evaluation: almost finished benchmark environment section

thesis/references.bib

@@ -400,6 +400,7 @@
 	author = {Winter, Martin and Parger, Mathias and Mlakar, Daniel and Steinberger, Markus},
 	urldate = {2025-02-27},
 	date = {2021-02-17},
+	file = {PDF:C\:\\Users\\danwi\\Zotero\\storage\\UURX5BER\\Winter et al. - 2021 - Are dynamic memory managers on GPUs slow a survey and benchmarks.pdf:application/pdf},
 }
 
 @article{bartlett_exhaustive_2024,
@@ -1253,6 +1254,28 @@
 	author = {Faingnaert, Thomas and Besard, Tim and De Sutter, Bjorn},
 	urldate = {2025-04-20},
 	date = {2022-09},
-	keywords = {Codes, Graphics processing units, graphics processors, high-level programming languages, Instruction sets, Kernel, Libraries, Matrix multiplication, Productivity, Programming},
+	keywords = {Graphics processing units, Kernel, Programming, Instruction sets, Codes, graphics processors, high-level programming languages, Libraries, Matrix multiplication, Productivity},
 	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\QCJ6LSF3\\Faingnaert et al. - 2022 - Flexible Performant GEMM Kernels on GPUs.pdf:application/pdf},
 }
+
+@report{nikuradse_laws_1950,
+	title = {Laws of Flow in Rough Pipes},
+	url = {https://digital.library.unt.edu/ark:/67531/metadc63009/},
+	author = {Nikuradse, J.},
+	date = {1950-11},
+}
+
+@article{guimera_bayesian_2020,
+	title = {A Bayesian machine scientist to aid in the solution of challenging scientific problems},
+	volume = {6},
+	url = {https://www.science.org/doi/10.1126/sciadv.aav6971},
+	doi = {10.1126/sciadv.aav6971},
+	abstract = {Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need “machine scientists” that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.},
+	pages = {eaav6971},
+	number = {5},
+	journaltitle = {Science Advances},
+	author = {Guimerà, Roger and Reichardt, Ignasi and Aguilar-Mogas, Antoni and Massucci, Francesco A. and Miranda, Manuel and Pallarès, Jordi and Sales-Pardo, Marta},
+	urldate = {2025-05-21},
+	date = {2020-01-31},
+	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\HLG9FD4H\\Guimerà et al. - 2020 - A Bayesian machine scientist to aid in the solution of challenging scientific problems.pdf:application/pdf},
+}