reference.bib

@INPROCEEDINGS{5655011,
    author={ {Li Jun} and  {Li Ling}},
    booktitle={2010 International Conference on Intelligent Computing and Integrated Systems},
    title={Comparative research on Python speed optimization strategies},
    year={2010},
    volume={},
    number={},
    pages={57-59},
    doi={10.1109/ICISS.2010.5655011}
}

@Article{NumpyArray,
    author={Harris, Charles R.
    and Millman, K. Jarrod
    and van der Walt, St{\'e}fan J.
    and Gommers, Ralf
    and Virtanen, Pauli
    and Cournapeau, David
    and Wieser, Eric
    and Taylor, Julian
    and Berg, Sebastian
    and Smith, Nathaniel J.
    and Kern, Robert
    and Picus, Matti
    and Hoyer, Stephan
    and van Kerkwijk, Marten H.
    and Brett, Matthew
    and Haldane, Allan
    and del R{\'i}o, Jaime Fern{\'a}ndez
    and Wiebe, Mark
    and Peterson, Pearu
    and G{\'e}rard-Marchant, Pierre
    and Sheppard, Kevin
    and Reddy, Tyler
    and Weckesser, Warren
    and Abbasi, Hameer
    and Gohlke, Christoph
    and Oliphant, Travis E.},
    title={Array programming with NumPy},
    journal={Nature},
    year={2020},
    month={Sep},
    day={01},
    volume={585},
    number={7825},
    pages={357-362},
    abstract={Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.},
    issn={1476-4687},
    doi={10.1038/s41586-020-2649-2},
    url={https://doi.org/10.1038/s41586-020-2649-2}
}

@misc{wiki:python,
    author = "{Wikipedia contributors}",
    title = "Python (programming language) --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Python_(programming_language)&oldid=999718546}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{Py:cProfile,
    author = "{Python Developer team}",
    title = "The Python Profilers",
    year = "2021",
    howpublished = "\url{https://docs.python.org/3/library/profile.html#the-python-profilers}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{Py:pickle,
    author = "{Python Developer team}",
    title = "The Python Profilers",
    year = "2021",
    howpublished = "\url{https://docs.python.org/3/library/pickle.html}",
    note = "[Online; accessed 04-April-2021]"
}

@misc{Py:filter,
    author = "{Python Developer team}",
    title = "The Python Profilers",
    year = "2021",
    howpublished = "\url{https://docs.python.org/3/library/functions.html#filter}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{Py:map,
    author = "{Python Developer team}",
    title = "The Python Profilers",
    year = "2021",
    howpublished = "\url{https://docs.python.org/3/library/functions.html#map}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{Py:listComprehension,
    author = "{Python Developer team}",
    title = "The Python Profilers",
    year = "2021",
    howpublished = "\url{https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{matProfile,
    title = "Profile",
    year = "2020",
    howpublished = "\url{https://nl.mathworks.com/help/matlab/ref/profile.html}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{wiki:HashTable,
    author = "{Wikipedia contributors}",
    title = "Hash table --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Hash_table&oldid=999105548}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{matEval,
    title = "Alternatives to the eval Function",
    year = "2020",
    howpublished = "\url{https://nl.mathworks.com/help/matlab/matlab_prog/string-evaluation.html}",
    note = "[Online; accessed 11-January-2021]"
}

@misc{wiki:SVM,
    author = "{Wikipedia contributors}",
    title = "Support-vector machine --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2020",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Support-vector_machine&oldid=997327362}",
    note = "[Online; accessed 11-January-2021]"
}

@Article{Cortes1995,
    author={Cortes, Corinna
    and Vapnik, Vladimir},
    title={Support-vector networks},
    journal={Machine Learning},
    year={1995},
    month={Sep},
    day={01},
    volume={20},
    number={3},
    pages={273-297},
    abstract={Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.},
    issn={1573-0565},
    doi={10.1007/BF00994018},
    url={https://doi.org/10.1007/BF00994018}
}

