{'fraction_non_alphanumeric': 0.023730070448646643, 'fraction_numerical': 0.006303299962921765, 'mean_word_length': 5.596577017114915, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Parameterized Modeling and Scheduling of Dataflow Graphs

Dataflow has proven to be an attractive computational model for programming DSP applications. A restricted version of dataflow, called Synchronous Dataflow (SDF) is particularly well-suited for modeling a large class of signal processing applications, as it offers strong formal properties and compile-time predictability. Efficient techniques have been developed for generating software implementations from an SDF graph that are geared towards various optimization objectives. However, the SDF model does not allow data-dependent flow of control or dynamically varying communication patterns between functional modules. This results in limited expressive power. Consequently, a variety of extensions to SDF have been developed, where the objective is to provide increased expressive power, while maintaining a significant part of the compile-time predictability of SDF, e.g., boolean dataflow, cyclo-dynamic dataflow, and bounded dynamic dataflow. In this report, we propose a parameterized dataflow framework that can be applied as a meta-modeling technique to an arbitrary dataflow model that satisfies certain requirements, to further increase its expressive power. Parameterized modeling imposes a hierarchy discipline on an underlying dataflow model, where the behavior of a subsystem is controlled by a set of parameters, which can control, among others, the functional behavior as well as token flow behavior of a dataflow graph. The parameter coordination mechanism is such that every parameterized graph always behaves like a graph in the underlying dataflow model during each of its invocations, but can assume different configurations across invocations. Thus, any dataflow model that possesses a concept of a graph invocation (iteration) can be parameterized in this fashion. For clarity, we focus on synchronous dataflow, and develop the precise semantics of parameterized synchronous dataflow (PSDF). We propose a formal framework for the PSDF model, and introduce the concept of local synchrony, which is a condition that must be satisfied for consistent execution of PSDF specifications. We derive precise relationships between local synchrony and the manner in which subsystem parameters are dynamically configured in PSDF. Based on these, we define a precise operational semantics for the PSDF model. From our experience, it appears that the PSDF model significantly 1. This research was sponsored by Northrop Grumman Corporation and the National Science Foundation (CAREER MIP9734275). Technical Report #UMIACS-TR-99-73, Institute for Advanced Computer Studies, University of Maryland at College Park, December 2, 1999