A compiler infrastructure for embedded heterogeneous MPSoCs

Sheng, Weihua; Schürmans, Stefan; Odendahl, Maximilian; Bertsch, Mark; Volevach, Vitaliy; Leupers, Rainer; Ascheid, Gerd

doi:10.1016/j.parco.2013.11.007

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…The "mjpeg" and "audio filter" benchmarks from the multimedia and signal processing domains are similar to those described in [5]. Similarly, the "sobel" filter benchmark is described in [27].…”

Section: Resultsmentioning

confidence: 99%

“…It includes a parallelizing compiler that uses profiling information to dynamically track data dependencies and thereby extract hidden parallelism from sequential C programs [8]. Besides the sequential input, MAPS accepts a parallel specification written in the so-called "C for Process Networks" language [27], an extension to the C language that allows to specify KPN applications. Finally, MAPS includes a source-to-source compiler based on Clang [17], called cpn-cc, that generates platformspecific C code for heterogeneous MPSoCs.…”

Section: B the Maps Frameworkmentioning

confidence: 99%

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Why Comparing System-Level MPSoC Mapping Approaches is Difficult: A Case Study

Goens¹,

Khasanov²,

Castrillón³

et al. 2016

2016 IEEE 10th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSOC)

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Software abstractions are crucial to effectively program heterogeneous Multi-Processor Systems on Chip (MPSoCs).Prime examples of such abstractions are Kahn Process Networks (KPNs) and execution traces. When modeling computation as a KPN, one of the key challenges is to obtain a good mapping, i.e., an assignment of logical computation and communication to physical resources. In this paper we compare two systemlevel frameworks for solving the mapping problem: Sesame and MAPS. These frameworks, while superficially similar, embody different approaches. Sesame, motivated by modeling and designspace exploration, uses evolutionary algorithms for mapping. MAPS, being a compiler framework, uses simple and fast heuristics instead. In this work we highlight the value of common abstractions, such as KPNs and traces, as a vehicle to enable comparisons between large independent frameworks. These types of comparisons are fundamental for advancing research in the area. At the same time, we illustrate how the lack of formalized models at the hardware level are an obstacle to achieving fair comparisons. Additionally, using a set of applications from the embedded systems domain, we observe that genetic algorithms tend to outperform heuristics by a factor between 1× and 5×, with notable exceptions. This performance comes at the cost of a longer computation time, between 0 and 2 orders of magnitude in our experiments.

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Section: Resultsmentioning

confidence: 99%

Section: B the Maps Frameworkmentioning

confidence: 99%

Why Comparing System-Level MPSoC Mapping Approaches is Difficult: A Case Study

Goens¹,

Khasanov²,

Castrillón³

et al. 2016

2016 IEEE 10th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSOC)

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“…4. A compiler making load balancing of an originally sequential code for different number of cores is described and validated in paper [31], by running the executable code on platforms having different number of cores. The authors' first example shows results of implementing parallelized processing of an audio stream manually, with an initial, and a later, more careful implementation.…”

Section: Load Balancingmentioning

confidence: 99%

“…In the right column of the figure (Fig. 10 in [31]) the diagrams show the (1 − α ef f ) values, derived from the same data. In contrast with the left side, these values are nearly constant (at least within the measurement data readback error) which means that the derived parameter is really characteristic to the system.…”

Section: Load Balancingmentioning

confidence: 99%

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Can parallelization save the (computing) world?

Végh¹,

Vásárhelyi²,

Drótos³

2019

Adv. sci. technol. eng. syst. j.

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Single-processor approachAs all other laws of the growth in computing, the growth of computing performance also shows a "logistic curve"-like behavior, rather than an unlimited exponential growth. The stalling of the single-processor performance experienced nearly two decades ago forced computer experts to look for alternative methods, mainly for some kind of parallelization. Solving the task needs different parallelization methods, and the wide range of those distributed systems limits the computing performance in very different ways. Some general limitations are shortly discussed, and a (by intention strongly simplified) general model of performance of parallelized systems is introduced. The model enables to highlight bottlenecks of parallelized systems of different kind and with the published performance data enables to predict performance limits of strongly parallelized systems like large scale supercomputers and neural networks. Some alternative solution possibilities of increasing computing performance are also discussed.

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