You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We demonstrate that general-purpose memory allocation involving many threads on many cores can be done with high performance, multicore scalability, and low memory consumption. For this purpose, we have designed and implemented scalloc, a concurrent allocator that generally performs and scales in our experiments better than other allocators while using less memory, and is still competitive otherwise. The main ideas behind the design of scalloc are: uniform treatment of small and big objects through so-called virtual spans, efficiently and effectively reclaiming free memory through fast and scalable global data structures, and constant-time (modulo synchronization) allocation and deallocation operations that trade off memory reuse and spatial locality without being subject to false sharing.

Cited Abstract(s):
[1]: Increasingly popular languages such as Java and C# require efficient garbage collection. This paper presents the design, implementation, and evaluation of MMTk, a Memory Management Toolkit for and in Java. MMTk is an efficient, composable, extensible, and portable framework for building garbage collectors. MMTk uses design patterns and compiler cooperation to combine modularity and efficiency. The resulting system is more robust, easier to maintain, and has fewer defects than monolithic collectors. Experimental comparisons with monolithic Java and C implementations reveal MMTk has significant performance advantages as well. Performance critical system software typically uses monolithic C at the expense of flexibility. Our results refute common wisdom that only this approach attains efficiency, and suggest that performance critical software can embrace modular design and high-level languages.
A concept related to virtual spans called spaces appears in the Memory Management Toolkit (MMTk) for managed languages [1] . We note that the generally poor performance of SuperMalloc for concurrent workloads shows that virtual spans alone do not suffice for achieving competitive temporal and spatial performance and scalability. Virtual spans alone only simplify allocator design as they enable uniform treatment of small and big objects, and reduce memory consumption. As shown in our experimental evaluation, the combination of virtual spans with a scalable backend and a high-performance frontend is crucial for achieving competitive performance and scalability.