Graduation Semester and Year
Spring 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Dr. Leonidas Fegaras
Second Advisor
Dr. Zaman Noor
Third Advisor
Prof. David Levine
Abstract
This thesis introduces an innovative approach within the DIABLO (Data-Intensive Array-Based Loop Optimizer) framework, which integrates NumPy-inspired syntax and functionalities to facilitate the transition from single-node, array-based scientific computing to scalable, distributed data-parallel programming. This integration aims to reduce the learning curve and enhance the usability of distributed computing technologies for scientific researchers traditionally accustomed to NumPy’s operational paradigms. DIABLO leverages advanced distributed computing techniques to optimize traditional matrix operations, such as addition, subtraction, and multiplication, critical for numerous applications ranging from physics to machine learning. By reinterpreting these matrix operations to run efficiently over distributed architectures, DIABLO not only ensures computational integrity and scalability but also significantly enhances execution speeds compared to traditional single-node implementations. The effectiveness of DIABLO is demonstrated through detailed benchmarks that compare its performance with traditional methods, highlighting substantial improvements in computational efficiency and resource utilization. The results affirm that DIABLO’s approach to integrating familiar numerical computing techniques with robust distributed processing capabilities sets a new standard for scientific computing, making it an indispensable tool for researchers dealing with large-scale data sets.
Keywords
Diablo, Array, Numpy, Matrix, Matrix arithmetics
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Gupta, Priyank, "ENHANCING ARRAY BASED OPERATIONS: A NUMPY INSPIRED APPROACH IN DIABLO FRAMEWORK" (2024). Computer Science and Engineering Theses. 9.
https://mavmatrix.uta.edu/cse_theses/9