead-of-time to executable code as part of the VM. The dissertation proposes Sista, an architecture for an optimising JIT, in which the optimised state of the VM can be persisted across multiple VM start-ups and the optimising JIT is running in the same runtime than the program executed. To do so, the optimising JIT is split in two parts. One part is high-level: it performs optimisations specific to the programming language run by the VM and is written in a metacircular style. Staying away from low-level details, this part can be read, edited and debugged while the program is running using the standard tool set of the programming language executed by the VM. The second part is low-level: it performs machine specific optimisations and is compiled ahead-of-time to executable code as part of the VM. The two parts of the JIT use a well-defined intermediate representation to share the code to optimise. This representation is machine-independent and can be persisted across multiple VM start-ups, allowing the VM to reach peak performance very quickly. To validate the architecture, the dissertation includes the description of an implementation on top of Pharo Smalltalk and its VM. The implementation is able to run a large set of benchmarks, from large application benchmarks provided by industrial users to micro-benchmarks used to measure the performance of specific code patterns. The optimising JIT is implemented according to the architecture proposed and shows significant speed-up (up to 5x) over the current production VM. In addition, large benchmarks show that peak performance can be reached almost immediately after VM start-up if the VM can reuse the optimised state persisted from another run.
- Directeurs de thèse : Stéphane Ducasse et Marcus Denker - Rapporteurs : Gaël Thomas et Laurence Tratt - Examinateurs : Theo D'Hondt et Elisa Gonzales Boix
Thesis of the team RMoD defended on 15/09/2017