implementation: started writing impl; finished technology section

thesis/references.bib

@@ -1176,7 +1176,7 @@
 	booktitle = {2005 {IEEE} Congress on Evolutionary Computation},
 	author = {Gustafson, S. and Burke, E.K. and Krasnogor, N.},
 	date = {2005-09},
-	keywords = {Computer science, Concrete, Diversity methods, Evolutionary computation, Genetic programming, Problem-solving},
+	keywords = {Evolutionary computation, Computer science, Concrete, Diversity methods, Genetic programming, Problem-solving},
 	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\28ZEEUYG\\Gustafson et al. - 2005 - On improving genetic programming for symbolic regression.pdf:application/pdf},
 }
 
@@ -1204,7 +1204,7 @@
 	publisher = {{arXiv}},
 	author = {Bruneton, J.-P.},
 	date = {2025-03-24},
-	keywords = {Computer Science - Neural and Evolutionary Computing, Computer Science - Symbolic Computation, Physics - Data Analysis, Statistics and Probability},
+	keywords = {Computer Science - Symbolic Computation, Computer Science - Neural and Evolutionary Computing, Physics - Data Analysis, Statistics and Probability},
 	file = {Preprint PDF:C\:\\Users\\danwi\\Zotero\\storage\\9U346ZEV\\Bruneton - 2025 - Enhancing Symbolic Regression with Quality-Diversity and Physics-Inspired Constraints.pdf:application/pdf},
 }
 
@@ -1222,3 +1222,37 @@
 	date = {1999},
 	langid = {english},
 }
+
+@article{bezanson_julia_2017,
+	title = {Julia: A Fresh Approach to Numerical Computing},
+	volume = {59},
+	issn = {0036-1445},
+	url = {https://epubs.siam.org/doi/10.1137/141000671},
+	doi = {10.1137/141000671},
+	shorttitle = {Julia},
+	abstract = {This is the third in a series of papers on aspects of modern computing environments that are relevant to statistical data analysis. In this paper, we discuss programming environments. In particular, we argue that integrated programming environments (for example, Lisp and Smalltalk environments) are more appropriate as a base for data analysis than conventional operating systems (for example, Unix).},
+	pages = {65--98},
+	number = {1},
+	journaltitle = {{SIAM} Review},
+	shortjournal = {{SIAM} Rev.},
+	author = {Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B.},
+	date = {2017-01},
+	file = {Submitted Version:C\:\\Users\\danwi\\Zotero\\storage\\9R4QSU35\\Bezanson et al. - 2017 - Julia A Fresh Approach to Numerical Computing.pdf:application/pdf},
+}
+
+@article{faingnaert_flexible_2022,
+	title = {Flexible Performant {GEMM} Kernels on {GPUs}},
+	volume = {33},
+	issn = {1558-2183},
+	url = {https://ieeexplore.ieee.org/document/9655458},
+	doi = {10.1109/TPDS.2021.3136457},
+	abstract = {General Matrix Multiplication or {GEMM} kernels take centre place in high performance computing and machine learning. Recent {NVIDIA} {GPUs} include {GEMM} accelerators, such as {NVIDIA}’s Tensor Cores. Their exploitation is hampered by the two-language problem: it requires either low-level programming which implies low programmer productivity or using libraries that only offer a limited set of components. Because rephrasing algorithms in terms of established components often introduces overhead, the libraries’ lack of flexibility limits the freedom to explore new algorithms. Researchers using {GEMMs} can hence not enjoy programming productivity, high performance, and research flexibility at once. In this paper we solve this problem. We present three sets of abstractions and interfaces to program {GEMMs} within the scientific Julia programming language. The interfaces and abstractions are co-designed for researchers’ needs and Julia’s features to achieve sufficient separation of concerns and flexibility to easily extend basic {GEMMs} in many different ways without paying a performance price. Comparing our {GEMMs} to state-of-the-art libraries {cuBLAS} and {CUTLASS}, we demonstrate that our performance is in the same ballpark of the libraries, and in some cases even exceeds it, without having to write a single line of code in {CUDA} C++ or assembly, and without facing flexibility limitations.},
+	pages = {2230--2248},
+	number = {9},
+	journaltitle = {{IEEE} Transactions on Parallel and Distributed Systems},
+	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},
+	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\QCJ6LSF3\\Faingnaert et al. - 2022 - Flexible Performant GEMM Kernels on GPUs.pdf:application/pdf},
+}