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Current high-performance multicore processors provide users with a non-uniform memory access model (NUMA). These systems perform better when threads access data on mem-ory banks next to the core where they run. However, en-suring data locality is dicult. In this paper, we propose compiler analyses and code generation methods to support a lightweight runtime system that dynamically migrates mem-ory pages to improve data locality. Our technique combines static and dynamic analyses and is capable of identifying the most promising pages to migrate. Statically, we infer the size of arrays, plus the amount of reuse of each memory access instruction in a program. These estimates rely on a simple, yet accurate, trip count predictor of our own design. This knowledge let’s us build templates of dynamic checks, to be filled with values known only at runtime. These checks determine when it is profitable to migrate data closer to the processors where this data is used. Our static analyses are quadratic on the number of variables in a program, and the dynamic checks are O(1) in practice. Our technique does not require any form of user intervention, neither the sup-port of a third-party middleware, nor modifications in the operating system’s kernel. We have applied our technique on several parallel algorithms, which are completely oblivi-ous to the asymmetric memory topology, and have observed speedups of up to 4x, compared to static heuristics. We compare our approach against Minas, a middleware that supports NUMA-aware data allocation, and show that we can outperform it by up to 50 % in some cases. Categories and Subject Descriptors D.3.4 [Software]: Programming Languages—Compiler