@Inbook{pythonBook,
    title="Advanced Python",
    bookTitle="Python Scripting for Computational Science",
    year="2006",
    publisher="Springer Berlin Heidelberg",
    address="Berlin, Heidelberg",
    pages="313--441",
    isbn="978-3-540-31269-7",
    doi="10.1007/3-540-31269-2_8",
    url="https://doi.org/10.1007/3-540-31269-2_8"
}

@misc{WhyMatlab,
    title = "MATLAB vs. Python: Top Reasons to Choose MATLAB",
    year = "2021",
    howpublished = "\url{https://nl.mathworks.com/products/matlab/matlab-vs-python.html}",
    note = "[Online; accessed 12-January-2021]"
}

@article{ARUOBA2015265,
    title = "A comparison of programming languages in macroeconomics",
    journal = "Journal of Economic Dynamics and Control",
    volume = "58",
    pages = "265 - 273",
    year = "2015",
    issn = "0165-1889",
    doi = "https://doi.org/10.1016/j.jedc.2015.05.009",
    url = "http://www.sciencedirect.com/science/article/pii/S0165188915000883",
    author = "S. Borağan Aruoba and Jesús Fernández-Villaverde",
    keywords = "Dynamic equilibrium economies, Computational methods, Programming languages",
    abstract = "We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++14, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and briefly comment on the strengths and weaknesses of each language."
}

@INPROCEEDINGS{EMMatVsPy,
    author={A. {Weiss} and A. {Elsherbeni}},
    booktitle={2020 International Applied Computational Electromagnetics Society Symposium (ACES)}, 
    title={Computational Performance of MATLAB and Python for Electromagnetic Applications}, 
    year={2020},
    volume={},
    number={},
    pages={1-2},
    doi={10.23919/ACES49320.2020.9196078}
}

@misc{Numpy:Vectorization,
    title = "Array Broadcasting in Numpy",
    year = "2020",
    howpublished = "\url{https://numpy.org/doc/stable/user/theory.broadcasting.html}",
    note = "[Online; accessed 13-January-2021]"
}

@misc{Numpy:OpenBLAS,
    title = "NumPy packages \& accelerated linear algebra libraries",
    year = "2020",
    howpublished = "\url{https://numpy.org/install/}",
    note = "[Online; accessed 01-April-2021]"
}

@misc{Numpy:Fast,
    title = "Why is NumPy Fast?",
    year = "2020",
    howpublished = "\url{https://numpy.org/doc/stable/user/whatisnumpy.html#why-is-numpy-fast}",
    note = "[Online; accessed 10-April-2021]"
}

@misc{wiki:LoopUnroll,
    author = "{Wikipedia contributors}",
    title = "Loop unrolling --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Loop_unrolling&oldid=997858676}",
    note = "[Online; accessed 13-January-2021]"
}

@misc{agner:LoopUnroll,
    author = {Fog,Agner},
    title = "Optimizing subroutines in assembly language",
    year = "2021",
    howpublished = "\url{https://www.agner.org/optimize/optimizing_assembly.pdf}",
    note = "[Online; accessed 27-April-2021]"
}

@book{oreilly,
    author={Gorelick,Micha and Ozsvald,Ian},
    year={2020},
    title={High Performance Python, 2nd Edition},
    publisher={O'Reilly Media, Inc},
    address={S.l.},
    edition={2},
    abstract={Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation.How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more.Get a better grasp of NumPy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on local or remote clustersDeploy code faster using tools like Docker;Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker;},
    keywords={CoreOs; Python},
    isbn={1492055026;9781492055020;9781492055013;1492055018;},
    language={English},
    chapter = 4,
}

@inbook{oreillyCh4,
    author={Gorelick,Micha and Ozsvald,Ian},
    year={2020},
    title={High Performance Python, 2nd Edition},
    publisher={O'Reilly Media, Inc},
    address={S.l.},
    edition={2},
    keywords={CoreOs; Python},
    isbn={1492055026;9781492055020;9781492055013;1492055018;},
    language={English},
    chapter={Chapter 4. Dictionaries and Sets},
}

