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Programming Languages and Compilers

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Articles 301 - 330 of 1844

Full-Text Articles in Computer Sciences

Using Landsat Satellite Imagery To Estimate Groundcover In The Grainbelt Of Western Australia, Justin Laycock, Nick Middleton, Karen Holmes Dec 2022

Using Landsat Satellite Imagery To Estimate Groundcover In The Grainbelt Of Western Australia, Justin Laycock, Nick Middleton, Karen Holmes

Resource management technical reports

Maintaining vegetative groundcover is an important component of sustainable agricultural systems and plays a critical function for soil and land conservation in Western Australia’s (WA) grainbelt (the south-west cropping region). This report describes how satellite imagery can be used to quantitatively and objectively estimate total vegetative groundcover, both in near real time and historically across large areas. We used the Landsat seasonal fractional groundcover products developed by the Joint Remote Sensing Research Program from the extensive archive of Landsat imagery. These products provide an estimate of the percentage of green vegetation, non-green vegetation and bare soil for each 30 m …

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R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao Dec 2022

R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao

Research Collection School Of Computing and Information Systems

Document-level natural language inference (DocNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents. Current datasets and baselines largely follow sentence-level settings, but fail to address the issues raised by longer documents. In this paper, we establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DocNLI: interpretability, long-range dependency, and cross-sentence inference. The basic idea of the framework is to simplify document-level task into a set of sentence-level tasks, and improve both performance and …

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The Effectiveness Of Using Python Programming Approach In Teaching Financial Analytics, Clarence Goh, Yuanto Kusnadi, Gary Pan Dec 2022

The Effectiveness Of Using Python Programming Approach In Teaching Financial Analytics, Clarence Goh, Yuanto Kusnadi, Gary Pan

Research Collection School Of Accountancy

This study presents a learning method and challenges regarding implementing a Python programming approach in teaching financial analytics to graduate accounting students. The advent of Big Data, as well as related applications and technologies, has significantly changed the process and practice of accounting. This has led to essential changes in the construction and teaching content of accounting education. While there have been several studies examining how data analytics is embedded in the accounting curriculum, the majority of the teaching cases in accounting focus on analysis and communication with Excel as the principal tool, with very few covering the necessary steps …

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A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer Dec 2022

A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer

Research Collection School Of Computing and Information Systems

We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (QoS) constraints. The key challenge lies in the fact that the QoS functions have no closed-form and need to be approximated by simulation. In this paper we propose a new way to approximate the QoS functions by logistic functions and design a new algorithm that combines logistic regression, cut generations and logistic-based local search to efficiently find good staffing solutions. We report computational results using examples up to 65 call types and 89 agent groups …

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Integrating Computing Into Preservice Teacher Preparation Programs Across The Core: Language, Mathematics, And Science, Lauren E. Margulieux, Patrick Enderle, Pier Junor Clarke, Natalie King, Caroline Sullivan, Michelle Zoss, Joyce Many Nov 2022

Integrating Computing Into Preservice Teacher Preparation Programs Across The Core: Language, Mathematics, And Science, Lauren E. Margulieux, Patrick Enderle, Pier Junor Clarke, Natalie King, Caroline Sullivan, Michelle Zoss, Joyce Many

Journal of Computer Science Integration

This paper describes the beginning of a design-based research project for integrating computing activities in preservice teacher programs throughout a middle and secondary education department. Computing integration activities use computing tools, like programming, to support learning in non-computing disciplines. The paper begins with the motivation for integrating computing that encouraged widespread buy-in, design goals, and design parameters. The primary motivating factor for this work was preparing teachers to use technology to support learning in their classrooms. Involving computing education faculty in the preparation enabled the activities to include computer science and spread computational literacy. The paper also describes the process …

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Unoapi: Balancing Performance, Portability, And Productivity (P3) In Hpc Education, Konstantin Laufer, George K. Thiruvathukal Nov 2022

