Effective use of the memory hierarchy is critical for achieving high performance on embedded systems. We focus on the class of streaming applications, which is increasingly prevalent in the embedded domain. We exploit the widespread parallelism and regular communication patterns in stream programs to formulate a set of cache aware optimizations that automatically improve instruction and data locality. Our work is in the context of the Synchronous Dataflow model, in which a program is described as a graph of independent actors that communicate over channels. The communication rates between actors are known at compile time, allowing the compiler to statically model the caching behavior.We present three cache aware optimizations: 1) execution scaling, which judiciously repeats actor executions to improve instruction locality, 2) cache aware fusion, which combines adjacent actors while respecting instruction cache constraints, and 3) scalar replacement, which converts certain data buffers into a sequence of scalar variables that can be register allocated. The optimizations are founded upon a simple and intuitive model that quantifies the temporal locality for a sequence of actor executions. Our implementation of cache aware optimizations in the StreamIt compiler yields a 249% average speedup (over unoptimized code) for our streaming benchmark suite on a StrongARM 1110 processor. The optimizations also yield a 154% speedup on a Pentium 3 and a 152% speedup on an Itanium 2.

In this paper, we develop a new language construct to address one of the pitfalls of parallel programming: precise handling of events across parallel components. The construct, termed teleport messaging, uses data dependences between components to provide a common notion of time in a parallel system. Our work is done in the context of the Synchronous Dataflow (SDF) model, in which computation is expressed as a graph of independent components (or actors) that communicate in regular patterns over data channels. We leverage the static properties of SDF to compute a stream dependence function, SDEP, that compactly describes the ordering constraints between actor executions.Teleport messaging utilizes SDEP to provide powerful and precise event handling. For example, an actor A can specify that an event should be processed by a downstream actor B as soon as B sees the "effects" of the current execution of A. We argue that teleport messaging improves readability and robustness over existing practices. We have implemented messaging as part of the StreamIt compiler, with a backend for a cluster of workstations. As teleport messaging exposes optimization opportunities to the compiler, it also results in a 49% performance improvement for a software radio benchmark.