@inbook{oreillyCh10,
    author={Gorelick,Micha and Ozsvald,Ian},
    year={2020},
    title={High Performance Python, 2nd Edition},
    publisher={O'Reilly Media, Inc},
    address={S.l.},
    edition={2},
    keywords={CoreOs; Python},
    isbn={1492055026;9781492055020;9781492055013;1492055018;},
    language={English},
    chapter={Chapter 10. Clusters and Job Queues},
}

@Inbook{Silaparasetty2020,
    author="Silaparasetty, Nikita",
    title="Machine Learning With Python",
    bookTitle="Machine Learning Concepts with Python and the Jupyter Notebook Environment: Using Tensorflow 2.0",
    year="2020",
    publisher="Apress",
    address="Berkeley, CA",
    pages="67--87",
    abstract="In previous chapters, we saw what artificial intelligence is and how machine learning and deep learning techniques are used to train machines to become smart. In these next few chapters, we will learn how machines are trained to take data, process it, analyze it, and develop inferences from it.",
    isbn="978-1-4842-5967-2",
    doi="10.1007/978-1-4842-5967-2_5",
    url="https://doi.org/10.1007/978-1-4842-5967-2_5",
    chapter={5. Machine Learning With Python}
}


@misc{PythonClusterFrameworks,
    title = "Parallel Processing and Multiprocessing in Python",
    year = "2020",
    howpublished = "\url{https://wiki.python.org/moin/ParallelProcessing}",
    note = "[Online; accessed 20-January-2021]"
}

@misc{MNEVersusFielder,
    title = "Background",
    year = "2021",
    howpublished = "\url{https://www.fieldtriptoolbox.org/development/project/integrate_with_mne/#background}",
    note = "[Online; accessed 11-April-2021]"
}

@misc{wiki:parallelComputing,
    author = "{Wikipedia contributors}",
    title = "Parallel computing --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Parallel_computing&oldid=1000667608}",
    note = "[Online; accessed 20-January-2021]"
}

@misc{wiki:distributedComputing,
    author = "{Wikipedia contributors}",
    title = "Distributed computing --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2020",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Distributed_computing&oldid=991259366}",
    note = "[Online; accessed 20-January-2021]"
}

@misc{exax:Accelerator,
    title = "About",
    year="2016",
    howpublished = "\url{https://expertmakeraccelerator.org/about/}",
    note = "[Online; accessed 10-February-2021]"
}


@misc{enwiki:crossvalidation,
    author = "{Wikipedia contributors}",
    title = "Cross-validation (statistics) --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Cross-validation_(statistics)&oldid=1007001562}",
    note = "[Online; accessed 17-February-2021]"
}

@misc{ray:delayget,
    author = "{The Ray Team}",
    title = "Tips for first-time users",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/auto_examples/tips-for-first-time.html#tip-1-delay-ray-get}",
    note = "[Online; accessed 01-March-2021]"
}

@misc{enwiki:avx,
    author = "{Wikipedia contributors}",
    title = "Advanced Vector Extensions --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Advanced_Vector_Extensions&oldid=1010378804}",
    note = "[Online; accessed 8-March-2021]"
}

@misc{AgnerFog:avx,
    author = {Fog,Agner},
    title = "The microarchitecture of Intel, AMD,and VIA CPUs",
    year = "2021",
    howpublished = "\url{https://www.agner.org/optimize/microarchitecture.pdf}",
    note = "[Online; accessed 27-April-2021]"
}

@misc{Intel:avx,
    author = {Lomont,Chris},
    title = "Introduction to Intel® Advanced Vector Extensions",
    year = "2021",
    howpublished = "\url{https://software.intel.com/content/www/us/en/develop/articles/introduction-to-intel-advanced-vector-extensions.html}",
    note = "[Online; accessed 27-April-2021]"
}