Unoapi: Balancing Performance, Portability, And Productivity (P3) In Hpc Education, Konstantin Laufer, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

oneAPI is a major initiative by Intel aimed at making it easier to program heterogeneous architectures used in high-performance computing using a unified application programming interface (API). While raising the abstraction level via a unified API represents a promising step for the current generation of students and practitioners to embrace high- performance computing, we argue that a curriculum of well- developed software engineering methods and well-crafted exem- plars will be necessary to ensure interest by this audience and those who teach them. We aim to bridge the gap by developing a curriculum—codenamed UnoAPI—that takes a more holistic approach by looking …

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Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte Nov 2022

Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte

LSU Doctoral Dissertations

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), …

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Lstm-Sdm: An Integrated Framework Of Lstm Implementation For Sequential Data Modeling[Formula Presented], Hum Nath Bhandari, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal Nov 2022

Lstm-Sdm: An Integrated Framework Of Lstm Implementation For Sequential Data Modeling[Formula Presented], Hum Nath Bhandari, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal

Arts & Sciences Faculty Publications

LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems.

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Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim Nov 2022

Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Programming courses provide students with the skills to develop complex business applications. Teaching and learning programming is challenging, and collaborative learning is proposed to help with this challenge. Online discussion forums promote networking with other learners such that they can build knowledge collaboratively. It aids students open their horizons of thought processes to acquire cognitive skills. Cognitive analysis of discussion is critical to understand students' learning process. In this paper, we propose Bloom's taxonomy based cognitive model for programming discussion forums. We present machine learning (ML) based solution to extract students' cognitive skills. Our evaluations on compupting courses show that …

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Reinforcement Learning Approach To Coordinate Real-World Multi-Agent Dynamic Routing And Scheduling, Joe Waldy Nov 2022

Reinforcement Learning Approach To Coordinate Real-World Multi-Agent Dynamic Routing And Scheduling, Joe Waldy

Dissertations and Theses Collection (Open Access)

In this thesis, we study new variants of routing and scheduling problems motivated by real-world problems from the urban logistics and law enforcement domains. In particular, we focus on two key aspects: dynamic and multi-agent. While routing problems such as the Vehicle Routing Problem (VRP) is well-studied in the Operations Research (OR) community, we know that in real-world route planning today, initially-planned route plans and schedules may be disrupted by dynamically-occurring events. In addition, routing and scheduling plans cannot be done in silos due to the presence of other agents which may be independent and self-interested. These requirements create …

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Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li Nov 2022

Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li

Research Collection School Of Computing and Information Systems

Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing wellestablished benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study …

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Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang Nov 2022

Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper we study how to measure stereotypical bias in pre-trained vision-language models. We leverage a recently released text-only dataset, StereoSet, which covers a wide range of stereotypical bias, and extend it into a vision-language probing dataset called VLStereoSet to measure stereotypical bias in vision-language models. We analyze the differences between text and image and propose a probing task that detects bias by evaluating a model’s tendency to pick stereotypical statements as captions for anti-stereotypical images. We further define several metrics to measure both a vision-language model’s overall stereotypical bias and its intra-modal and inter-modal bias. Experiments on six …

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Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang Nov 2022

Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang

Research Collection School Of Computing and Information Systems

Due to the emergence of large-scale codebases, such as GitHub and Gitee, searching and reusing existing code can help developers substantially improve software development productivity. Over the years, many code search tools have been developed. Early tools leveraged the information retrieval (IR) technique to perform an efficient code search for a frequently changed large-scale codebase. However, the search accuracy was low due to the semantic mismatch between query and code. In the recent years, many tools leveraged Deep Learning (DL) technique to address this issue. But the DL-based tools are slow and the search accuracy is unstable.In this paper, we …

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Towards Understanding The Faults Of Javascript-Based Deep Learning Systems, Lili Quan, Qianyu Guo, Xiaofei Xie, Sen Chen, Xiaohong Li, Yang Liu Oct 2022

Towards Understanding The Faults Of Javascript-Based Deep Learning Systems, Lili Quan, Qianyu Guo, Xiaofei Xie, Sen Chen, Xiaohong Li, Yang Liu

