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Streaming dataflow applications are an attractive target to parallelize on wide-SIMD processors such as GPUs. These applications can be expressed as a pipeline of compute nodes connected by edges, which feed outputs from one node to the next. Streaming applications often exhibit irregular dataflow, where the amount of output produced for one input is unknowna priori. Inserting finite queues between pipeline nodes can ameliorate the impact of irregularity and improve SIMD lane occupancy. The sizing of these queues is driven by both performance and safety considerations- relative queue sizes should be chosen to reduce runtime overhead and maximize throughput, but each node’s output queue must be large enough to accommodate the maximum number of outputs produced by one SIMD vector of inputs to the node. When safety and performance considerations conflict, the application may incur excessive memory usage and runtime overhead. In this work, we identify properties of applications that lead to such undesirable behaviors, with examples from applications implemented in our MERCATOR framework for irregular streaming on GPUs. To address these issues, we propose extensions to supportinterruptible nodesthat can be suspended mid-execution if their output queues fill. We illustrate the impacts of adding interruptible nodes to the MERCATOR framework on representative irregular streaming applications from the domains of branching search and bioinformatics.