Software Distributed Shared Memory (DSM) provides a shared-memory abstraction on distributed memory hardware, making a parallel programmer's task easier. Unfortunately, software DSM is less efficient than the direct use of the underlying message-passing hardware. The chief reason for this is that hand-coded and compiler-generated message-passing programs typically achieve better data aggregation in their messages than programs using software DSM. Software DSM has poorer data aggregation because the system lacks the knowledge of the application's behavior that a programmer or compiler analysis can provide. We propose four new techniques to perform automatic data aggregation in software DSM. Our techniques use run-time analysis of past data-fetch accesses made by a processor, to aggregate data movement for future accesses. They do not need any additional compiler support. We implemented our techniques in the TreadMarks software DSM system. We used a test suite of four applications--3D-FFT, Barnes-Hut, Ilink and Shallow. For these applications we obtained 40% to 66% reduction in message counts which resulted in 6% to 19% improvement in execution times.