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Automatically Generating Symbolic Prefetches for Distributed Transactional Memories

Abstract : Developing efficient distributed applications while managing complexity can be challenging. Managing network latency is a key challenge for distributed applications. We propose a new approach to prefetching, symbolic prefetching, that can prefetch remote objects before their addresses are known. Our approach was designed to hide the latency of accessing remote objects in distributed transactional memory and a wide range of distributed object middleware frameworks. We present a static compiler analysis for the automatic generation of symbolic prefetches -- symbolic prefetches allow objects whose addresses are unknown to be prefetched. We evaluate this prefetching mechanism in the context of a middleware framework for distributed transactional memory. Our evaluation includes microbenchmarks, scientific benchmarks, and distributed benchmarks. Our results show that symbolic prefetching combined with caching can eliminate an average of 87% of remote reads. We measured speedups due to prefetching of up to 13.31× for accessing arrays and 4.54× for accessing linked lists.
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Alokika Dash, Brian Demsky. Automatically Generating Symbolic Prefetches for Distributed Transactional Memories. ACM/IFIP/USENIX 11th International Middleware Conference (MIDDLEWARE), Nov 2010, Bangalore, India. pp.355-375, ⟨10.1007/978-3-642-16955-7_18⟩. ⟨hal-01055276⟩



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