Owl Scientific Computing

Last updated
Original author(s) Liang Wang
Developer(s) Community project
Initial release2016 (2016)
Stable release
1.2 / 24 December 2024;0 days ago (2024-12-24) [1]
Repository https://github.com/owlbarn/owl
Written in OCaml, C
Operating system Cross-platform
Type Numerical analysis
License MIT
Website https://ocaml.xyz

Owl Scientific Computing is a software system for scientific and engineering computing developed in the Department of Computer Science and Technology, University of Cambridge. [2] The System Research Group (SRG) in the department recognises Owl as one of the representative systems developed in SRG in the 2010s. [3] The source code is licensed under the MIT License and can be accessed from the GitHub repository. [4]

Contents

The library is mostly designed and developed in the functional programming language OCaml. As a unique functional programming language, OCaml offers runtime efficiency, flexible module system, static type checking, intelligent garbage collector, and powerful type inference. Owl inherits these features directly from OCaml. With Owl, users can write succinct type-safe numerical applications in a concise functional language without sacrificing performance. It speeds up the development life-cycle, and reduces the cost from prototype to production use. The system serves as the de facto tool for computation intensive tasks in OCaml. [5]

History

Owl was developed when Dr. Liang Wang was working as a Post-Doc in the OCaml Labs. [6] Owl originated from a research project which studied the design of synchronous parallel machines for large-scale distributed computing in July 2016. Back then the libraries for numerical computing in OCaml ecosystem were very limited and the tooling was fragmented at that time. In order to test various analytical applications, many numerical functions had to be implemented, from very low level algebra and random number generators to the high level stuff like algorithmic differentiation and deep neural networks. These code snippets started accumulating. These functions were later taken out and wrapped into a standalone library named Owl.

Owl's architecture undertook at least a dozen of iterations in the beginning, and some of the architectural changes are quite drastic. After one-year intensive development, Owl was capable of doing many complicated numerical tasks (e.g. image classification). Dr. Liang Wang held a tutorial at the CUFP 2017 to demonstrate data science in OCaml. [7] In 2018, Prof. Richard Mortier gave a talk about Owl in the Alan Turing Institute. [8] To further promote OCaml and functional programming in data science, Owl provides abundant learning materials in the form of a details manual. [9]

Design and features

Owl has implemented many advanced numerical functions atop of its implementation of n-dimensional arrays. Compared to other numerical libraries, Owl is unique in many perspectives, e.g. algorithmic differentiation and distributed computing have been included as integral components in the core system to maximise developers' productivity. The figure below gives a bird view of Owl's system architecture. The subsystem on the left part is Owl's Numerical system. The modules contained in this subsystem fall into three categories.

The architecture of the Owl numerical library. Owl lib architecture.svg
The architecture of the Owl numerical library.

The first is core modules contains basic data structures, i.e., N-dimensional array (Ndarray) in both dense and sparse forms. The Ndarray module supports various number types: float32, float64, complex32, complex64, int16, int32, etc. Also, the core module provide foreign function interfaces to other low level numerical libraries, such as CBLAS and LAPACK. These libraries are fully interfaced to the Linear Algebra module.

The second category is the classic analytics modules. This part contains basic mathematical and statistical functions, linear algebra, regression, optimisation, plotting, etc. Advanced math and statistics functions such as statistical hypothesis testing and Markov chain Monte Carlo are also included. As a core functionality, Owl provides the algorithmic differentiation (or automatic differentiation) and dynamic computation graph modules.

The highest level in the Owl architecture includes modules more advanced numerical applications such as neural network, natural language processing, data processing etc. The Zoo system is used for efficient scripting and code sharing. The modules in the second category, especially the algorithmic differentiation, make the code at this level quite concise.

The subsystem on the right is called Actor Subsystem which extends Owl's capability to parallel and distributed computing. The core idea is to transform a user application from sequential execution mode into parallel mode (using various computation engines) with minimal efforts. The method is to compose two subsystems together with functors to generate the parallel version of the module defined in the numerical subsystem.

Besides what have been mentioned in this figure, there are several other features in Owl. For example, the JavaScript and unikernel backends, integration with other frameworks such as TensorFlow and PyTorch, utilising GPU and other accelerator frameworks via symbolic graph, etc.

Research

The Owl project is research oriented, and supports research of numerical computing in multiple related topics. Some of its research topics are listed below.

As result of research following part of these directions, Owl produces several publications. In 2018, a paper titled Data Analytics Service Composition and Deployment on Edge Devices is accepted at the ACM SIGCOMM 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks. [14] Two talks are also accepted at the OCaml Workshop of the International Conference on Functional Programming 2019, on the topics of numerical ordinary differential equation solving, [15] and executing Owl computation on GPUs. [16] An internship in the OCaml Labs investigates the topic of image segmentation and related memory optimisation in Owl. [17] In 2022, the book <<OCaml Scientific Computing>> was published by Springer. [18] In 2023, the book <<Architecture of Advanced Numerical Analysis Systems: Designing a Scientific Computing System using OCaml>> was published by Apress. [19]

See also

Related Research Articles

Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

<span class="mw-page-title-main">Theoretical computer science</span> Subfield of computer science and mathematics

Theoretical computer science is a subfield of computer science and mathematics that focuses on the abstract and mathematical foundations of computation.

Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.

The GNU Scientific Library is a software library for numerical computations in applied mathematics and science. The GSL is written in C; wrappers are available for other programming languages. The GSL is part of the GNU Project and is distributed under the GNU General Public License.

