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State-of-the-art dynamic compilers often use global approaches, like Linear Scan or Graph Coloring, for register allocation. These algorithms consider the complete compilation unit for allocation, which increases the complexity of the implementation (e.g., support for lifetime holes in Linear Scan) and potentially also affects compilation time. We propose a novel non-global algorithm, which splits a compilation unit into traces based on profiling feedback and subsequently performs register allocation within each trace individually. Traces reduce the problem size to a single linear code segment, which simplifies the problem a register allocator needs to solve. Additionally, we can apply different register allocation algorithms to each trace. We show that this non-global approach can achieve results competitive to global register allocation.We present an implementation of Trace Register Allocation based on the Graal VM and show an evaluation for common Java benchmarks. We demonstrate that performance of this non-global approach is within 3% (on AMD64) and 1% (on SPARC) of global Linear Scan register allocation.

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Many dynamic languages such as Ruby, Python and Perl offer some kind of functionality for writing parts of applications in a lowerlevel language such as C. These C extension modules are usually written against the API of an interpreter, which provides access to the higher-level language's internal data structures. Alternative implementations of the high-level languages often do not support such C extensions because implementing the same API as in the original implementations is complicated and limits performance.In this paper we describe a novel approach for modular composition of languages that allows dynamic languages to support C extensions through interpretation. We propose a flexible and reusable cross-language mechanism that allows composing multiple language interpreters, which run on the same VM and share the same form of intermediate representation -in this case abstract syntax trees. This mechanism allows us to efficiently exchange runtime data across different interpreters and also enables the dynamic compiler of the host VM to inline and optimize programs across multiple language boundaries. We evaluate our approach by composing a Ruby interpreter with a C interpreter. We run existing Ruby C extensions and show how our system executes combined Ruby and C modules on average over 3× faster than the conventional implementation of Ruby with native C extensions, and on average over 20× faster than an existing alternate Ruby implementation on the JVM (JRuby) calling compiled C extensions through a bridge interface. We demonstrate that cross-language inlining, which is not possible with native code, is performance-critical by showing how speedup is reduced by around 50% when it is disabled.