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In our experiments for Adiar v1.2 we have tried to uncover the two separate questions below. The code for the benchmarks can be found at https://github.com/SSoelvsten/bdd-benchmark. Alice: Which version proposed for Adiar v1.2.0 is the best in terms of performance? In other words, what granularity is enough to solve Adiar v1.0's performance issues at the least cost? The machine Alice is a consumer-grade laptop. Grendel: How has the use of maximum cuts improved Adiar's performance? To this end, we have run all of our benchmarks once more but with all three versions of Adiar. These experiments were run on the computation nodes of the Grendel cluster (phys.au.dk/forskning/cscaa/).
External Memory Algorithms, Binary Decision Diagrams, Maximum Graph Cuts
External Memory Algorithms, Binary Decision Diagrams, Maximum Graph Cuts
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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