2026-09-22T07:36:41Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129945 2025-10-25 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms The student, Arthur Huang, accepted the attached license on 2025-07-15 at 17:44. The student, Arthur Huang, submitted this Thesis for approval on 2025-07-15 at 18:03. This Thesis was approved for publication on 2025-07-18 at 14:13. DSpace SAF Submission Ingestion Package generated from Vireo submission #22590 on 2025-10-20 at 20:15:18 MojoFrame: dataframe library in Mojo language Huang, Arthur 2025-07-18 Park, Yongjoo Mlir Jit Compilation Systems For Data Science Systems For Machine Learning Dataframe Database Management Systems eng Mojo is an emerging programming language built on MLIR (Multi-Level Intermediate Representation) and JIT compilation. It enables transparent optimizations with respect to the underlying hardware (e.g., CPUs, GPUs), while allowing users to express their logic using Python-like user-friendly syntax. Mojo has been shown to offer great performance in tensor operations; however, its performance has not been tested for relational operations (e.g., filtering, join, and group-by), which are common in data science workflows. To date, no dataframe implementation exists in the Mojo ecosystem. In this work, we introduce the first Mojo-native dataframe library, called MojoFrame, that supports core relational operations and user-defined functions (UDFs). MojoFrame is built on top of Mojo’s tensor to achieve fast operations on numeric columns, while utilizing a cardinality-aware approach to effectively integrate non-numeric columns for flexible data representation. To achieve high efficiency, MojoFrame takes significantly different approaches than existing libraries. MojoFrame supports all operations for TPC-H queries, and achieves up to 2.97× speedup versus existing dataframe libraries in other programming languages. Nevertheless, there remain optimization opportunities for MojoFrame (and the Mojo language), particularly in data loading and dictionary operations. 2025-08 Text https://hdl.handle.net/2142/129945 Copyright 2025 Arthur Huang Siebel School Comp & Data Sci Computer Science University of Illinois Urbana-Champaign M.S. Thesis