@ARTICLE{10.3389/fnins.2010.00198,
    AUTHOR={Blankertz, Benjamin and Tangermann, Michael and Vidaurre, Carmen and Fazli, Siamac and Sannelli, Claudia and Haufe, Stefan and Maeder, Cecilia and Ramsey, Lenny and Sturm, Irene and Curio, Gabriel and Mueller, Klaus},
    TITLE={The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology},      
    JOURNAL={Frontiers in Neuroscience},
    VOLUME={4},
    PAGES={198},
    YEAR={2010},
    URL={https://www.frontiersin.org/article/10.3389/fnins.2010.00198},
    DOI={10.3389/fnins.2010.00198},
    ISSN={1662-453X},   
    ABSTRACT={Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.}
}


@misc{nishihara2017realtime,
      title={Real-Time Machine Learning: The Missing Pieces}, 
      author={Robert Nishihara and Philipp Moritz and Stephanie Wang and Alexey Tumanov and William Paul and Johann Schleier-Smith and Richard Liaw and Mehrdad Niknami and Michael I. Jordan and Ion Stoica},
      year={2017},
      eprint={1703.03924},
      archivePrefix={arXiv},
      primaryClass={cs.DC}
}

@misc{moritz2018ray,
      title={Ray: A Distributed Framework for Emerging AI Applications}, 
      author={Philipp Moritz and Robert Nishihara and Stephanie Wang and Alexey Tumanov and Richard Liaw and Eric Liang and Melih Elibol and Zongheng Yang and William Paul and Michael I. Jordan and Ion Stoica},
      year={2018},
      eprint={1712.05889},
      archivePrefix={arXiv},
      primaryClass={cs.DC}
}

@misc{ray:whatIsRay,
    author ="{The Ray Team}",
    title = "A Gentle Introduction to Ray",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/ray-overview/index.html}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:RaySGD,
    author = "{The Ray Team}",
    title = "RaySGD: Distributed Training Wrappers",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/raysgd/raysgd.html}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:Rayrllib,
    author = "{The Ray Team}",
    title = "RLlib: Scalable Reinforcement Learning",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/rllib.html}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:RayTune,
    author = "{The Ray Team}",
    title = "Tune: Scalable Hyperparameter Tuning",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/tune/index.html}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:RaySklearn,
    author = "{The Ray Team}",
    title = "Distributed Scikit-learn / Joblib",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/joblib.html}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:DaskOnRay,
    author = "{The Ray Team}",
    title = "Dask on Ray",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/dask-on-ray.html}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:SystemDesign,
    author = "{The Ray Team}",
    title = "System design goals",
    year = "2020",
    howpublished = "\url{https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview#}",
    pages = "4--4",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:Architecture,
    author = "{The Ray Team}",
    title = "Architecture Overview",
    year = "2020",
    howpublished = "\url{https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview#}",
    pages = "5--8",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:GCS,
    author = "{The Ray Team}",
    title = "Global Control Store",
    year = "2020",
    howpublished = "\url{https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview#}",
    pages = "31--33",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:Resources,
    author = "{The Ray Team}",
    title = "ray.remote",
    year = "2021",
    howpublished = "\url{https://docs.ray.io/en/master/package-ref.html#ray-remote}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:PlacementGroup,
    author = "{The Ray Team}",
    title = "Placement Groups",
    year = "2020",
    howpublished = "\url{https://docs.ray.io/en/master/package-ref.html#placement-group}",
    note = "[Online; accessed 26-March-2021]"
}

@misc{ray:remoteFunctions,
    author = "{The Ray Team}",
    title = "Remote functions (Tasks)",
    year = "2020",
    howpublished = "\url{https://docs.ray.io/en/master/walkthrough.html#remote-functions-tasks}",
    note = "[Online; accessed 29-March-2021]"
}

@misc{ray:remoteObjects,
    author = "{The Ray Team}",
    title = "Objects in ray",
    year = "2020",
    howpublished = "\url{https://docs.ray.io/en/master/walkthrough.html#objects-in-ray}",
    note = "[Online; accessed 29-March-2021]"
}

