This paper describes a new garbage collection scheme for large persistent object stores that makes efficient use of the disk and main memory. The heap is divided into partitions that are collected independently using information about inter-partition references. We present efficient techniques to maintain this information stably using auxiliary data structures in memory and the log. The result is a scheme that truly preserves the localized and scalable nature of partitioned collection. Remembering

Conventional parallel computer architectures do not provide support for nonuniformly distributed objects. In this thesis, I introduce sparsely faceted arrays (SFAs), a new low-level mechanism for naming regions of memory, or facets, on different processors in a distributed, shared memory parallel processing system. Sparsely faceted arrays address the disconnect between the global distributed arrays provided by conventional architectures (e.g. the Cray T3 series), and the requirements of high-level parallel programming methods that wish to use objects that are distributed over only a subset of processing elements. A sparsely faceted array names a virtual globally-distributed array, but actual facets are lazily allocated. By providing simple semantics and making efficient use of memory, SFAs enable efficient implementation of a variety of non-uniformly distributed data structures and related algorithms. I present example applications which use SFAs, and describe and evaluate simple hardware mechanisms for implementing SFAs.