To fully utilize multicore processors in Node.js applications, the applications must be programmed as multiple processes. Parallel execution can increase the throughput of data and hence lower data buffering for inter-process communica- tion. Node.js’s asynchronous programming model and interface to the operating system make for convenient tools that are well suited for multiprocess program- ming. However, the run-time behavior of asynchronous processes results in non-deterministic processor load and data flow. That means the performance gain from increasing concurrency depends on both the application’s run-time state and the hardware’s capacity for parallel execution. The objective of this thesis work is to explore the effects of increasing parallel- ism in Node.js applications by measuring the differences in the amount of data buffering when distributed processes run of a varying number of cores with a fixed rate of asynchronously arriving data. The goal is to simulate and examine the run-time behavior of three basic multiprocess Node.js application architec- tures in order to discuss and evaluate software parallelism techniques. The three architectures are: pipelined nodes for temporally dependent processing, a vector of nodes for data parallel processing, and a grid of nodes for one-to-many branched processing. To simulate and visualize the run-time behavior, a simulation environment us- ing multiple Node.js processes is created. The simulation is agent-based, where the agent is an abstraction for a specific data flow within the application. The simulation models and visualizes all of the data flows within a distributed appli- cation where processes communicate asynchronously via messages through sockets. The results show that performance can increase when distributing Node.js ap- plications across multiple processes running in parallel on multicore hardware. There are however diminishing returns as the number of active processes equal or exceed the number of cores. A good rule of thumb seem to be to distribute the decoupled logic across as many processes as there are cores. The interaction between asynchronous processes is on the whole made very simple with Node.js. Although running multiple instances of Node.js requires more memory, the distributed architecture has the potential to increase performance by nearly as many times as the number of cores in the processor.