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title: Reordering Shell Script Execution for Enhanced Performance | ||
presenter: Georgios Liargkovas, AUEB and Brown University | ||
title: LLMs for Code: The Potential, Prospects, and Problems | ||
presenter: Tushar Sharma, Dalhousie University | ||
date: 2024-06-12 | ||
time: 16:30 | ||
category: seminars | ||
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The execution order of shell scripts is determined by their syntax, thus missing key optimization opportunities routinely available in single-language runtime environments. | ||
hS is a new system that exploits the potential for non-linear execution in shell scripts, executing components speculatively out of order by inferring—and complying with—their runtime dependencies during their execution. | ||
To achieve this, hS introduces a new system-call monitor that collects ordering and effect constraints, a lightweight container that controls the order and application of side effects, a formally verified streaming scheduler that executes components within a window of speculation while respecting their ordering constraints, and several runtime optimizations for speculation and application of side effects. | ||
Applying hS to a large and diverse set of shell scripts yields a ~2x speedup for free (i.e. without any extra code or annotations). | ||
With the introduction of Large Language Models (LLMs) and their integration with software development tasks, the software development landscape has changed drastically in the last couple of years. In this session, we delve into the intricate world of large language models for code (LLM4Code) and explore their benefits, challenges, and threats. On one hand, these models have revolutionized code completion, bug detection, and even generated entire sections of code with remarkable accuracy. However, on the other side, several concerns have emerged surrounding inaccurate, buggy, and vulnerable code generation, biases, implications for climate, and the potential for unintended consequences. The talk promises an exploratory take that not only seeks to harness the potential of LLMs4Code but also ensures a conscientious and mindful approach toward their integration into our coding practices. | ||
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#### Biography | ||
Georgios Liargkovas will be pursuing a PhD in OS scheduling and cloud computing, with a focus on distributed and serverless architectures, at Columbia University, advised by Prof. Kostis Kaffes. He graduated with a Bachelor's degree in Software Engineering and Data Science from the Department of Management Science and Technology at Athens University of Economics and Business. Since 2020, he has been a research assistant at BALab, concentrating on empirical software engineering and mining software repositories studies. He is also an affiliate researcher at Brown University’s Atlas Systems Group, engaged in advancing shell-script parallelization. His research interests include system design and optimization, particularly through the application of machine learning techniques. Outside academia, he is passionate about long-distance running, cycling, and music. | ||
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Tushar Sharma is a tenure-track assistant professor at Dalhousie University, Canada. He leads the Software Maintenance and Analytics Research Team (SMART) lab in the Faculty of Computer Science. Topics related to software engineering, sustainable artificial intelligence, and machine learning for software engineering (ML4SE) define his career interests. He earned a PhD from the Athens University of Economics and Business, Athens, Greece, specializing in software engineering in 2019. He has ten years of industry experience, mainly with Siemens Research, USA and India. He co-authored Refactoring for Software Design Smells: Managing Technical Debt and two Oracle Java certification books. He founded Designite, offering code quality assessment tools that many practitioners and researchers use worldwide. He is an IEEE Senior Member. |
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title: Reordering Shell Script Execution for Enhanced Performance | ||
presenter: Georgios Liargkovas, AUEB and Brown University | ||
date: 2024-06-12 | ||
time: 18:00 | ||
category: seminars | ||
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The execution order of shell scripts is determined by their syntax, thus missing key optimization opportunities routinely available in single-language runtime environments. | ||
hS is a new system that exploits the potential for non-linear execution in shell scripts, executing components speculatively out of order by inferring—and complying with—their runtime dependencies during their execution. | ||
To achieve this, hS introduces a new system-call monitor that collects ordering and effect constraints, a lightweight container that controls the order and application of side effects, a formally verified streaming scheduler that executes components within a window of speculation while respecting their ordering constraints, and several runtime optimizations for speculation and application of side effects. | ||
Applying hS to a large and diverse set of shell scripts yields a ~2x speedup for free (i.e. without any extra code or annotations). | ||
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#### Biography | ||
Georgios Liargkovas will be pursuing a PhD in OS scheduling and cloud computing, with a focus on distributed and serverless architectures, at Columbia University, advised by Prof. Kostis Kaffes. He graduated with a Bachelor's degree in Software Engineering and Data Science from the Department of Management Science and Technology at Athens University of Economics and Business. Since 2020, he has been a research assistant at BALab, concentrating on empirical software engineering and mining software repositories studies. He is also an affiliate researcher at Brown University’s Atlas Systems Group, engaged in advancing shell-script parallelization. His research interests include system design and optimization, particularly through the application of machine learning techniques. Outside academia, he is passionate about long-distance running, cycling, and music. |