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Abstract 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. 

Streaming computations on massive data sets are an attractive candidate for parallelization, particularly when they exhibit independence (and hence data parallelism) between items in the stream. However, some streaming computations are stateful, which disrupts independence and can limit parallelism. In this work, we consider how to extract data parallelism from streaming computations with a common, limited form of statefulness. The stream is assumed to be divided into variably-sized regions, and items in the same region are processed in a common context of state. In general, the computation to be performed on a stream is also irregular, with each item potentially undergoing different, data-dependent processing. This work describes mechanisms to implement such computations efficiently on a SIMD-parallel architecture such as a GPU. We first develop a low-level protocol by which a data stream can be augmented with control signals that are delivered to each stage of a computation at precise points in the stream. We then describe an abstraction, enumeration and aggregation, by which an application developer can specify the behavior of a streaming application with region-based state. Finally, we study an implementation of our ideas as part of the MERCATOR system for irregular streaming computations on GPUs, investigating how the frequency of region boundaries in a stream impacts SIMD occupancy and hence application performance.