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2022 ◽  
Vol 27 (3) ◽  
pp. 1-23
Mari-Liis Oldja ◽  
Jangryul Kim ◽  
Dowhan Jeong ◽  
Soonhoi Ha

Although dataflow models are known to thrive at exploiting task-level parallelism of an application, it is difficult to exploit the parallelism of data, represented well with loop structures, since these structures are not explicitly specified in existing dataflow models. SDF/L model overcomes this shortcoming by specifying the loop structures explicitly in a hierarchical fashion. We introduce a scheduling technique of an application represented by the SDF/L model onto heterogeneous processors. In the proposed method, we explore the mapping of tasks using an evolutionary meta-heuristic and schedule hierarchically in a bottom-up fashion, creating parallel loop schedules at lower levels first and then re-using them when constructing the schedule at a higher level. The efficiency of the proposed scheduling methodology is verified with benchmark examples and randomly generated SDF/L graphs.

2022 ◽  
Vol 73 ◽  
pp. 276-281
Marta M Maciel ◽  
Tiago R Correia ◽  
Mariana Henriques ◽  
João F Mano

2022 ◽  
Vol 66 ◽  
pp. 125-139
Keyu Bao ◽  
Lisa-Marie Bieber ◽  
Sandra Kürpick ◽  
Mamy Harimisa Radanielina ◽  
Rushikesh Padsala ◽  

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-29
Anders Miltner ◽  
Adrian Trejo Nuñez ◽  
Ana Brendel ◽  
Swarat Chaudhuri ◽  
Isil Dillig

We present a novel bottom-up method for the synthesis of functional recursive programs. While bottom-up synthesis techniques can work better than top-down methods in certain settings, there is no prior technique for synthesizing recursive programs from logical specifications in a purely bottom-up fashion. The main challenge is that effective bottom-up methods need to execute sub-expressions of the code being synthesized, but it is impossible to execute a recursive subexpression of a program that has not been fully constructed yet. In this paper, we address this challenge using the concept of angelic semantics. Specifically, our method finds a program that satisfies the specification under angelic semantics (we refer to this as angelic synthesis), analyzes the assumptions made during its angelic execution, uses this analysis to strengthen the specification, and finally reattempts synthesis with the strengthened specification. Our proposed angelic synthesis algorithm is based on version space learning and therefore deals effectively with many incremental synthesis calls made during the overall algorithm. We have implemented this approach in a prototype called Burst and evaluate it on synthesis problems from prior work. Our experiments show that Burst is able to synthesize a solution to 94% of the benchmarks in our benchmark suite, outperforming prior work.

2022 ◽  
pp. 146144482110678
Anat Leshnick

Much research has documented how global technologies and platforms are part of specific cultures and reflect local values. In this study, I examine the case of Hebrew Wikipedia as representative of localization that is neither top-down (producer-driven) nor bottom-up (user-driven); but rather, it is implemented by mid-level, self-selecting bureaucratic administrators in an ongoing process that is driven by their own perceptions of Wikipedia’s mission. Through an analysis of Hebrew Wikipedia’s deletion discussion pages—in which editors decide what information should be excluded from Wikipedia—I demonstrate how national ideology customarily triumphs over the global, communitarian ethos of the Wikipedia project. Even when decisions are aligned with a more “global” agenda, editors still portray their choices as congruent with the national cause through strategic use of depersonalized discourses about Wikipedia’s policies. I thus argue that global, seemingly “neutral” policies can provide a discursive framework that conceals questions about the power of local ideologies.

2022 ◽  
Vol 11 ◽  
Ruoxi Xie ◽  
Changqiang Wu ◽  
Lu Yang ◽  
Peng Mi ◽  
Dong-Hyun Kim ◽  

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