Abstract: Applications that operate on sparse data induce irregular data access patterns and cannot take full advantage of caches and prefetching. Novel hardware architectures have been proposed to address the disparity between processor and memory speeds by moving computation closer to memory. One such architecture is the Emu system, which employs light-weight threads that migrate to the location of the data being accessed. While smart heuristics and profile-guided techniques have been developed to derive good data layouts for traditional machines, these methods are largely ineffective when applied to a migratory thread architecture. In this work, we present an application-independent framework for data layout optimizations that targets the Emu architecture. We discuss the necessary tools and concepts to facilitate such optimizations, including a data-centric profiler, data distribution library, and cost model. To demonstrate the framework, we have designed a block placement optimization that distributes blocks of data across the system such that access latency is reduced. The optimization was applied towards sparse matrix-vector multiplication on an Emu FPGA implementation, achieving a geometric mean speed up of 12.5% across 57 matrices. Only one matrix experienced a loss of performance of 6%, while the maximum runtime speedup was 50%.