Traditional vectorization techniques build a dependence graph with distance and direction information to determine whether a loop is vectorizable. Since vectorization reorders the execution of instructions across iterations, in general instructions involved in a strongly connected component (SCC) are deemed not vectorizable unless the SCC can be eliminated using techniques such as scalar expansion or privatization. Therefore, traditional vectorization techniques are limited in their ability to efficiently handle loops with dynamic cross-iteration dependencies or complex control flow interweaved within the dependence cycles. When potential dependencies do not occur very often, the end-result is under utilization of the SIMD hardware. In this paper, we propose FlexVec architecture that combines new vector instructions with novel code generation techniques to dynamically adjusts vector length for loop statements affected by cross-iteration dependencies that happen at runtime. We have designed and implemented FlexVec's new ISA as extensions to the recently released AVX-512 ISA. We have evaluated the performance improvements enabled by FlexVec vectorization for 11 C/C++ SPEC 2006 benchmarks and 7 real applications with AVX-512 vectorization as baseline. We show that FlexVec vectorization technique produces a Geomean speedup of 9% for SPEC 2006 and a Geomean speedup of 11% for 7 real applications.
传统的向量化技术通过建立一个包含距离和方向信息的依赖图来确定环路是否可向量化。由于向量化在迭代中对指令的执行进行了重新排序，一般来说，强连接组件(SCC)中涉及的指令被认为是不可向量化的，除非使用标量展开或私有化等技术可以消除SCC。因此，传统的矢量化技术在有效处理具有动态交叉迭代依赖关系的循环或在依赖循环中交织的复杂控制流的能力方面受到限制。当潜在的依赖关系不经常发生时，最终结果是SIMD硬件的利用率不足。在本文中，我们提出了FlexVec架构，该架构结合了新的矢量指令和新的代码生成技术，可以动态调整受运行时交叉迭代依赖影响的循环语句的矢量长度。我们设计并实现了FlexVec的新ISA，作为最近发布的AVX-512 ISA的扩展。我们已经在11个C/ c++ SPEC 2006基准测试和7个以AVX-512向量化为基准的实际应用中评估了FlexVec向量化所带来的性能改进。我们表明，FlexVec矢量化技术在spec2006中使Geomean加速了9%，在7个实际应用中使Geomean加速了11%。
{"title":"FlexVec: auto-vectorization for irregular loops","authors":"Sara S. Baghsorkhi, N. Vasudevan, Youfeng Wu","doi":"10.1145/2908080.2908111","DOIUrl":"https://doi.org/10.1145/2908080.2908111","url":null,"abstract":"Traditional vectorization techniques build a dependence graph with distance and direction information to determine whether a loop is vectorizable. Since vectorization reorders the execution of instructions across iterations, in general instructions involved in a strongly connected component (SCC) are deemed not vectorizable unless the SCC can be eliminated using techniques such as scalar expansion or privatization. Therefore, traditional vectorization techniques are limited in their ability to efficiently handle loops with dynamic cross-iteration dependencies or complex control flow interweaved within the dependence cycles. When potential dependencies do not occur very often, the end-result is under utilization of the SIMD hardware. In this paper, we propose FlexVec architecture that combines new vector instructions with novel code generation techniques to dynamically adjusts vector length for loop statements affected by cross-iteration dependencies that happen at runtime. We have designed and implemented FlexVec's new ISA as extensions to the recently released AVX-512 ISA. We have evaluated the performance improvements enabled by FlexVec vectorization for 11 C/C++ SPEC 2006 benchmarks and 7 real applications with AVX-512 vectorization as baseline. We show that FlexVec vectorization technique produces a Geomean speedup of 9% for SPEC 2006 and a Geomean speedup of 11% for 7 real applications.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127487618","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}
M. Laurenzano, Parker Hill, M. Samadi, S. Mahlke, Jason Mars, Lingjia Tang
This paper introduces Input Responsive Approximation (IRA), an approach that uses a canary input — a small program input carefully constructed to capture the intrinsic properties of the original input — to automatically control how program approximation is applied on an input-by-input basis. Motivating this approach is the observation that many of the prior techniques focusing on choosing how to approximate arrive at conservative decisions by discounting substantial differences between inputs when applying approximation. The main challenges in overcoming this limitation lie in making the choice of how to approximate both effectively (e.g., the fastest approximation that meets a particular accuracy target) and rapidly for every input. With IRA, each time the approximate program is run, a canary input is constructed and used dynamically to quickly test a spectrum of approximation alternatives. Based on these runtime tests, the approximation that best fits the desired accuracy constraints is selected and applied to the full input to produce an approximate result. We use IRA to select and parameterize mixes of four approximation techniques from the literature for a range of 13 image processing, machine learning, and data mining applications. Our results demonstrate that IRA significantly outperforms prior approaches, delivering an average of 10.2× speedup over exact execution while minimizing accuracy losses in program outputs.
