Abstract. Kahn Process Networks (KPN) are an appealing model of computation to specify streaming applications. When a KPN has to execute on a multi-processor platform, a mapping of the KPN model to the execution platform model should mitigate all possible overhead introduced by the mismatch between primitives realizing the communication semantics of the two models. In this paper, we consider mappings of KPN specification of streaming applications onto the Cell BE multi-processor execution platform. In particular, we investigate how to realize the FIFO communication of a KPN onto the Cell BE in order to reduce the synchronization overhead. We present a solution based on token packetization and show the performance results of five different streaming applications mapped onto the Cell BE.
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.
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.
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.
Process Networks (PNs) is an appealing computation abstraction helping to specify an application in parallel form and realize it on parallel platforms. The key questions to be answered are how a PN can be derived and how its components can be realized efficiently on a given parallel system. In this paper we present a novel approach of communication model identification in PNs derived from nested loops programs with data-dependent control statements, which we call Weakly Dynamic Programs (WDP). Identifying communication models at compile-time allows us to select the most efficient realization of communication components in a PN. We show how our approach is seamlessly integrated into our existing procedure of automated PN derivation from WDP programs. This paper can be considered as an important complementary work that makes our automated derivation of WDPs to PNs complete.
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