We describe an analysis-driven storage allocation scheme for concurrent systems that use message passing with copying semantics. The basic principle is that in such a system, data which is not part of any message does not need to be allocated in a shared data area. This allows for the deallocation of thread-specific data without requiring global synchronization and often without even triggering garbage collection. On the other hand, data that is part of a message should preferably be allocated on a shared area since this allows for fast (O(1)) interprocess communication that does not require actual copying. In the context of a dynamically typed, higher-order concurrent functional language, we present a static message analysis which guides the allocation. As shown by our performance evaluation, conducted using a production-quality language implementation, the analysis is effective enough to discover most data which is to be used as a message, and to allow the allocation scheme to combine the best performance characteristics of both a process-centric and a communal memory architecture.

Abstract.We describe an analysis-driven storage allocation scheme for concurrent languages that use message passing with copying semantics. The basic principle is that in such a language, data which is not part of any message does not need to be allocated in a shared data area. This allows for deallocation of threadspecific data without requiring global synchronization and often without even triggering garbage collection. On the other hand, data that is part of a message should preferably be allocated on a shared area, which allows for fast (O(1)) interprocess communication that does not require actual copying. In the context of a dynamically typed, higher-order, concurrent functional language, we present a static message analysis which guides the allocation. As shown by our performance evaluation, conducted using an industrial-strength language implementation, the analysis is effective enough to discover most data which is to be used as a message, and to allow the allocation scheme to combine the best performance characteristics of both a process-centric and a shared-heap memory architecture.

We present a memory management scheme for a concurrent programming language where communication occurs using messagepassing with copying semantics. The runtime system is built around process-local heaps, which frees the memory manager from redundant synchronization in a multithreaded implementation and allows the memory reclamation of process-local heaps to be a private business and to often take place without garbage collection. The allocator is guided by a static analysis which speculatively allocates data possibly used as messages in a shared memory area. To respect the (soft) real-time requirements of the language, we develop a generational, incremental garbage collection scheme tailored to the characteristics of this runtime system. The collector imposes no overhead on the mutator, requires no costly barrier mechanisms, and has a relatively small space overhead. We have implemented these schemes in the context of an industrial-strength implementation of a concurrent functional language used to develop largescale, highly concurrent, embedded applications. Our measurements across a range of applications indicate that the incremental collector substantially reduces pause times, imposes only very small overhead on the total runtime, and achieves a high degree of mutator utilization.

The mark-sweep garbage collection algorithm constructs a list of memory areas to allocate into (the free list) during its sweep phase. This phase needs time proportional to the size of the heap which is collected. We introduce mark-split, a non-moving garbage collection algorithm that constructs the free list during the mark phase by maintaining and splitting free intervals. With mark-split, the sweep phase of mark-sweep becomes unnecessary and the cost of collection is proportional to the size of the live data set. Our performance evaluation, using a high performance Java implementation running standard benchmarks, shows that mark-split can significantly reduce collection times compared with mark-sweep and requires little extra space to do so. The overhead to the cost of marking is moderate and often pays off for itself by avoiding the sweep phase. Since there is no guarantee that this is always the case, we also propose adaptive schemes that try to combine the best performance characteristics of mark-split and mark-sweep collection.

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