A Gentle Introduction to S-Net: Typed Stream Processing and Declarative Coordination of Asynchronous Components

Grelck, Clemens; Scholz, Sven-Bodo; Shafarenko, Alex

doi:10.1142/s0129626408003351

Cited by 36 publications

(29 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As our benchmark, we chose a fast fourier transform (FFT), face detection (FD), and data encryption standard (DES) applications implemented using the S-Net [22] streaming language.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic power management for reactive stream processing on the SCC tiled architecture

et al. 2016

View full text Add to dashboard Cite

Dynamic voltage and frequency scaling (DVFS) is a means to adjust the computing capacity and power consumption of computing systems to the application demands. DVFS is generally useful to provide a compromise between computing demands and power consumption, especially in the areas of resource-constrained computing systems. Many modern processors support some form of DVFS. In this article, we focus on the development of an execution framework that provides lightweight DVFS support for reactive stream processing systems (RSPS). RSPs are a common form of embedded control systems, operating in direct response to inputs from their environment. At the execution framework, we focus on support for many-core scheduling for parallel execution of concurrent programs. We provide a DVFS strategy for RSPs that is simple and lightweight, to be used for dynamic adaptation of the power consumption at runtime. The simplicity of the DVFS strategy became possible by the sole focus on the application domain of RSPs. The presented DVFS strategy does not require specific assumptions about the message arrival rate or the underlying scheduling method. While DVFS is a very active field, in contrast to most existing research, our approach works also for platforms like many-core processors, where the power settings typically cannot be controlled individually for each computational unit. We also support dynamic scheduling with variable workload. While many research results are provided with simulators, in our approach, we present a parallel execution framework with experiments conducted on real hardware, using the single-chip cloud computer many-core processor. The results of our experimental evaluation confirm that our simple DVFS strategy provides potential for significant energy saving on RSPs.

show abstract

“…As our benchmark, we chose a fast fourier transform (FFT), face detection (FD), and data encryption standard (DES) applications implemented using the S-Net [22] streaming language.…”

Section: Methodsmentioning

confidence: 99%

“…Nodes can be connected in different patterns, such as pipelines and parallel networks. Some examples of stream programming can be found in [21][22][23].…”

Section: Stream Programmingmentioning

confidence: 99%

Dynamic power management for reactive stream processing on the SCC tiled architecture

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The first comprehensive solution for S-Net was by Grelck and Shafarenko (see [20], where the language definition and some relevant algorithms are presented). Further development of the type system was done by Cai et al [21] and some recent examples of the use of S-Net in applications are found in [18].…”

Section: Related Workmentioning

confidence: 99%

“…To support the parallel component technology being discussed, a coordination language has been designed and implemented [20,18]. The language is called S-Net, which stands for Streaming Networks.…”

Section: Languagementioning

confidence: 99%

Concurrency engineering

Jesshope

Shafarenko²

2008

2008 13th Asia-Pacific Computer Systems Architecture Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…Examples of systems that use the streaming approach include Auto-Pipe [6], [11], S-Net [14], Streams-C [12], and StreamIt [29]. In the streaming paradigm, an application is decomposed into processing kernels, or blocks, which are connected using explicit communication channels.…”

Section: Introductionmentioning

confidence: 99%

ScalaPipe: A Streaming Application Generator

Wingbermuehle

Chamberlain

Cytron

2012

2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines

View full text Add to dashboard Cite

Abstract-ScalaPipe is a streaming application generator for heterogeneous platforms. By using a collection of domain-specific languages (DSLs) embedded in the Scala programming language, ScalaPipe allows creation of streaming applications that can run on a variety of hardware, including traditional processors, graphics processors, and field-programmable gate arrays (FPGAs). Its application DSL allows specification of the application topology and resource mapping. Its block DSL allows the authoring of implementations for processing kernels, or blocks, which are used in the streaming application. ScalaPipe makes it easy to generate, modify, and instrument large, complex topologies and resource mappings while also exposing optimization opportunities.

show abstract

A Gentle Introduction to S-Net: Typed Stream Processing and Declarative Coordination of Asynchronous Components

Cited by 36 publications

References 8 publications

Dynamic power management for reactive stream processing on the SCC tiled architecture

Dynamic power management for reactive stream processing on the SCC tiled architecture

Concurrency engineering

ScalaPipe: A Streaming Application Generator

Contact Info

Product

Resources

About