Reducing Queuing Impact in Irregular Data Streaming Applications

TLDR: In this article, the authors studied irregular data flow applications with data-dependent and unknown a priori data flow and proposed a dynamic programming approach to minimize the frequency of switching between nodes.

Abstract: Throughput-oriented streaming applications on massive data sets are a prime candidate for parallelization on wide-SIMD platforms, especially when inputs are independent of one another. Many such applications are represented as a pipeline of compute nodes connected by directed edges. Here, we study applications with irregular data flow, i.e., those where the number of outputs produced per input to a node is data-dependent and unknown a priori. Moreover, we target these applications to architectures (GPUs) where different nodes of the pipeline execute cooperatively on a single wide-SIMD processor. To promote greater SIMD parallelism, irregular application pipelines can utilize queues to gather and compact multiple data items between nodes. However, the decision to introduce a queue between two nodes must trade off benefits to occupancy against costs associated with queue reading, writing, and management. Moreover, once queues are introduced to an application, their relative sizes impact the frequency with which the application switches between nodes, incurring scheduling and context-switching overhead. This work examines two optimization problems associated with queues. First, we consider which pairs of successive nodes in a pipeline should have queues between them to maximize overall application throughput. Second, given a fixed total budget for queue space, we consider how to choose the relative sizes of inter-node queues to minimize the frequency of switching between nodes. We formulate a dynamic programming approach to the first problem and give an empirically useful approximation to the second that allows for an analytical solution. Finally, we validate our theoretical results using real-world irregular streaming computations.