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Concurrent data structures such as concurrent queues, stacks, and pools are widely used for concurrent programming of shared-memory multiprocessor and multicore machines. The key challenge is to develop data structures that are not only fast on a given machine but whose performance scales, ideally linearly, with the number of threads, cores, and processors on even bigger machines. Part of that challenge is to provide a common ground for systematically evaluating the performance and scalability of new concurrent data structures and comparing the results with the performance and scalability of existing solutions. For this purpose, we have developed Scal which is an open-source benchmarking framework that provides (1) software infrastructure for executing concurrent data structure algorithms, (2) workloads for benchmarking their performance and scalability, and (3) implementations of a large set of concurrent data structures. We discuss the Scal infrastructure, workloads, and implementations, and encourage further use and development of Scal in the design and implementation of ever faster concurrent data structures.

We present ACDC-JS, an open-source JavaScript memory management benchmarking tool. ACDC-JS incorporates a heap model based on real web applications and may be configured to expose virtually any relevant performance characteristics of JavaScript memory management systems. ACDC-JS is based on ACDC, a benchmarking tool for C/C++ that models periodic allocation and deallocation behavior (AC) as well as persistent memory (DC). We identify important characteristics of JavaScript mutator behavior and propose a configurable heap model based on typical distributions of these characteristics as foundation for ACDC-JS. We describe heap analyses of 13 real web applications extending existing work on JavaScript behavior analysis. Our experimental results show that ACDC-JS enables performance benchmarking and debugging of state-of-the-art JavaScript virtual machines such as V8 and SpiderMonkey by exposing key aspects of their memory management performance.

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