Will Killian
October 4, 2020
CSCI 330 - Programming Languages
Paper Bibliography

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Paper Topic:
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C++ Template Metaprogramming Makes Computer Go BRRRRR


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Sources:
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Title:
A Survey on Compiler Autotuning using Machine Learning

Authors:
A Ashouri, W Killian, J Cavazos, G Palermo, C Silvano

Type:
Journal Article

Description:
This paper highlights how various machine learning algorithms have been
adopted to solve several classes of compiler autotuning problems.
I'm typing this just to have an example.

BibTeX:
@article{10.1145/3197978,
author = {Ashouri, Amir H. and Killian, William and Cavazos, John and Palermo, Gianluca and Silvano, Cristina},
title = {A Survey on Compiler Autotuning Using Machine Learning},
year = {2018},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {51},
number = {5},
issn = {0360-0300},
url = {https://doi.org/10.1145/3197978},
doi = {10.1145/3197978},
abstract = {Since the mid-1990s, researchers have been trying to use machine-learning-based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations, and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches, and finally, the influential papers of the field.},
journal = {ACM Comput. Surv.},
month = sep,
articleno = {96},
numpages = {42},
keywords = {compilers, phase ordering, machine learning, Autotuning, optimizations}
}

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Title:
William Killian

Authors:
William Killian

Type:
Website

Description:
This source is a personal website that includes various links and resources
provided by Dr. Killian. Again, this is just an example to show what I would
consider an acceptable submission.

BibTeX:
@misc{killian_2020,
  url={https://cs.millersville.edu/~wkillian},
  journal={William Killian},
  author={Killian, William},
  year={2020}
}

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Title:
Accelerating Financial Applications on GPUs

Authors:
S Grauer-Gray, W Killian, R Searles, J Cavazos

Type:
Paper

Description:
This was my first peer-reviewed (and accepted) publication. Again, I am just
including this as another example.

BibTeX:
@inproceedings{10.1145/2458523.2458536,
author = {Grauer-Gray, Scott and Killian, William and Searles, Robert and Cavazos, John},
title = {Accelerating Financial Applications on the GPU},
year = {2013},
isbn = {9781450320177},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2458523.2458536},
doi = {10.1145/2458523.2458536},
abstract = {The QuantLib library is a popular library used for many areas of computational finance. In this work, the parallel processing power of the GPU is used to accelerate QuantLib financial applications. Black-Scholes, Monte-Carlo, Bonds, and Repo code paths in QuantLib are accelerated using hand-written CUDA and OpenCL codes specifically targeted for the GPU. Additionally, HMPP and OpenACC versions of the applications were created to drive the automatic generation of GPU code from sequential code. The results demonstrate a significant speedup for each code using each parallelization method. We were also able to increase the speedup of HMPP-generated code with auto-tuning.},
booktitle = {Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units},
pages = {127–136},
numpages = {10},
keywords = {auto-tuning, optimization, GPU, HMPP, computational finance, GPGPU, OpenCL, CUDA, OpenACC},
location = {Houston, Texas, USA},
series = {GPGPU-6}
}

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Note: I would still have one source here

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Note: I would still have one source here