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Distributed and parallel applications often require accessing shared data. Distributed transactional memory is an emerging concept for concurrent shared data access. By using optimistic synchronization, transactional memory is simpler to use and less error-prone than explicit lock-based synchronization. However, distributed transactional memories are particularly sensitive to phenomena such as true sharing and false sharing, which are caused by correlated data access patterns on multiple nodes. In this paper, we propose a transparent technique that adaptively manages conflict unit sizes for distributed optimistic synchronization in order to relieve application developers from reasoning about such sharing phenomena. Experiments with micro-benchmarks and an on-line data processing application similar to Twitter (using the MapReduce computing model) show the benefits of the proposed approach.