Research Collection School Of Computing and Information Systems

Quality assurance is of great importance for deep learning (DL) systems, especially when they are applied in safety-critical applications. While quality issues of native DL applications have been extensively analyzed, the issues of JavaScript-based DL applications have never been systematically studied. Compared with native DL applications, JavaScript-based DL applications can run on major browsers, making the platform- and device-independent. Specifically, the quality of JavaScript-based DL applications depends on the 3 parts: the application, the third-party DL library used and the underlying DL framework (e.g., TensorFlow.js), called JavaScript-based DL system. In this paper, we conduct the first empirical study on the …

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How Many Mutex Bugs Can A Simple Analysis Find In Go Programs?, Fumi Takeuchi, Hidehiko Masuhara, Raffi Khatchadourian, Youyou Cong, Keisuke Ishibashi Sep 2022

How Many Mutex Bugs Can A Simple Analysis Find In Go Programs?, Fumi Takeuchi, Hidehiko Masuhara, Raffi Khatchadourian, Youyou Cong, Keisuke Ishibashi

Publications and Research

In open-source software, it is known that there are many concurrency bugs. A previous study in Go revealed that a considerable number of such bugs are simple (for example, 9% of the bugs are the ones that forget to unlock a mutex,) through a manual program investigation. This paper tries to detect such bugs by applying a simple analysis to see how far such a tool can match the manual analysis. We built a simple intraprocedural control flow analysis in Go, and evaluated its performance concerning the open source programs with concurrency bugs reported in the previous study. Consequently, as …

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Robustness And Cross-Lingual Transfer: An Exploration Of Out-Of-Distribution Scenario In Natural Language Processing, Yu, Sicheng Sep 2022

Robustness And Cross-Lingual Transfer: An Exploration Of Out-Of-Distribution Scenario In Natural Language Processing, Yu, Sicheng

Dissertations and Theses Collection (Open Access)

Most traditional machine learning or deep learning methods are based on the premise that training data and test data are independent and identical distributed, i.e., IID. However, it is just an ideal situation. In real-world applications, test set and training data often follow different distributions, which we refer to as the out of distribution, i.e., OOD, setting. As a result, models trained with traditional methods always suffer from an undesirable performance drop on the OOD test set. It's necessary to develop techniques to solve this problem for real applications. In this dissertation, we present four pieces of work in the …

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User Guided Abductive Proof Generation For Answer Set Programming Queries, Avishkar Mahajan, Martin Strecker, Meng Weng (Huang Mingrong) Wong Sep 2022

User Guided Abductive Proof Generation For Answer Set Programming Queries, Avishkar Mahajan, Martin Strecker, Meng Weng (Huang Mingrong) Wong

Research Collection Yong Pung How School Of Law

We present a method for generating possible proofs of a query with respect to a given Answer Set Programming (ASP) rule set using an abductive process where the space of abducibles is automatically constructed just from the input rules alone. Given a (possibly empty) set of user provided facts, our method infers any additional facts that may be needed for the entailment of a query and then outputs these extra facts, without the user needing to explicitly specify the space of all abducibles. We also present a method to generate a set of directed edges corresponding to the justification graph …

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The Effects Of Side-Channel Attacks On Post-Quantum Cryptography: Influencing Frodokem Key Generation Using The Rowhammer Exploit, Michael Jacob Fahr Aug 2022

The Effects Of Side-Channel Attacks On Post-Quantum Cryptography: Influencing Frodokem Key Generation Using The Rowhammer Exploit, Michael Jacob Fahr

Graduate Theses and Dissertations

Modern cryptographic algorithms such as AES and RSA are effectively used for securing data transmission. However, advancements in quantum computing pose a threat to modern cryptography algorithms due to the potential of solving hard mathematical problems faster than conventional computers. Thus, to prepare for quantum computing, NIST has started a competition to standardize quantum-resistant public-key cryptography algorithms. These algorithms are evaluated for strong theoretical security and run-time performance. NIST is in the third round of the competition, and the focus has shifted to analyzing the vulnerabilities to side-channel attacks. One algorithm that has gained notice is the Round 3 alternate …