@misc{ray:FetchingResults,
    author = "{The Ray Team}",
    title = "Fetching Results",
    year = "2020",
    howpublished = "\url{https://docs.ray.io/en/master/walkthrough.html#fetching-results}",
    note = "[Online; accessed 29-March-2021]"
}

@misc{ray:remoteClasses,
    author = "{The Ray Team}",
    title = "Remote Classes (Actors)",
    year = "2020",
    howpublished = "\url{https://docs.ray.io/en/master/walkthrough.html#remote-classes-actors}",
    note = "[Online; accessed 29-March-2021]"
}

@misc{ray:rayCluster,
    author = "{The Ray Team}",
    title = "Ray Cluster Overview",
    year = "2020",
    howpublished = "\url{https://docs.ray.io/en/master/cluster/index.html}",
    note = "[Online; accessed 29-March-2021]"
}

@misc{ray:OMPNUMTHREAD,
    author = "{The Ray Team}",
    title = "Cluster Resources",
    year = "2020",
    howpublished = "\url{https://docs.ray.io/en/master/configure.html#cluster-resources}",
    note = "[Online; accessed 1-April-2021]"
}

@inbook{exax:sourceCode,
    author={Berkeman,Anders and Drougge,Carl and Sofia Hörberg},
    year={2020},
    title={ExAx: TheAccelerator},
    keywords={Accelerator; exax},
    howpublished="\url{https://expertmakeraccelerator.org/pdf/acc_manual.pdf}",
    language={English},
    chapter={2.2.2 Jobs Can Only be Run Once},
    note = "[Online; accessed 29-March-2021]"
}

@inbook{exax:parallelExecution,
    author={Berkeman,Anders and Drougge,Carl and Sofia Hörberg},
    year={2020},
    title={ExAx: TheAccelerator},
    keywords={Accelerator; exax},
    howpublished="\url{https://expertmakeraccelerator.org/pdf/acc_manual.pdf}",
    language={English},
    chapter={2.4.2 Parallel Execution},
    note = "[Online; accessed 29-March-2021]"
}

@misc{AA:apacheArrow,
    author = "{The Apache Software Foundation}",
    title = "Apache Arrow",
    year = "2021",
    howpublished = "\url{https://arrow.apache.org/}",
    note = "[Online; accessed 30-March-2021]"
}

@misc{AA:Overview,
    author = "{The Apache Software Foundation}",
    title = "Columnar is Fast",
    year = "2021",
    howpublished = "\url{https://arrow.apache.org/overview/}",
    note = "[Online; accessed 30-March-2021]"
}

@misc{enwiki:MISD,
    author = "{Wikipedia contributors}",
    title = "SIMD --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=SIMD&oldid=1007613409}",
    note = "[Online; accessed 30-March-2021]"
}

@misc{GCC:MISD,
    author = "{GCC team}",
    title = "6.52 Using Vector Instructions through Built-in Functions",
    year = "2021",
    howpublished = "\url{https://gcc.gnu.org/onlinedocs/gcc/Vector-Extensions.html}",
    note = "[Online; accessed 27-April-2021]"
}

@misc{Intel:MISD,
    author = {{Marissa} du Bois and {Pete} Brubaker and {Dominic} Milano},
    title = "Single Instruction Multiple Data Made Easy with Intel® Implicit SPMD Program Compiler",
    year = "2019",
    howpublished = "\url{https://software.intel.com/content/www/us/en/develop/articles/simd-made-easy-with-intel-ispc.html}",
    note = "[Online; accessed 30-March-2021]"
}

@misc{enwiki:columnarData,
    author = "{Wikipedia contributors}",
    title = "Column-oriented DBMS --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Column-oriented_DBMS&oldid=1005533076}",
    note = "[Online; accessed 30-March-2021]"
}

@misc{AWS:columnarData,
    author = "{Amazon Web Services, Inc.}",
    title = "What is a Columnar Database?",
    year = "2021",
    howpublished = "\url{https://aws.amazon.com/nosql/columnar/}",
    note = "[Online; accessed 27-April-2021]"
}

