Partial memoization of concurrency and communication

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Partial memoization of concurrency and communication

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International Conference on Functional Programming archive
Proceedings of the 14th ACM SIGPLAN international conference on Functional programming table of contents

Edinburgh, Scotland

SESSION: Session 7 table of contents

Pages 161-172

Year of Publication: 2009

ISBN:978-1-60558-332-7

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Authors

Lukasz Ziarek	Purdue University, West Lafayette, IN, USA
KC Sivaramakrishnan	Purdue University, West Lafayette, IN, USA
Suresh Jagannathan	Purdue University, West Lafayette, IN, USA

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ACM: Association for Computing Machinery
SIGPLAN: ACM Special Interest Group on Programming Languages

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ACM New York, NY, USA

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ABSTRACT

Memoization is a well-known optimization technique used to eliminate redundant calls for pure functions. If a call to a function f with argument v yields result r, a subsequent call to f with v can be immediately reduced to r without the need to re-evaluate f's body. Understanding memoization in the presence of concurrency and communication is significantly more challenging. For example, if f communicates with other threads, it is not sufficient to simply record its input/output behavior; we must also track inter-thread dependencies induced by these communication actions. Subsequent calls to f can be elided only if we can identify an interleaving of actions from these call-sites that lead to states in which these dependencies are satisfied. Similar issues arise if f spawns additional threads. In this paper, we consider the memoization problem for a higher-order concurrent language whose threads may communicate through synchronous message-based communication. To avoid the need to perform unbounded state space search that may be necessary to determine if all communication dependencies manifest in an earlier call can be satisfied in a later one, we introduce a weaker notion of memoization called partial memoization that gives implementations the freedom to avoid performing some part, if not all, of a previously memoized call. To validate the effectiveness of our ideas, we consider the benefits of memoization for reducing the overhead of recomputation for streaming, server-based, and transactional applications executed on a multi-core machine. We show that on a variety of workloads, memoization can lead to substantial performance improvements without incurring high memory costs.

REFERENCES

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