Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Comparing Julia and Python: An investigation of the performance on image processing with deep neural networks and classification
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2020 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Python is the most popular language when it comes to prototyping and developing machine learning algorithms. Python is an interpreted language that causes it to have a significant performance loss compared to compiled languages. Julia is a newly developed language that tries to bridge the gap between high performance but cumbersome languages such as C++ and highly abstracted but typically slow languages such as Python. However, over the years, the Python community have developed a lot of tools that addresses its performance problems. This raises the question if choosing one language over the other has any significant performance difference.

This thesis compares the performance, in terms of execution time, of the two languages in the machine learning domain. More specifically, image processing with GPU-accelerated deep neural networks and classification with k-nearest neighbor on the MNIST and EMNIST dataset. Python with Keras and Tensorflow is compared against Julia with Flux for GPU-accelerated neural networks. For classification Python with Scikit-learn is compared against Julia with Nearestneighbors.jl.

The results point in the direction that Julia has a performance edge in regards to GPU-accelerated deep neural networks. With Julia outperforming Python by roughly 1.25x − 1.5x. For classification with k-nearest neighbor the results were a bit more varied with Julia outperforming Python in 5 out of 8 different measurements. However, there exists some validity threats and additional research is needed that includes all different frameworks available for the languages in order to provide a more conclusive and generalized answer.

Place, publisher, year, edition, pages
2020. , p. 52
Keywords [en]
julia, python, performance, comparison, machine learning, image processing, GPU, GPU-acceleration, neural networks, autoencoder, classification, knn, k-nearest neighbor
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-19160OAI: oai:DiVA.org:bth-19160DiVA, id: diva2:1389123
Subject / course
PA1445 Kandidatkurs i Programvaruteknik
Educational program
PAGPT Software Engineering
Presentation
(English)
Supervisors
Examiners
Available from: 2020-01-29 Created: 2020-01-28 Last updated: 2020-01-29Bibliographically approved

Open Access in DiVA

Comparing Julia and Python(286 kB)46 downloads
File information
File name FULLTEXT02.pdfFile size 286 kBChecksum SHA-512
154714dfb8a4a8b0dbe7d7b488257c63fedea5f59556bf156e190e3cecebbcbc41c53fe738018fd95ca39b2c1c1c9eb430bb5c4d458bfd3bf683ef56d2a2171d
Type fulltextMimetype application/pdf

By organisation
Department of Software Engineering
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 46 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 69 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf