Creating an interpreter is a simple and fast way to implement a dynamic programming language. With this ease also come major drawbacks. Interpreters are significantly slower than compiled machine code because they have a high dispatch overhead and cannot perform optimizations. To overcome these limitations, interpreters are commonly combined with just-in-time compilers to improve the overall performance. However, this means that a just-in-time compiler has to be implemented for each language. We explore the approach of taking an interpreter of a dynamic language and running it on top of an optimizing trace-based virtual machine, i.e., we run a guest VM on top of a host VM . The host VM uses trace recording to observe the guest VM executing the application program. Each recorded trace represents a sequence of guest VM bytecodes corresponding to a given execution path through the application program. The host VM optimizes and compiles these traces to machine code, thus eliminating the need for a custom just-in-time compiler for the guest VM. The guest VM only needs to provide basic information about its interpreter loop to the host VM.

Optionally typed languages enable direct performance comparisons between untyped and type annotated source code. We present a comprehensive performance evaluation of two different JIT compilers in the context of ActionScript, a production-quality optionally typed language. One JIT compiler is optimized for quick compilation rather than JIT compiled code performance. The second JIT compiler is a more aggressively optimizing compiler, performing both high-level and low-level optimizations.We evaluate both JIT compilers directly on the same benchmark suite, measuring their performance changes across fully typed, partially typed, and untyped code. Such evaluations are especially relevant to dynamically typed languages such as JavaScript, which are currently evaluating the idea of adding optional type annotations. We demonstrate that low-level optimizations rarely accelerate the program enough to pay back the investment into performing them in an optionally typed language. Our experiments and data demonstrate that high-level optimizations are required to improve performance by any significant amount.

Since their inception, the usage pattern of web browsers has changed substantially. Rather than sequentially navigating static web sites, modern web browsers often manage a large number of simultaneous tabs displaying dynamic web content, each of which might be running a substantial amount of client-side JavaScript code. This environment introduced a new degree of parallelism that was not fully embraced by the underlying JavaScript virtual machine architecture. We propose a novel abstraction for multiple disjoint JavaScript heaps, which we call compartments. We use the notion of document origin to cluster objects into separate compartments. Objects within a compartment can reference each other directly. Objects across compartments can only reference each other through wrappers. Our approach reduces garbage collection pause times by permitting collection of sub-heaps (compartments), and we can use cross-compartment wrappers to enforce cross origin object access policy.

Generators offer an elegant way to express iterators. However, their performance has always been their Achilles heel and has prevented widespread adoption. We present techniques to efficiently implement and optimize generators. We have implemented our optimizations in ZipPy, a modern, light-weight AST interpreter based Python 3 implementation targeting the Java virtual machine. Our implementation builds on a framework that optimizes AST interpreters using just-in-time compilation. In such a system, it is crucial that AST optimizations do not prevent subsequent optimizations. Our system was carefully designed to avoid this problem. We report an average speedup of 3.58x for generator-bound programs. As a result, using generators no longer has downsides and programmers are free to enjoy their upsides.

Java uses automatic memory management, usually implemented as a garbage-collected heap. That lifts the burden of manually allocating and deallocating memory, but it can incur significant runtime overhead and increase the memory footprint of applications. We propose a hybrid memory management scheme that utilizes region-based memory management to deallocate objects automatically on region exits. Static program analysis detects allocation sites that are safe for region allocation, i.e., the static analysis proves that the objects allocated at such a site are not reachable after the region exit. A regular garbage-collected heap is used for objects that are not region allocatable. The region allocation exploits the temporal locality of object allocation. Our analysis uses coarse-grain source code annotations to disambiguate objects with non-overlapping lifetimes, and maps them to different memory scopes. Region-allocated memory does not require garbage collection as the regions are simply deallocated when they go out of scope. The region allocation technique is backed by a garbage collector that manages memory that is not region allocated. We provide a detailed description of the analysis, provide experimental results showing that as much as 78% of the memory is region allocatable and discuss how our hybrid memory management system can be implemented efficiently with respect to both space and time.