Baseline JIT compilers need to compile early and as fast as possible, while still performing optimizations. One powerful technique to write fast baseline JIT compilers is abstract interpretation. Several implementations of this technique exist in practice, implementing in a single pass optimizations such as register allocation, constant propagation, and instruction scheduling. However, although they share the same technique, all these implementations vary in the exact optimizations performed, their internal design and further implementation details (e.g., the implementation language and framework). Thus, it is challenging to understand and isolate the benefits of the technique by simply studing these existing implementations.Understanding the real impact of compile-time abstract interpreters requires isolating performance differences and experimenting with different variations of the same implementation, which demands extensive engineering work. In this paper, we propose to analyse the impact of abstract interpreters through metacompilation. We use metacompilation as a means to (a) reduce the experimentation effort and (b) to produce compiler variants that are comparable, reducing implementation noise.We implemented our solution to generate several JIT compiler variants for the Pharo VM. We describe the adaptations required in the metacompilation framework to target both abstract interpreters and direct translators, in combination with Static Type Prediction optimizations.Our benchmarks show that compile-time abstract interpreters, on average, reduce the emitted machine code size by 12% and increase execution speed by 10%, up to 30%, without increasing JIT compilation overhead, compared to direct translators.

Producing robust memory manager implementations is a challenging task. Defects in garbage collection algorithms produce subtle effects that are revealed later in program execution as memory corruptions. This problem is exacerbated by the fact that garbage collection algorithms deal with low-level implementation details to be efficient. Finding, reproducing, and debugging such bugs is complex and time-consuming.In this article, we propose to fuzz heaps by generating large sequences of random heap events guided by virtual machine experts. Randomly generated events exercise the garbage collection algorithm with the objective of crashing the virtual machine and finding bugs. Once a bug is found, we use a test case reduction algorithm to find the smaller subset of events that reproduces the issue.We implemented our approach on top of the virtual machine simulator of the Pharo Virtual Machine, to test its sequential stop-the-world generational scavenger. Experts guided our fuzzing toward the ephemeron finalization mechanism, corner allocation cases, and the heap compaction algorithm. Our prototype found 6 bugs: 3 in Pharo’s ephemeron implementation which is not yet in production, 2 bugs in the default compactor which has been in production for 8 years, and 1 bug in the VM simulator used daily by VM developers. We show how such test cases were automatically reduced to trivial sequences that were easy to debug.

Garbage collector pauses can take human perceivable pauses on big heaps. In this poster, we argue the existence of semi-permanent objects: objects that have really low chances of being collected, and even lower chances in production scenarios where the application code is not subject to change at run-time. The key challenge that stems from having a semi-permanent generation is to select what objects are meant to be moved or allocated to such generation. We present preliminary work that shows that a manual selection of code-related objects following hand-crafted heuristics reduces GC pause times by half.