MASES
| Autor: | Andreas Gerstlauer, Jacob Kornerup, Wenxiao Yu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Dataflow Data stream mining Distributed computing Resource constraints 02 engineering and technology Graph 020202 computer hardware & architecture Scheduling (computing) 020204 information systems 0202 electrical engineering electronic engineering information engineering Latency (engineering) Heuristics Implementation |
| Zdroj: | SCOPES |
| DOI: | 10.1145/3207719.3207733 |
| Popis: | Dataflow and task graph descriptions are widely used for mapping and scheduling of real-time streaming applications onto heterogeneous processing platforms. Such applications are often characterized by the need to process large-volume data streams with not only high throughput, but also low latency. Mapping such application descriptions into tightly constrained implementations requires optimization of pipelined scheduling of tasks on different processing elements. This poses the problem of finding an optimal solution across a latency-throughput objective space. In this paper, we present a novel list-scheduling based heuristic called MASES for pipelined dataflow scheduling to minimize latency under throughput and heterogeneous resource constraints. MASES explores the flexibility provided by mobility and slack of actors in a partial schedule. It can find a valid schedule if one exists even under tight throughput and resource constraints. Furthermore, MASES can improve runtime by up to 4x while achieving similar results as other latency-oriented heuristics for problems they can solve. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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