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Effective Knowledge Graph Aggregation For Malware-Related Cybersecurity Text, Phillip Ryan Boudreau Aug 2022

Effective Knowledge Graph Aggregation For Malware-Related Cybersecurity Text, Phillip Ryan Boudreau

Graduate Theses and Dissertations

With the rate at which malware spreads in the modern age, it is extremely important that cyber security analysts are able to extract relevant information pertaining to new and active threats in a timely and effective manner. Having to manually read through articles and blog posts on the internet is time consuming and usually involves sifting through much repeated information. Knowledge graphs, a structured representation of relationship information, are an effective way to visually condense information presented in large amounts of unstructured text for human readers. Thusly, they are useful for sifting through the abundance of cyber security information that …

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Sound And Complete Certificates For Quantitative Termination Analysis Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic Aug 2022

Sound And Complete Certificates For Quantitative Termination Analysis Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the quantitative problem of obtaining lower-bounds on the probability of termination of a given non-deterministic probabilistic program. Specifically, given a non-termination threshold p∈[0,1], we aim for certificates proving that the program terminates with probability at least 1−p. The basic idea of our approach is to find a terminating stochastic invariant, i.e. a subset SI of program states such that (i) the probability of the program ever leaving SI is no more than p, and (ii) almost-surely, the program either leaves SI or terminates.While stochastic invariants are already well-known, we provide the first proof that the idea above is …

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Early Rumor Detection Using Neural Hawkes Process With A New Benchmark Dataset, Fengzhu Zeng, Wei Gao Jul 2022

Early Rumor Detection Using Neural Hawkes Process With A New Benchmark Dataset, Fengzhu Zeng, Wei Gao

Research Collection School Of Computing and Information Systems

Little attention has been paid on EArly Rumor Detection (EARD), and EARD performance was evaluated inappropriately on a few datasets where the actual early-stage information is largely missing. To reverse such situation, we construct BEARD, a new Benchmark dataset for EARD, based on claims from fact-checking websites by trying to gather as many early relevant posts as possible. We also propose HEARD, a novel model based on neural Hawkes process for EARD, which can guide a generic rumor detection model to make timely, accurate and stable predictions. Experiments show that HEARD achieves effective EARD performance on two commonly used general …

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Designing Flipped Learning Activities For Beginner Programming Course, Benjamin Gan, Eng Lieh Ouh Jul 2022

Designing Flipped Learning Activities For Beginner Programming Course, Benjamin Gan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This study focuses on designing flipped classroom learning activities across pre-class problem-based exercises; with in-class active discussions and practical problem-solving sessions; and follow up with postclass problem-based labs and assessments. We evaluate the effectiveness of our learning activities based on student surveys, course feedback, grades, and teacher feedback for a beginner programming course with non-IS students. We describe detail programming learning activities with comparisons to existing practices based on related work. Our findings are that majority of students (86%) agreed with flipped classroom, but teachers should be aware of the 14% who disagreed and cater for them. Teachers should avoid …

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A Weakly Supervised Propagation Model For Rumor Verification And Stance Detection With Multiple Instance Learning, Ruichao Yang, Jing Ma, Hongzhan Lin, Wei Gao Jul 2022

A Weakly Supervised Propagation Model For Rumor Verification And Stance Detection With Multiple Instance Learning, Ruichao Yang, Jing Ma, Hongzhan Lin, Wei Gao

Research Collection School Of Computing and Information Systems

The diffusion of rumors on social media generally follows a propagation tree structure, which provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor verification and stance detection are two relevant tasks that can jointly enhance each other despite their differences. For example, rumors can be debunked by cross-checking the stances conveyed by their relevant posts, and stances are also conditioned on the nature of the rumor. However, stance detection typically requires a large training set of labeled stances at post level, which are rare and costly to annotate. …

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Q2r: A Query-To-Resolution System For Natural-Language Queries, Shiau Hong Lim, Laura Wynter Jul 2022

