A new viewpoint on code generation for directed acyclic graphs

Liao, Stan; Keutzer, Kurt; Tjiang, Steve; Devadas, Srinivas

doi:10.1145/270580.270583

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…According to Liao et al [235,236] and Cong et al [82], the pioneering use of binate covering to solve DAG covering was done by Rudell [296] in 1989 as a part of a very large scale integration (VLSI) synthesis design. Liao et al [235,236] later adapted it to instruction selection in a method that optimizes code size for one-register target machines.…”

Section: Applicationsmentioning

confidence: 99%

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Instruction Selection

Blindell

2016

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Section: Applicationsmentioning

confidence: 99%

“…Liao et al [235,236] later adapted it to instruction selection in a method that optimizes code size for one-register target machines. To prune the search space, Liao et al perform pattern selection in two iterations.…”

Section: Applicationsmentioning

confidence: 99%

Instruction Selection

Blindell

2016

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“…Weighted satisfiability problems, by which throughout we mean that the propositional variables are the weighted objects, provide natural generalizations of SAT and also have important applications, e.g. in the area of code generation [1,12].…”

Section: Introductionmentioning

confidence: 99%

On variable-weighted exact satisfiability problems

Porschen

2007

Ann Math Artif Intell

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We show that the NP-hard optimization problems minimum and maximum weight exact satisfiability (XSAT) for a CNF formula C over n propositional variables equipped with arbitrary real-valued weights can be solved in O( C 2 0.2441n ) time. To the best of our knowledge, the algorithms presented here are the first handling weighted XSAT optimization versions in non-trivial worst case time. We also investigate the corresponding weighted counting problems, namely we show that the number of all minimum, resp. maximum, weight exact satisfiability solutions of an arbitrarily weighted formula can be determined in O(n 2 · C + 2 0.40567n ) time. In recent years only the unweighted counterparts of these problems have been studied [8,9,15].

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“…Liao [5,6] used clauses with adjacency variables to describe the all legal worm-partitions. He applies binate covering formulation to find optimal scheduling.…”

Section: Introductionmentioning

confidence: 99%

Code Generation: On the Scheduling of DAGs Using Worm-Partition

El-Boghdadi

Bohalfaeh

2007

2007 IEEE International Parallel and Distributed Processing Symposium

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Code generation consists of three main stages, instruction selection, scheduling and register allocation. The scheduling stage is very important because it affects the execution time of resulting code as well as the associated memory space needed to store the program. This paper deals with scheduling directed acyclic graphs (DAGs) using worm-partition. First, we develop a new algorithm to partition DAGs into a collection of worms while ensuring that the worm-partition is legal. Although the algorithm does not guarantee the minimum number of worms, it runs in optimal O(|V | + |E|) time. This is in contrast to the known method [4] for producing the minimum number of worms that runs in O(|V | 2 + |V ||E|). We apply the algorithm to benchmark real problems and show its comparable results to the previous method. Then we study some DAG properties that are related to worm partitioning. We derive a necessary condition for the minimum number of worms in a given DAG. In other words, a lower bound for the number of worms. Then we identify two important classes of DAGs, for which this necessary condition is sufficient as well; i.e. we show that the lower bound is a tight one. Finally, we show that our algorithm generates the minimum number of worms for theses classes of DAGs.

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A new viewpoint on code generation for directed acyclic graphs

Cited by 14 publications

References 9 publications

Instruction Selection

Instruction Selection

On variable-weighted exact satisfiability problems

Code Generation: On the Scheduling of DAGs Using Worm-Partition

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