Concurrency and Scalability versus Fragmentation and Compaction with Compact-fit

We study, formally and experimentally, the trade-off in temporal and spatial performance when managing contiguous pieces of memory using the explicit, dynamic memory management system Compact-fit (CF). The key property of CF is that temporal and spatial performance can be bounded, related, and predicted in constant time through the notion of partial and incremental compaction. Partial compaction determines the maximally tolerated degree of memory fragmentation. Incremental compaction, introduced here, determines the maximal amount of memory involved in any, logically atomic portion of a compaction operation. We explore CF’s potential application space on (1) multiprocessor and multicore systems as well as on (2) memory-constrained uniprocessor systems. For (1), we argue that little or no compaction is likely to avoid the worst case in temporal as well as spatial performance but also observe that scalability only improves by a constant factor. Scalability can be further improved significantly by reducing overall data sharing through separate instances of Compact-fit. For (2), we observe that incremental compaction can effectively trade-off throughput and memory fragmentation for lower latency.