Picking the right data structure for the right job is one of the key challenges for every developer. However, especially in the realm of object-oriented programming, the memory layout of data structures is often still suboptimal for certain data access patterns, due to objects being scattered across the heap. Therefore, this work presents an approach for the automated transformation of arrays of objects into a contiguous format (called columnar arrays). At run time, we identify suitable arrays, perform the transformation and use a dynamic compiler to gain performance improvements. In the evaluation, we show that our approach can improve the performance of certain queries over large, uniform arrays.

Today’s huge memories make it possible to store and process large data structures in memory instead of in a database. Hence, accesses to this data should be optimized, which is normally relegated either to the runtimes and compilers or is left to the developers, who often lack the knowledge about optimization strategies. As arrays are often part of the language, developers frequently use them as an underlying storage mechanism. Thus, optimization of arrays may be vital to improve performance of data-intensive applications. While compilers can apply numerous optimizations to speed up accesses, it would also be beneficial to adapt the actual layout of the data in memory to improve cache utilization. However, runtimes and compilers typically do not perform such memory layout optimizations. In this work, we present an approach to dynamically perform memory layout optimizations on arrays of objects to transform them into a columnar memory layout, a storage layout frequently used in analytical applications that enables faster processing of read-intensive workloads. By integration into a state-of-the-art JavaScript runtime, our approach can speed up queries for large workloads by up to 9x, where the initial transformation overhead is amortized over time.

Tool support is essential to help developers in understanding the memory behavior of complex software systems. Anomalies such as memory leaks can dramatically impact application performance and can even lead to crashes. Unfortunately, most memory analysis tools lack advanced visualizations (especially of the memory evolution over time) that could facilitate developers in analyzing suspicious memory behavior.In this paper, we present Memory Cities, a technique to visualize an application's heap memory evolution over time using the software city metaphor. While this metaphor is typically used to visualize static artifacts of a software system such as class hierarchies, we use it to visualize the dynamic memory behavior of an application. In our approach, heap objects can be grouped by multiple properties such as their types or their allocation sites. The resulting object groups are visualized as buildings arranged in districts, where the size of a building corresponds to the number of heap objects or bytes it represents. Continuously updating the city over time creates the immersive feeling of an evolving city. This can be used to detect and analyze memory leaks, i.e., to search for suspicious growth behavior. Memory cities further utilize various visual attributes to ease this task. For example, they highlight strongly growing buildings using color, while making less suspicious buildings semi-transparent.We implemented memory cities as a standalone application developed in Unity, with a JSON-based interface to ensure easy data import from external tools. We show how memory cites can use data provided by AntTracks, a trace-based memory monitoring tool, and present case studies on different applications to demonstrate the tool's applicability and feasibility.