JavaScript is the most popular programming language for client-side Web applications, and Node.js has popularized the language for server-side computing, too. In this domain, the minimal support for parallel programming remains however a major limitation. In this paper we introduce a novel parallel programming abstraction called Generic Messages (GEMs). GEMs allow one to combine message passing and shared-memory parallelism, extending the classes of parallel applications that can be built with Node.js. GEMs have customizable semantics and enable several forms of thread safety, isolation, and concurrency control. GEMs are designed as convenient JavaScript abstractions that expose high-level and safe parallelism models to the developer. Experiments show that GEMs outperform equivalent Node.js applications thanks to their usage of shared memory.

Systems based on event-loops have been popularized by Node.JS, and are becoming a key technology in the domain of cloud computing. Despite their popularity, such systems support only share-nothing parallelism via message passing between parallel entities usually called workers. In this paper, we introduce a novel parallel programming abstraction called Generic Messages (GEMs), which enables shared-memory parallelism for share-nothing event-based systems. A key characteristic of GEMs is that they enable workers to share state by specifying how the state can be accessed once it is shared. We call this aspect of the GEMs model capability-based parallelism.

Researchers often rely on benchmarks to demonstrate feasibility or efficiency of their contributions. However, finding the right benchmark suite can be a daunting task - existing benchmark suites may be outdated, known to be flawed, or simply irrelevant for the proposed approach. Creating a proper benchmark suite is challenging, extremely time consuming, and also - unless it becomes widely popular - a thankless endeavor. In this paper, we introduce a novel approach to help researchers find relevant workloads for their experimental evaluation needs. Our approach relies on the huge number of open-source projects available in public repositories, and on unit testing having become best practice in software development. Using a repository crawler employing pluggable static and dynamic analyses for filtering and workload characterization, we allow users to automatically find projects with relevant workloads. Preliminary results presented here show that unit tests can provide a viable source of workloads, and that the combination of static and dynamic analyses improves the ability to identify relevant workloads that can serve as the basis for custom benchmark suites.