@inbook{DiskSlow,
    author={Stallings,William},
    year={2012},
    title={Operating systems: internals and design principles},
    publisher={Pearson},
    address={Boston},
    edition={7., internat.},
    keywords={Betriebssystem; Operativsystem},
    isbn={0273751506;013230998X;9780132309981;9780273751502;},
    chapter={1.4 INTERRUPTS},
    language={English},
}

@INPROCEEDINGS{1607248,
  author={P. {Ratanaworabhan} and  {Jian Ke} and M. {Burtscher}},
  booktitle={Data Compression Conference (DCC'06)}, 
  title={Fast lossless compression of scientific floating-point data}, 
  year={2006},
  volume={},
  number={},
  pages={133-142},
  doi={10.1109/DCC.2006.35}
}

@inbook{ComputerMemoryHierarchy,
    author={Hennessy,John L. and Patterson,David A. and Asanović,Krste},
    year={2012;2011;},
    title={Computer architecture: a quantitative approach},
    publisher={Morgan Kaufmann/Elsevier},
    address={Waltham, MA},
    edition={5th},
    abstract={Computer Architecture: A Quantitative Approach explores the ways that software and technology in the cloud are accessed by digital media, such as cell phones, computers, tablets, and other mobile devices. The book became a part of Intel's 2012 recommended reading list for developers, and it covers the revolution of mobile computing. The text also highlights the two most important factors in architecture today: parallelism and memory hierarchy. The six chapters that this book is composed of follow a consistent framework: explanation of the ideas in each chapter; a ""crosscutting issues"" section, which presents how the concepts covered in one chapter connect with those given in other chapters; a ""putting it all together"" section that links these concepts by discussing how they are applied in real machine; and detailed examples of misunderstandings and architectural traps commonly encountered by developers and architects. The first chapter of the book includes formulas for energy, static and dynamic power, integrated circuit costs, reliability, and availability. Chapter 2 discusses memory hierarchy and includes discussions about virtual machines, SRAM and DRAM technologies, and new material on Flash memory. The third chapter covers the exploitation of instruction-level parallelism in high-performance processors, superscalar execution, dynamic scheduling and multithreading, followed by an introduction to vector architectures in the fourth chapter. Chapters 5 and 6 describe multicore processors and warehouse-scale computers (WSCs), respectively. This book is an important reference for computer architects, programmers, application developers, compiler and system software developers, computer system designers and application developers.
    
    Part of Intel's 2012 Recommended Reading List for DevelopersUpdated to cover the mobile computing revolutionEmphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms.Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next")Includes three review appendices in the printed text. Additional reference appendices are available online.Includes updated Case Studies and completely new exercises.;Computer Architecture: A Quantitative Approach explores the ways that software and technology in the cloud are accessed by digital media, such as cell phones, computers, tablets, and other mobile devices. The book became a part of Intel's 2012 recommended reading list for developers, and it covers the revolution of mobile computing. The text also highlights the two most important factors in architecture today: parallelism and memory hierarchy. The six chapters that this book is composed of follow a consistent framework: explanation of the ideas in each chapter; a ""crosscutting issues"" section, which presents how the concepts covered in one chapter connect with those given in other chapters; a ""putting it all together"" section that links these concepts by discussing how they are applied in real machine; and detailed examples of misunderstandings and architectural traps commonly encountered by developers and architects. The first chapter of the book includes formulas for energy, static and dynamic power, integrated circuit costs, reliability, and availability. Chapter 2 discusses memory hierarchy and includes discussions about virtual machines, SRAM and DRAM technologies, and new material on Flash memory. The third chapter covers the exploitation of instruction-level parallelism in high-performance processors, superscalar execution, dynamic scheduling and multithreading, followed by an introduction to vector architectures in the fourth chapter. Chapters 5 and 6 describe multicore processors and warehouse-scale computers (WSCs), respectively. This book is an important reference for computer architects, programmers, application developers, compiler and system software developers, computer system designers and application developers. Part of Intel's 2012 Recommended Reading List for Developers Updated to cover the mobile computing revolution
    
    Emphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms. Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next") Includes three review appendices in the printed text. Additional reference appendices are available online. Includes updated Case Studies and completely new exercises.;},
    keywords={Computer architecture},
    isbn={9780123838728;012383872X;},
    chapter={Chapter 2: Memory Hierarchy Design},
    language={English},
}

@misc{enwiki:memoryHierarchy,
    author = "{Wikipedia contributors}",
    title = "Memory hierarchy --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Memory_hierarchy&oldid=1002366093}",
    note = "[Online; accessed 30-March-2021]"
}

@article{cFaster,
    author={Prechelt,L.},
    year={2000},
    title={An empirical comparison of seven programming languages},
    journal={Computer (Long Beach, Calif.)},
    volume={33},
    number={10},
    pages={23-29},
    abstract={Often heated, debates regarding different programming languages' effectiveness remain inconclusive because of scarce data and a lack of direct comparisons. The author addresses that challenge, comparatively analyzing 80 implementations of the phone-code program in seven different languages (C, C++, Java, Perl, Python, Rexx and Tcl). Further, for each language, the author analyzes several separate implementations by different programmers. The comparison investigates several aspects of each language, including program length, programming effort, runtime efficiency, memory consumption, and reliability. The author uses comparisons to present insight into program language performance.},
    keywords={Computer languages; Java; Runtime; Program processors; Production; Read-write memory; Workstations; Sun; Statistics; Programming profession; Usage; Programming languages},
    isbn={0018-9162},
    language={English},
}

@misc{enwiki:AutoParallelization,
    author = "{Wikipedia contributors}",
    title = "Automatic parallelization --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Automatic_parallelization&oldid=1008941284}",
    note = "[Online; accessed 30-March-2021]"
}

@misc{enwiki:parallelization,
    author = "{Wikipedia contributors}",
    title = "Parallel computing --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Parallel_computing&oldid=1008901597}",
    note = "[Online; accessed 30-March-2021]"
}

@misc{mat:eval,
    author = "{The MathWorks, Inc.}",
    title = "eval",
    year = "2021",
    howpublished = "\url{https://www.mathworks.com/help/matlab/ref/eval.html}",
    note = "[Online; accessed 31-March-2021]"
}

@misc{OpenBLAS:threadSafe,
    author = "{seberg}",
    title = "OpenBLAS threadsafety issues for downstream libraries (NumPy)",
    year = "2021",
    howpublished = "\url{https://github.com/xianyi/OpenBLAS/issues/1844}",
    note = "[Online; accessed 1-April-2021]"
}

@misc{enwiki:1015447262,
    author = "{Wikipedia contributors}",
    title = "Disk buffer --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Disk_buffer&oldid=1015447262}",
    note = "[Online; accessed 10-April-2021]"
}

@misc{diskbuffer,
    author = "{The kernel development community}",
    title = "Explicit volatile write back cache control",
    year = "2021",
    howpublished = "\url{https://www.kernel.org/doc/Documentation/block/writeback_cache_control.txt}",
    note = "[Online; accessed 27-April-2021]"
}

@inbook{stackFrame,
    author={Miller,Bradley N. and Ranum,David L.},
    year={2006},
    title={Problem solving with algorithms and data structures using Python},
    publisher={Franklin, Beedle & Associates},
    address={Wilsonville, Or},
    keywords={Programspråk; Särskilda programspråk; Python (Computer program language); Computer algorithms; Data structures (Computer science); Python},
    isbn={9781590280539;1590280539;},
    chapter={5.6. Stack Frames: Implementing Recursion},
    language={English},
}

@misc{enwiki:1011877534,
    author = "{Wikipedia contributors}",
    title = "Profiling (computer programming) --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Profiling_(computer_programming)&oldid=1011877534}",
    note = "[Online; accessed 11-April-2021]"
}

