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Benchmark and system parameters often have a significant impact on performance evaluation, which raises a long-lasting question about which settings we should use. This paper studies the feasibility and benefits of extensive evaluation. A full extensive evaluation, which tests all possible settings, is usually too expensive. This work investigates whether it is possible to sample a subset of the settings and, upon them, generate observations that match those from a full extensive evaluation. Towards this goal, we have explored the incremental sampling approach, which starts by measuring a small subset of random settings, builds a prediction model on these samples using the popular ANOVA approach, adds more samples if the model is not accurate enough, and terminates otherwise. To summarize our findings: 1) Enhancing a research prototype to support extensive evaluation mostly involves changing hard-coded configurations, which does not take much effort. 2) Some systems are highly predictable, which means that they can achieve accurate predictions with a low sampling rate, but some systems are less predictable. 3) We have not found a method that can consistently outperform random sampling + ANOVA. Based on these findings, we provide recommendations to improve artifact predictability and strategies for selecting parameter values during evaluation.
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A review on recent trends in selective hydrodeoxygenation of lignin derived molecules
A review on the selective catalytic hydrodeoxygenation of lignin biomass derived compounds. The focus is on recent reports which highlight achievements in selectively and reactivity trends which lead to deoxygenated aromatic products.
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- Award ID(s):
- 1954850
- PAR ID:
- 10496994
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- RSC Sustainability
- Volume:
- 1
- Issue:
- 7
- ISSN:
- 2753-8125
- Page Range / eLocation ID:
- 1608 to 1633
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d3su00232b
