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Spatial dataflow architectures (SDAs) are a promising and versatile accelerator platform. They are software-programmable and achieve near-ASIC performance and energy efficiency, beating CPUs by orders of magnitude. Unfortunately, many SDAs struggle to efficiently implement irregular computations because they suffer from an abstraction inversion: they fail to capture coarse-grain dataflow semantics in the application — namely asynchronous communication, pipelining, and queueing — that are naturally supported by the dataflow execution model and existing SDA hardware. Ripple is a language and architecture that corrects the abstraction inversion by preserving dataflow semantics down the stack. Ripple provides asynchronous iterators, shared-memory atomics, and a familiar task-parallel interface to concisely express the asynchronous pipeline parallelism enabled by an SDA. Ripple efficiently implements deadlock-free, asynchronous task communication by exposing hardware token queues in its ISA. Across nine important workloads, compared to a recent ordered-dataflow SDA, Ripple shrinks programs by 1.9×, improves performance by 3×, increases IPC by 58%, and reduces dynamic instructions by 44%. 

Coarse-grained reconfigurable arrays (CGRAs) have gained attention in recent years due to their promising power efficiency compared to traditional von Neumann architectures. To program these architectures using ordinary languages such as C, a dataflow compiler must transform the original sequential, imperative program into an equivalent dataflow graph, composed of dataflow operators running in parallel. This transformation is challenging since the asynchronous nature of dataflow graphs allows out-of-order execution of operators, leading to behaviors not present in the original imperative programs. We address this challenge by developing a translation validation technique for dataflow compilers to ensure that the dataflow program has the same behavior as the original imperative program on all possible inputs and schedules of execution. We apply this method to a state-of-the-art dataflow compiler targeting the RipTide CGRA architecture. Our tool uncovers 8 compiler bugs where the compiler outputs incorrect dataflow graphs, including a data race that is otherwise hard to discover via testing. After repairing these bugs, our tool verifies the correct compilation of all programs in the RipTide benchmark suite.