@misc{Mat73HDF5,
    author = "{The MathWorks, Inc.}",
    title = "MAT-File Versions",
    year = "2021",
    howpublished = "\url{https://www.mathworks.com/help/matlab/import_export/mat-file-versions.html}",
    note = "[Online; accessed 12-April-2021]"
}

@misc{WhatIsHDF5,
    author = "{The HDF Group}",
    title = "HDF5",
    year = "2006",
    howpublished = "\url{https://www.hdfgroup.org/solutions/hdf5/}",
    note = "[Online; accessed 12-April-2021]"
}

@misc{SVMSVC,
    author = "{scikit-learn developers}",
    title = "sklearn.svm.SVC",
    year = "2020",
    howpublished = "\url{https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html?highlight=svc#sklearn-svm-svc}",
    note = "[Online; accessed 12-April-2021]"
}

@misc{LinearSVC,
    author = "{scikit-learn developers}",
    title = "sklearn.svm.LinearSVC",
    year = "2020",
    howpublished = "\url{https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html?highlight=svc#sklearn.svm.LinearSVC}",
    note = "[Online; accessed 12-April-2021]"
}

@Article{Oostenveld2010,
    author={Oostenveld, Robert
    and Fries, Pascal
    and Maris, Eric
    and Schoffelen, Jan-Mathijs},
    title={FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data},
    journal={Computational Intelligence and Neuroscience},
    year={2010},
    month={Dec},
    day={23},
    publisher={Hindawi Publishing Corporation},
    volume={2011},
    pages={156869},
    abstract={This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.},
    issn={1687-5265},
    doi={10.1155/2011/156869},
    url={https://doi.org/10.1155/2011/156869}
}

@article{superlinearSPEED,
    author={Ristov,Sasko and Prodan,Radu and Gusev,Marjan and Skala,Karolj},
    year={2016},
    title={Superlinear Speedup in HPC Systems: why and when?},
    journal={Annals of Computer Science and Information Systems},
    volume={8},
    pages={889-898},
    isbn={2300-5963},
    language={English},

}

@article {BramoENEURO.0251-18.2018,
	author = {Bram{\~a}o, In{\^e}s and Johansson, Mikael},
	title = {Neural Pattern Classification Tracks Transfer-Appropriate Processing in Episodic Memory},
	volume = {5},
	number = {4},
	elocation-id = {ENEURO.0251-18.2018},
	year = {2018},
	doi = {10.1523/ENEURO.0251-18.2018},
	publisher = {Society for Neuroscience},
	abstract = {The transfer-appropriate processing (TAP) account holds that episodic memory depends on the overlap between encoding and retrieval processing. In the current study, we employed multivariate pattern analysis (MVPA) of electroencephalography to examine the relevance of spontaneously engaged visual processing during encoding for later retrieval. Human participants encoded word-picture associations, where the picture could be a famous face, a landmark, or an object. At test, we manipulated the retrieval demands by asking participants to retrieve either visual or verbal information about the pictures. MVPA revealed classification between picture categories during early perceptual stages of encoding (\~{}170 ms). Importantly, these visual category-specific neural patterns were predictive of later episodic remembering, but the direction of the relationship was contingent on the particular retrieval demand of the memory task: a benefit for the visual and a cost for the verbal. A reinstatement of the category-specific neural patterns established during encoding was observed during retrieval, and again the relationship with behavior varied with retrieval demands. Reactivation of visual representations during retrieval was associated with better memory in the visual task, but with lower performance in the verbal task. Our findings support and extend the TAP account by demonstrating that processing of particular aspects during memory formation can also have detrimental effects on later episodic remembering when other aspects of the event are called-for and shed new light on encoding and retrieval interactions in episodic memory.},
	URL = {https://www.eneuro.org/content/5/4/ENEURO.0251-18.2018},
	eprint = {https://www.eneuro.org/content/5/4/ENEURO.0251-18.2018.full.pdf},
	journal = {eNeuro}
}