General-purpose computing on graphics processing units is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.

<span class="mw-page-title-main">Hardware acceleration</span> Specialized computer hardware

Hardware acceleration is the use of computer hardware designed to perform specific functions more efficiently when compared to software running on a general-purpose central processing unit (CPU). Any transformation of data that can be calculated in software running on a generic CPU can also be calculated in custom-made hardware, or in some mix of both.

<span class="mw-page-title-main">CUDA</span> Parallel computing platform and programming model

In computing, CUDA is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs. CUDA was created by Nvidia in 2006. When it was first introduced, the name was an acronym for Compute Unified Device Architecture, but Nvidia later dropped the common use of the acronym and now rarely expands it.

In computing, algorithmic skeletons, or parallelism patterns, are a high-level parallel programming model for parallel and distributed computing.

The Sidney Fernbach Award established in 1992 by the IEEE Computer Society, in memory of Sidney Fernbach, one of the pioneers in the development and application of high performance computers for the solution of large computational problems as the Division Chief for the Computation Division at Lawrence Livermore Laboratory from the late 1950s through the 1970s. A certificate and $2,000 are awarded for outstanding contributions in the application of high performance computers using innovative approaches. The nomination deadline is 1 July each year.

mlpack

mlpack is a free, open-source and header-only software library for machine learning and artificial intelligence written in C++, built on top of the Armadillo library and the ensmallen numerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. mlpack has also a light deployment infrastructure with minimum dependencies, making it perfect for embedded systems and low resource devices. Its intended target users are scientists and engineers.

Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.

<span class="mw-page-title-main">TensorFlow</span> Machine learning software library

TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks. It is one of the most popular deep learning frameworks, alongside others such as PyTorch and PaddlePaddle. It is free and open-source software released under the Apache License 2.0.

The following tables compare notable software frameworks, libraries, and computer programs for deep learning applications.

Approximate computing is an emerging paradigm for energy-efficient and/or high-performance design. It includes a plethora of computation techniques that return a possibly inaccurate result rather than a guaranteed accurate result, and that can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provide disproportionate gains in performance and energy, while still achieving acceptable result accuracy. For example, in k-means clustering algorithm, allowing only 5% loss in classification accuracy can provide 50 times energy saving compared to the fully accurate classification.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence (AI), its subdisciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

The Center for Supercomputing Research and Development (CSRD) at the University of Illinois (UIUC) was a research center funded from 1984 to 1993. It built the shared memory Cedar computer system, which included four hardware multiprocessor clusters, as well as parallel system and applications software. It was distinguished from the four earlier UIUC Illiac systems by starting with commercial shared memory subsystems that were based on an earlier paper published by the CSRD founders. Thus CSRD was able to avoid many of the hardware design issues that slowed the Illiac series work. Over its 9 years of major funding, plus follow-on work by many of its participants, CSRD pioneered many of the shared memory architectural and software technologies upon which all 21st century computation is based.

References

  1. ↑ "Releases – owlbarn/owl" . Retrieved 2024-12-24– via GitHub.
  2. ↑ "Owl, Scientific Computing for OCaml" . Retrieved 2020-11-11.
  3. ↑ "System Research Group" . Retrieved 2020-11-11.
  4. ↑ Owlbarn GitHub repository, https://github.com/owlbarn/owl. Retrieved 2020-11-01.
  5. ↑ "OCamlverse: Machine Learning, Scientific Computing and Data Science" . Retrieved 2020-11-05.
  6. ↑ "OCaml Labs" . Retrieved 2020-11-01.
  7. ↑ "Owl: Data Science in OCaml". CUFP Tutorials. 2017. Retrieved 2020-11-01.
  8. ↑ "The design of functional numerical software". Alan Turing Institute. 2018. Retrieved 2020-11-05.
  9. ↑ "OCaml Scientific Computing: Functional Programming Meets Data Science". Owl Team. 2020. Retrieved 2020-11-18.
  10. ↑ Wang, Liang; Catterall, Ben; Mortier, Richard (2017). "Probabilistic Synchronous Parallel". arXiv: 1709.07772 [cs.DC].
  11. ↑ "Distributed Learning over Unreliable Networks". Proceedings of Machine Learning Research. 2019. Retrieved 2020-11-18.
  12. ↑ "JuliaDiff". Julia. 2019. Retrieved 2020-11-18.
  13. ↑ "Serving DNNs like Clockwork: Performance Predictability from the Bottom Up". Usenix. 2020. Retrieved 2020-11-18.
  14. ↑ "BIG-DAMA Workshop 2018 Programme". ACM. 2018. Retrieved 2020-11-05.
  15. ↑ "OwlDE: making ODEs first-class Owl citizens". OCaml Workshop, ICFP 2019. 2019. Retrieved 2020-11-11.
  16. ↑ "Executing Owl Computation on GPU and TPU". OCaml Workshop, ICFP 2019. 2019. Retrieved 2020-11-11.
  17. ↑ "Personal Webpage, Pierre Vandenhove". 2018. Retrieved 2020-11-05.
  18. ↑ "OCaml Scientific Computing - Functional Programming in Data Science and Artificial Intelligence". Springer. 2022. Retrieved 2022-02-15.
  19. ↑ "Architecture of Advanced Numerical Analysis Systems - Designing a Scientific Computing System using OCaml". Apress. 2023. Retrieved 2024-12-14.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.