Abstract-Synchronous dataflow graphs (SDFGs) are used extensively to model streaming applications. An SDFG can be extended with scheduling decisions, allowing SDFG analysis to obtain properties like throughput or buffer sizes for the scheduled graphs. Analysis times depend strongly on the size of the SDFG. SDFGs can be statically scheduled using static-order schedules. The only generally applicable technique to model a staticorder schedule in an SDFG is to convert it to a homogeneous SDFG (HSDFG). This conversion may lead to an exponential increase in the size of the graph and to sub-optimal analysis results (e.g., for buffer sizes in multi-processors). We present a technique to model periodic static-order schedules directly in an SDFG. Experiments show that our technique produces more compact graphs compared to the technique that relies on a conversion to an HSDFG. This results in reduced analysis times for performance properties and tighter resource requirements.

Abstract-Dynamic behavior of streaming applications can be effectively modeled by scenario-aware dataflow graphs (SADFs). Many streaming applications must provide timing guarantees (e.g., throughput) to assure their quality-of-service. For instance, a video decoder which is running on a mobile device is expected to deliver a video stream with a specific frame rate. Moreover, the energy consumption of such applications on handheld devices should be as low as possible. This paper proposes a technique to select a suitable multiprocessor DVFS point for each mode (scenario) of a dynamic application described by an SADF. The technique assures strict timing guarantees while minimizing energy consumption. The technique is evaluated by applying it to several streaming applications. It solves the problem faster than the state of the art technique for dataflow graphs. Moreover, the DVFS controller devised using the proposed technique is more compact and reduces energy consumption compared to the controller devised using the counterpart technique.

Abstract-Scenario-aware dataflow graphs (SADFs) efficiently model dynamic applications. The throughput of an application is an important metric to determine the performance of the system. For example, the number of frames per second output by a video decoder should always stay above a threshold that determines the quality of the system. During design-space exploration (DSE) or run-time management (RTM), numerous throughput calculations have to be performed. Throughput calculations have to be performed as fast as possible. For synchronous dataflow graphs (SDFs), a technique exists that extracts throughput expressions from a parameterized SDF in which the execution time of the tasks (actors) is a function of some parameters. Evaluation of these expressions can be done in a negligible amount of time and provides the throughput for a specific set of parameter values. This technique is not applicable to SADFs. In this paper, we present a technique, based on Max-Plus automata, that finds throughput expressions for a parameterized SADF. Experimental evaluation shows that our technique can be applied to realistic applications. These results also show that our technique is better scalable and faster compared to the available parametric throughput analysis technique for SDFs. I. INTRODUCTIONSignal processing and multimedia applications can be modeled with synchronous dataflow graphs (SDFs) [1]- [4]. An SDF can be analyzed to determine performance properties (e.g., throughput [5]) or resource requirements (e.g., buffer sizes [6]) of the underlying application. However, SDFs cannot efficiently capture the dynamic behavior of modern streaming applications (e.g., audio or video codecs with advanced compression schemes) because of their static nature. Using SDFs to model such applications with high dynamism cannot assure tight performance guarantees. Scenario-aware dataflow graphs (SADFs) [7] are have been introduced to relax this limitation of SDFs. An SADF of an application is composed of several SDFs and a finite state machine (FSM). Each mode (scenario) of the application in the SADF is modeled by an SDF; the FSM captures the order of scenario occurrence. In [8], it is shown that SDF throughput analysis (e.g., [5]) can result in pessimistic performance bounds. The paper introduces a novel technique to determine a tighter throughput bound for applications modeled with an SADF. The approach extracts a Max-Plus automaton graph (MPAG) from an SADF and then uses a maximum cycle mean algorithm to determine the critical timing cycle of the extracted MPAG.The timing behavior of an application depends on its binding, scheduling, buffer allocation, etc. Dynamic voltage and frequency scaling (DVFS), which is a commonly used

Abstract-Synchronous dataflow graphs (SDFGs) are used extensively to model streaming applications. An SDFG can be extended with scheduling decisions, allowing SDFG analysis to obtain properties like throughput or buffer sizes for the scheduled graphs. Analysis times depend strongly on the size of the SDFG. SDFGs can be statically scheduled using static-order schedules. The only generally applicable technique to model a static-order schedule in an SDFG is to convert it to a homogeneous SDFG (HSDFG). This may lead to an exponential increase in the size of the graph and to sub-optimal analysis results (e.g., for buffer sizes in multi-processors). We present techniques to model two types of static-order schedules, i.e., periodic schedules and periodic single appearance schedules, directly in an SDFG. Experiments show that both techniques produce more compact graphs compared to the technique that relies on a conversion to an HSDFG. This results in reduced analysis times for performance properties and tighter resource requirements.

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