Dynamic programming languages such as Python and Ruby are widely used, and much effort is spent on making them efficient. One substantial research effort in this direction is the enabling of parallel code execution. While there has been significant progress, making dynamic collections efficient, scalable, and thread-safe is an open issue. Typical programs in dynamic languages use few but versatile collection types. Such collections are an important ingredient of dynamic environments, but are difficult to make safe, efficient, and scalable. In this paper, we propose an approach for efficient and concurrent collections by gradually increasing synchronization levels according to the dynamic needs of each collection instance. Collections reachable only by a single thread have no synchronization, arrays accessed in bounds have minimal synchronization, and for the general case, we adopt the Layout Lock paradigm and extend its design with a lightweight version that fits the setting of dynamic languages. We apply our approach to Ruby's Array and Hash collections. Our experiments show that our approach has no overhead on single-threaded benchmarks, scales linearly for Array and Hash accesses, achieves the same scalability as Fortran and Java for classic parallel algorithms, and scales better than other Ruby implementations on Ruby workloads.

Comparing the performance of programming languages is difficult because they differ in many aspects including preferred programming abstractions, available frameworks, and their runtime systems. Nonetheless, the question about relative performance comes up repeatedly in the research community, industry, and wider audience of enthusiasts.

We are in the multi-core era. Dynamically-typed languages are in widespread use, but their support for multithreading still lags behind. One of the reasons is that the sophisticated techniques they use to efficiently represent their dynamic object models are often unsafe in multithreaded environments.