Pub Date : 1900-01-01DOI: 10.4230/LIPIcs.SNAPL.2015.15
Amal J. Ahmed
Though there has been remarkable progress on formally verified compilers in recent years, most of these compilers suffer from a serious limitation: they are proved correct under the assumption that they will only be used to compile whole programs. This is an unrealistic assumption since most software systems today are comprised of components written in different languages - both typed and untyped - compiled by different compilers to a common target, as well as low-level libraries that may be handwritten in the target language. We are pursuing a new methodology for building verified compilers for today's world of multi-language software. The project has two central themes, both of which stem from a view of compiler correctness as a language interoperability problem. First, to specify correctness of component compilation, we require that if a source component s compiles to target component t, then t linked with some arbitrary target code t' should behave the same as s interoperating with t'. The latter demands a formal semantics of interoperability between the source and target languages. Second, to enable safe interoperability between components compiled from languages as different as ML, Rust, Python, and C, we plan to design a gradually type-safe target language based on LLVM that supports safe interoperability between more precisely typed, less precisely typed, and type-unsafe components. Our approach opens up a new avenue for exploring sensible language interoperability while also tackling compiler correctness.
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Pub Date : 1900-01-01DOI: 10.4230/LIPIcs.SNAPL.2017.19
James R. Wilcox, Ilya Sergey, Zachary Tatlock
Distributed systems are rarely developed as monolithic programs. Instead, like any software, these systems may consist of multiple program components, which are then compiled separately and linked together. Modern systems also incorporate various services interacting with each other and with client applications. However, state-of-the-art verification tools focus predominantly on verifying standalone, closed-world protocols or systems, thus failing to account for the compositional nature of distributed systems. For example, standalone verification has the drawback that when protocols and their optimized implementations evolve, one must re-verify the entire system from scratch, instead of leveraging compositionality to contain the reverification effort. In this paper, we focus on the challenge of modular verification of distributed systems with respect to high-level protocol invariants as well as for low-level implementation safety properties. We argue that the missing link between the two is a programming paradigm that would allow one to reason about both high-level distributed protocols and low-level implementation primitives in a single verification-friendly framework. Such a link would make it possible to reap the benefits from both the vast body of research in distributed computing, focused on modular protocol decomposition and consistency properties, as well as from the recent advances in program verification, enabling construction of provably correct systems implementations. To showcase the modular verification challenges, we present some typical scenarios of decomposition between a distributed protocol and its implementations. We then describe our ongoing research agenda, in which we are attempting to address the outlined problems by providing a typing discipline and a set of domain-specific primitives for specifying, implementing and verifying distributed systems. Our approach, mechanized within a proof assistant, provides the means of decomposition necessary for modular proofs about distributed protocols and systems.
{"title":"Programming Language Abstractions for Modularly Verified Distributed Systems","authors":"James R. Wilcox, Ilya Sergey, Zachary Tatlock","doi":"10.4230/LIPIcs.SNAPL.2017.19","DOIUrl":"https://doi.org/10.4230/LIPIcs.SNAPL.2017.19","url":null,"abstract":"Distributed systems are rarely developed as monolithic programs. Instead, like any software, these systems may consist of multiple program components, which are then compiled separately and linked together. Modern systems also incorporate various services interacting with each other and with client applications. However, state-of-the-art verification tools focus predominantly on verifying standalone, closed-world protocols or systems, thus failing to account for the compositional nature of distributed systems. For example, standalone verification has the drawback that when protocols and their optimized implementations evolve, one must re-verify the entire system from scratch, instead of leveraging compositionality to contain the reverification effort. \u0000 \u0000In this paper, we focus on the challenge of modular verification of distributed systems with respect to high-level protocol invariants as well as for low-level implementation safety properties. We argue that the missing link between the two is a programming paradigm that would allow one to reason about both high-level distributed protocols and low-level implementation primitives in a single verification-friendly framework. Such a link would make it possible to reap the benefits from both the vast body of research in distributed computing, focused on modular protocol decomposition and consistency properties, as well as from the recent advances in program verification, enabling construction of provably correct systems implementations. To showcase the modular verification challenges, we present some typical scenarios of decomposition between a distributed protocol and its implementations. We then describe our ongoing research agenda, in which we are attempting to address the outlined problems by providing a typing discipline and a set of domain-specific primitives for specifying, implementing and verifying distributed systems. Our approach, mechanized within a proof assistant, provides the means of decomposition necessary for modular proofs about distributed protocols and systems.","PeriodicalId":231548,"journal":{"name":"Summit on Advances in Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/LIPIcs.SNAPL.2019.1
Michael Carbin
A new ecosystem of machine-learning driven applications, titled Software 2.0, has arisen that integrates neural networks into a variety of computational tasks. Such applications include image recognition, natural language processing, and other traditional machine learning tasks. However, these techniques have also grown to include other structured domains, such as program analysis and program optimization for which novel, domain-specific insights mate with model design. In this paper, we connect the world of Software 2.0 with that of traditional software - Software 1.0 - through overparameterization: a program may provide more computational capacity and precision than is necessary for the task at hand. In Software 2.0, overparamterization - when a machine learning model has more parameters than datapoints in the dataset - arises as a contemporary understanding of the ability for modern, gradient-based learning methods to learn models over complex datasets with high-accuracy. Specifically, the more parameters a model has, the better it learns. In Software 1.0, the results of the approximate computing community show that traditional software is also overparameterized in that software often simply computes results that are more precise than is required by the user. Approximate computing exploits this overparameterization to improve performance by eliminating unnecessary, excess computation. For example, one - of many techniques - is to reduce the precision of arithmetic in the application. In this paper, we argue that the gap between available precision and that that is required for either Software 1.0 or Software 2.0 is a fundamental aspect of software design that illustrates the balance between software designed for general-purposes and domain-adapted solutions. A general-purpose solution is easier to develop and maintain versus a domain-adapted solution. However, that ease comes at the expense of performance. We show that the approximate computing community and the machine learning community have developed overlapping techniques to improve performance by reducing overparameterization. We also show that because of these shared techniques, questions, concerns, and answers on how to construct software can translate from one software variant to the other.
