Abstract : Manual program parallelization and optimization may be necessary to reach a decent portion of the target architecture's peak performance when automatic tools fail at choosing the best strategy. While a broad range of languages and libraries provide convenient ways to express parallelism, the difficult, time consuming and error-prone parallelism identification and extraction task is mostly left under the programmer's responsibility. To address this issue, we introduce a visualization-based approach to ease parallelism extraction and expression that leverages polyhedral compilation technologies. Our interactive tool, Clint, maps direct manipulation of the visual representation to polyhedral program transformations with real-time semantics preservation feedback. We conducted two user studies showing that Clint's visualization can be accurately understood by both experts and non-expert programmers, and that the parallelism can be extracted better from Clint's representation than from the source code in many cases.