Identifying communication models in Process Networks derived from Weakly Dynamic Programs

Nadezhkin, Dmitry; Stefanov, Todor

doi:10.1109/icsamos.2010.5642041

“…To implement the linearization, we adapted the approaches proposed in Turjan et al [2002] and Nadezhkin and Stefanov [2010]. In these works, the communication characteristics are identified when exchanging data between pair of statements.…”

Section: Linearization (Substep 3)mentioning

confidence: 99%

Automated generation of polyhedral process networks from affine nested-loop programs with dynamic loop bounds

Nadezhkin¹,

Nikolov²,

Stefanov³

2013

ACM Trans. Embed. Comput. Syst.

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The Process Networks (PNs) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is a very difficult and highly error-prone task. To overcome the associated difficulties, we have developed the pn compiler, which derives specific Polyhedral Process Networks (PPN) parallel specifications from sequential static affine nested loop programs (SANLPs). However, there are many applications, for example, multimedia applications (MPEG coders/decoders, smart cameras, etc.) that have adaptive and dynamic behavior which cannot be expressed as SANLPs. Therefore, in order to handle dynamic multimedia applications, in this article we address the important question whether we can relax some of the restrictions of the SANLPs while keeping the ability to perform compile-time analysis and to derive PPNs. Achieving this would significantly extend the range of applications that can be parallelized in an automated way.The main contribution of this article is a first approach for automated translation of affine nested loop programs with dynamic loop bounds into input-output equivalent Polyhedral Process Networks. In addition, we present a method for analyzing the execution overhead introduced in the PPNs derived from programs with dynamic loop bounds. The presented automated translation approach has been evaluated by deriving a PPN parallel specification from a real-life application called Low Speed Obstacle Detection (LSOD) used in the smart cameras domain. By executing the derived PPN, we have obtained results which indicate that the approach we present in this article facilitates efficient parallel implementations of sequential nested loop programs with dynamic loop bounds. That is, our approach reveals the possible parallelism available in such applications, which allows for the utilization of multiple cores in an efficient way. ACM Reference Format:Nadezhkin, D., Stefanov, T., and Nikolov, H. 2013. Automated generation of polyhedral process networks from affine nested-loop programs with dynamic loop bounds.

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“…To implement the Linearization, we adapted the approaches proposed in [26], [27]. In these works, the communication characteristics are identified when exchanging data between pair of statements.…”

Section: Substep 3: Linearizationmentioning

confidence: 99%

Automatic derivation of polyhedral process networks from while-loop affine programs

Nadezhkin

¹

,

Stefanov

²

2011

2011 9th IEEE Symposium on Embedded Systems for Real-Time Multimedia

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The Process Networks (PNs) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is very difficult and highly error-prone task. To overcome the associated difficulties, an automated procedure exists for derivation of a specific polyhedral process networks (PPN) from static affine nested loop programs (SANLPs). This procedure is implemented in the pn complier. However, there are many applications, e.g., multimedia applications, signal processing, etc., that have adaptive and dynamic behavior which can not be expressed as SANLPs. Therefore, in order to handle more dynamic applications, in this paper we address the important question whether we can relax some of the restrictions of the SANLPs while keeping the ability to perform compile-time analysis and to derive PPNs. Achieving this would significantly extend the range of applications that can be parallelized in an automated way. The main contribution of this paper is a first approach for automated translation of affine nested loops programs with while-loops into input-output equivalent PPNs.

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“…To implement the Linearization, we adapted the approaches proposed in [17], [18]. In these works, the communication characteristics are identified when exchanging data between pair of statements.…”

Section: Linearizationmentioning

confidence: 99%

Translating affine nested-loop programs with dynamic loop bounds into Polyhedral Process Networks

Nadezhkin

¹

,

Nikolov

²

,

Stefanov

³

2010

2010 8th IEEE Workshop on Embedded Systems for Real-Time Multimedia

Self Cite

View full text Add to dashboard Cite

The Process Network (PN) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is very difficult and highly error-prone task. To overcome the associated difficulties, an automated procedure exists for derivation of a specific polyhedral process networks (PPN) from static affine nested loop programs (SANLPs). This procedure is implemented in the pn complier. However, there are many applications, e.g., multimedia applications (MPEG coders/decoders, smart cameras, etc.) that have adaptive and dynamic behavior which can not be expressed as SANLPs. Therefore, in order to handle more dynamic multimedia applications, in this paper we address the important question whether we can relax some of the restrictions of the SANLPs while keeping the ability to perform compile-time analysis and to derive PPNs. Achieving this would significantly extend the range of applications that can be parallelized in an automated way. The main contribution of this paper is a first approach for automated translation of affine nested loops programs with dynamic loop bounds into input-output equivalent polyhedral process networks.

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Identifying communication models in Process Networks derived from Weakly Dynamic Programs

Cited by 3 publications

References 6 publications

Automated generation of polyhedral process networks from affine nested-loop programs with dynamic loop bounds

Automated generation of polyhedral process networks from affine nested-loop programs with dynamic loop bounds

Automatic derivation of polyhedral process networks from while-loop affine programs

Translating affine nested-loop programs with dynamic loop bounds into Polyhedral Process Networks

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