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This paper explores collective personalized communication. For example, in all-to-all personalize d communication (AAPC), each processor sends a distinct message to every other processor. However, for many applica tions, the collec- tive communication pattern is many-to-many, where each processor sends a distinct message to a subs et of processors. In this paper we first present strategies that reduce per-message cost to optimize AAPC. We then prese nt performance results of these strategies in both all-to-all and many-to-many scenarios. These strategies are implemented in a flexible, asynchronous library with a non-blocking interface, and a message-driven runtime system. This allows the collect ive communication to run concurrently with the application, if desired. As a result the computational overhead of the commun ication is substantially reduced, at least on machines such as PSC Lemieux, which sport a co-processor capable of remote DMA . We demonstrate the advantages of our framework with performance results on several benchmarks and applications.