Speculative multithreading (SpMT) is an effective execution model for parallelizing non-numeric programs, which tend to use irregular and pointer-intensive data structures, and have complex flows of control. An SpMT compiler performs program partitioning by carefully considering the data dependencies present in the program. However, at run-time, the data dependency picture changes dramatically if the SpMT hardware performs data value prediction. Many of the data dependencies, which guided the compiler’s partitioning algorithm in taking decisions, may lose their relevance due to successful data value prediction. This paper presents a compiler framework that uses profile-based value predictability information when making program partitioning decisions. We have developed a Value Predictability Profiler (VPP) that generates the value prediction statistics for the source variables in a program. Our SpMT compiler utilizes this information by ignoring the data dependencies due to variables with high prediction accuracies. The compiler can thus perform more efficient thread formation. This SpMT compiler framework is implemented on the SUIF-MachSUIF platform. A simulation-based evaluation of SPEC programs shows that the speedup with 6 processing elements increases up to 21% when utilizing value predictability information during program partitioning.