{"title":"Overparameterization: A Connection Between Software 1.0 and Software 2.0","authors":"Michael Carbin","doi":"10.4230/LIPIcs.SNAPL.2019.1","DOIUrl":"https://doi.org/10.4230/LIPIcs.SNAPL.2019.1","url":null,"abstract":"A new ecosystem of machine-learning driven applications, titled Software 2.0, has arisen that integrates neural networks into a variety of computational tasks. Such applications include image recognition, natural language processing, and other traditional machine learning tasks. However, these techniques have also grown to include other structured domains, such as program analysis and program optimization for which novel, domain-specific insights mate with model design. In this paper, we connect the world of Software 2.0 with that of traditional software - Software 1.0 - through overparameterization: a program may provide more computational capacity and precision than is necessary for the task at hand. \u0000In Software 2.0, overparamterization - when a machine learning model has more parameters than datapoints in the dataset - arises as a contemporary understanding of the ability for modern, gradient-based learning methods to learn models over complex datasets with high-accuracy. Specifically, the more parameters a model has, the better it learns. \u0000In Software 1.0, the results of the approximate computing community show that traditional software is also overparameterized in that software often simply computes results that are more precise than is required by the user. Approximate computing exploits this overparameterization to improve performance by eliminating unnecessary, excess computation. For example, one - of many techniques - is to reduce the precision of arithmetic in the application. \u0000In this paper, we argue that the gap between available precision and that that is required for either Software 1.0 or Software 2.0 is a fundamental aspect of software design that illustrates the balance between software designed for general-purposes and domain-adapted solutions. A general-purpose solution is easier to develop and maintain versus a domain-adapted solution. However, that ease comes at the expense of performance. \u0000We show that the approximate computing community and the machine learning community have developed overlapping techniques to improve performance by reducing overparameterization. We also show that because of these shared techniques, questions, concerns, and answers on how to construct software can translate from one software variant to the other.","PeriodicalId":231548,"journal":{"name":"Summit on Advances in Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133546609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/LIPIcs.SNAPL.2017.13
Justin Pombrio, S. Krishnamurthi, Kathi Fisler
We present a new approach to teaching programming language courses. Its essence is to view programming language learning as a natural science activity, where students probe languages experimentally to understand both the normal and extreme behaviors of their features. This has natural parallels to the "security mindset" of computer security, with languages taking the place of servers and other systems. The approach is modular (with minimal dependencies), incremental (it can be introduced slowly into existing classes), interoperable (it does not need to push out other, existing methods), and complementary (since it introduces a new mode of thinking).
{"title":"Teaching Programming Languages by Experimental and Adversarial Thinking","authors":"Justin Pombrio, S. Krishnamurthi, Kathi Fisler","doi":"10.4230/LIPIcs.SNAPL.2017.13","DOIUrl":"https://doi.org/10.4230/LIPIcs.SNAPL.2017.13","url":null,"abstract":"We present a new approach to teaching programming language courses. Its essence is to view programming language learning as a natural science activity, where students probe languages experimentally to understand both the normal and extreme behaviors of their features. This has natural parallels to the \"security mindset\" of computer security, with languages taking the place of servers and other systems. The approach is modular (with minimal dependencies), incremental (it can be introduced slowly into existing classes), interoperable (it does not need to push out other, existing methods), and complementary (since it introduces a new mode of thinking).","PeriodicalId":231548,"journal":{"name":"Summit on Advances in Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/LIPIcs.SNAPL.2015.32
Joshua S. Auerbach, D. F. Bacon, P. Cheng, Stephen J. Fink, R. Rabbah, Sunil Shukla
The Liquid Metal project at IBM Research aimed to design and implement a new programming language called Lime to address some of the challenges posed by heterogeneous systems. Lime is a Java-compatible programming language with features designed to facilitate high level synthesis to hardware (FPGAs). This article reviews the language design from the outset, and highlights some of the earliest design decisions. We also describe how these decisions were revised recently to accommodate important requirements that arise in networking and cryptography.
{"title":"Growing a Software Language for Hardware Design","authors":"Joshua S. Auerbach, D. F. Bacon, P. Cheng, Stephen J. Fink, R. Rabbah, Sunil Shukla","doi":"10.4230/LIPIcs.SNAPL.2015.32","DOIUrl":"https://doi.org/10.4230/LIPIcs.SNAPL.2015.32","url":null,"abstract":"The Liquid Metal project at IBM Research aimed to design and implement a new programming language called Lime to address some of the challenges posed by heterogeneous systems. Lime is a Java-compatible programming language with features designed to facilitate high level synthesis to hardware (FPGAs). This article reviews the language design from the outset, and highlights some of the earliest design decisions. We also describe how these decisions were revised recently to accommodate important requirements that arise in networking and cryptography.","PeriodicalId":231548,"journal":{"name":"Summit on Advances in Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125065440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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