Q2r: A Query-To-Resolution System For Natural-Language Queries, Shiau Hong Lim, Laura Wynter

Research Collection School Of Computing and Information Systems

We present a system for document retrieval that combines direct classification with standard content-based retrieval approaches to significantly improve the relevance of the retrieved documents. Our system exploits the availability of an imperfect but sizable amount of labeled data from past queries. For domains such as technical support, the proposed approach enhances the system’s ability to retrieve documents that are otherwise ranked very low based on content alone. The system is easy to implement and can make use of existing text ranking methods, augmenting them through the novel Q2R orchestration framework. Q2R has been extensively tested and is in use …

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Making Curry With Rice: An Optimizing Curry Compiler, Steven Libby Jun 2022

Making Curry With Rice: An Optimizing Curry Compiler, Steven Libby

Dissertations and Theses

In this dissertation we present the RICE optimizing compiler for the functional logic language Curry. This is the first general optimizing compiler for a functional logic language. Our work is based on the idea of compiling through program transformations, which we have adapted from the functional language compiler community. We also present the GAS system for generating new program transformations, which uses the power of functional logic programming to provide a flexible framework for describing transformations. This allows us to describe and implement a wide range of optimizations including inlining, shortcut deforestation, unboxing, and case shortcutting, a new optimization we …

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Rasm: Compiling Racket To Webassembly, Grant Matejka Jun 2022

Rasm: Compiling Racket To Webassembly, Grant Matejka

Master's Theses

WebAssembly is an instruction set designed for a stack based virtual machine, with an emphasis on speed, portability and security. As the use cases for WebAssembly grow, so does the desire to target WebAssembly in compilation. In this thesis we present Rasm, a Racket to WebAssembly compiler that compiles a select subset of the top forms of the Racket programming language to WebAssembly. We also present our early findings in our work towards adding a WebAssembly backend to the Chez Scheme compiler that is the backend of Racket. We address initial concerns and roadblocks in adopting a WebAssembly backend and …

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Differential Cost Analysis With Simultaneous Potentials And Anti-Potentials, Dorde Zikelic, Bor-Yuh Evan Chang, Pauline Bolignano, Franco Raimondi Jun 2022

Differential Cost Analysis With Simultaneous Potentials And Anti-Potentials, Dorde Zikelic, Bor-Yuh Evan Chang, Pauline Bolignano, Franco Raimondi

Research Collection School Of Computing and Information Systems

We present a novel approach to differential cost analysis that, given a program revision, attempts to statically bound the difference in resource usage, or cost, between the two program versions. Differential cost analysis is particularly interesting because of the many compelling applications for it, such as detecting resource-use regressions at code-review time or proving the absence of certain side-channel vulnerabilities. One prior approach to differential cost analysis is to apply relational reasoning that conceptually constructs a product program on which one can over-approximate the difference in costs between the two program versions. However, a significant challenge in any relational approach …

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Blocklens: Visual Analytics Of Student Coding Behaviors In Block-Based Programming Environments., Sean Tung, Huan Wei, Haotian Li, Yong Wang, Meng Xia, Huamin. Qu Jun 2022

Blocklens: Visual Analytics Of Student Coding Behaviors In Block-Based Programming Environments., Sean Tung, Huan Wei, Haotian Li, Yong Wang, Meng Xia, Huamin. Qu

Research Collection School Of Computing and Information Systems

Block-based programming environments have been widely used to introduce K-12 students to coding. To guide students effectively, instructors and platform owners often need to understand behaviors like how students solve certain questions or where they get stuck and why. However, it is challenging for them to effectively analyze students’ coding data. To this end, we propose BlockLens, a novel visual analytics system to assist instructors and platform owners in analyzing students’ block-based coding behaviors, mistakes, and problem-solving patterns. BlockLens enables the grouping of students by question progress and performance, identification of common problem-solving strategies and pitfalls, and presentation of insights …

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Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja May 2022

Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved …

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Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja May 2022

Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved …

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