Memory-constrained Block Processing for DSP Software Optimization

Ko, Ming-Yung; Shen, Chung-Ching; Bhattacharyya, Shuvra S.

doi:10.1007/s11265-007-0114-1

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…For example, the CD-DAT benchmark (shown in Figure 5) is used in many studies [3,4,10,31,39,40,50]; it converts compact disk auto (sampled at 44.1 khz) to digital audio tape (sampled at 48 khz). Performing this conversion in stages improves efficiency [39].…”

Section: Neighboring Filters Often Have Matched I/o Ratesmentioning

confidence: 99%

An empirical characterization of stream programs and its implications for language and compiler design

Thies

Amarasinghe

2010

Proceedings of the 19th International Conference on Parallel Architectures and Compilation Techniques

143

122

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Stream programs represent an important class of high-performance computations. Defined by their regular processing of sequences of data, stream programs appear most commonly in the context of audio, video, and digital signal processing, though also in networking, encryption, and other areas. In order to develop effective compilation techniques for the streaming domain, it is important to understand the common characteristics of these programs. Prior characterizations of stream programs have examined legacy implementations in C, C++, or FORTRAN, making it difficult to extract the high-level properties of the algorithms.In this work, we characterize a large set of stream programs that was implemented directly in a stream programming language, allowing new insights into the high-level structure and behavior of the applications. We utilize the StreamIt benchmark suite, consisting of 65 programs and 33,600 lines of code. We characterize the bottlenecks to parallelism, the data reference patterns, the input/output rates, and other properties. The lessons learned have implications for the design of future architectures, languages and compilers for the streaming domain.

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Section: Neighboring Filters Often Have Matched I/o Ratesmentioning

confidence: 99%

An empirical characterization of stream programs and its implications for language and compiler design

Thies

Amarasinghe

2010

Proceedings of the 19th International Conference on Parallel Architectures and Compilation Techniques

143

122

View full text Add to dashboard Cite

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“…Fundamentals of vectorized execution for dataflow actors have been developed by Ritz [Ritz 1993] and explored further by Zivojnovic [Zivojnovic 1994], Lalgudi [Lalgudi 2000], and Ko [Ko 2008]. In such vectorization, multiple firings of the same actor are grouped together for execution to reduce the rate of context switching, enhance locality, and improve processor pipeline utilization.…”

Section: Methodsmentioning

confidence: 99%

Design Tools for Accelerating Development and Usage of Multi-Core Computing Platforms

Bhattacharyya¹

2014

Self Cite

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“…Fundamentals of vectorized execution for dataflow actors have been developed by Ritz [29], and explored further by Zivojnovic [30], Lalgudi [31], and Ko [32].…”

Section: Gpu Implementation Case Studymentioning

confidence: 99%

Application-Specific Instruction Set Processors for Video Processing

Kim¹,

Sunwoo²

2017

Multimedia Image and Video Processing

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Dataflow is a well known computational model and is widely used for expressing the functionality of digital signal processing (DSP) applications, such as audio and video data stream processing, digital communications, and image processing. These applications usually require real-time processing capabilities and have critical performance constraints. Dataflow provides a formal mechanism for describing specifications of DSP applications, imposes minimal datadependency constraints in specifications, and is effective in exposing and exploiting task or data level parallelism for achieving high performance implementations.To demonstrate dataflow-based design methods in a manner that is concrete and easily adapted to different platforms and back-end design tools, we present in this report a number of case studies based on the lightweight dataflow (LWDF) programming methodology. LWDF is designed as a "minimalistic" approach for integrating coarse grain dataflow programming structures into arbitrary simulation-or platform-oriented languages, such as C, C++, CUDA, MATLAB, SystemC, Verilog, and VHDL. In particular, LWDF requires minimal dependence on specialized tools or libraries. This feature -together with the rigorous adherence to dataflow principles throughout the LWDF design framework -allows designers to integrate and experiment with dataflow modeling approaches relatively quickly and flexibly into existing design methodologies and processes. Dataflow IntroductionModel-based design has been explored extensively over the years in many domains of embedded systems. In model-based design, application subsystems are represented in terms of functional components that interact through formal models of computation (e.g., see [1]). By exposing and exploiting high level application structure that is often difficult to extract from platform-based design tools, model-based approaches facilitate systematic integration, analysis, synthesis, and optimization that can be used to exploit platform-based tools and devices more effectively.Dataflow is a well known computational model and is widely used for expressing the functionality of digital signal processing (DSP) applications, such as audio and video data stream processing, digital communications, and image processing (e.g., see [2,3,4]). These applications usually require real-time processing capabilities and have critical performance constraints. Dataflow

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Memory-constrained Block Processing for DSP Software Optimization

Cited by 13 publications

References 18 publications

An empirical characterization of stream programs and its implications for language and compiler design

An empirical characterization of stream programs and its implications for language and compiler design

Design Tools for Accelerating Development and Usage of Multi-Core Computing Platforms

Application-Specific Instruction Set Processors for Video Processing

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