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
\emph{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 \emph{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$\times$, improves performance by 3$\times$,
increases IPC by 58%, and reduces dynamic instructions by 44%.