{"title":"Input responsiveness: using canary inputs to dynamically steer approximation","authors":"M. Laurenzano, Parker Hill, M. Samadi, S. Mahlke, Jason Mars, Lingjia Tang","doi":"10.1145/2908080.2908087","DOIUrl":"https://doi.org/10.1145/2908080.2908087","url":null,"abstract":"This paper introduces Input Responsive Approximation (IRA), an approach that uses a canary input — a small program input carefully constructed to capture the intrinsic properties of the original input — to automatically control how program approximation is applied on an input-by-input basis. Motivating this approach is the observation that many of the prior techniques focusing on choosing how to approximate arrive at conservative decisions by discounting substantial differences between inputs when applying approximation. The main challenges in overcoming this limitation lie in making the choice of how to approximate both effectively (e.g., the fastest approximation that meets a particular accuracy target) and rapidly for every input. With IRA, each time the approximate program is run, a canary input is constructed and used dynamically to quickly test a spectrum of approximation alternatives. Based on these runtime tests, the approximation that best fits the desired accuracy constraints is selected and applied to the full input to produce an approximate result. We use IRA to select and parameterize mixes of four approximation techniques from the literature for a range of 13 image processing, machine learning, and data mining applications. Our results demonstrate that IRA significantly outperforms prior approaches, delivering an average of 10.2× speedup over exact execution while minimizing accuracy losses in program outputs.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117025348","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}
This paper presents a novel automated procedure for discovering expressive shape specifications for sophisticated functional data structures. Our approach extracts potential shape predicates based on the definition of constructors of arbitrary user-defined inductive data types, and combines these predicates within an expressive first-order specification language using a lightweight data-driven learning procedure. Notably, this technique requires no programmer annotations, and is equipped with a type-based decision procedure to verify the correctness of discovered specifications. Experimental results indicate that our implementation is both efficient and effective, capable of automatically synthesizing sophisticated shape specifications over a range of complex data types, going well beyond the scope of existing solutions.
{"title":"Automatically learning shape specifications","authors":"He Zhu, G. Petri, S. Jagannathan","doi":"10.1145/2908080.2908125","DOIUrl":"https://doi.org/10.1145/2908080.2908125","url":null,"abstract":"This paper presents a novel automated procedure for discovering expressive shape specifications for sophisticated functional data structures. Our approach extracts potential shape predicates based on the definition of constructors of arbitrary user-defined inductive data types, and combines these predicates within an expressive first-order specification language using a lightweight data-driven learning procedure. Notably, this technique requires no programmer annotations, and is equipped with a type-based decision procedure to verify the correctness of discovered specifications. Experimental results indicate that our implementation is both efficient and effective, capable of automatically synthesizing sophisticated shape specifications over a range of complex data types, going well beyond the scope of existing solutions.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129321477","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}
We introduce transactions into libraries of concurrent data structures; such transactions can be used to ensure atomicity of sequences of data structure operations. By focusing on transactional access to a well-defined set of data structure operations, we strike a balance between the ease-of-programming of transactions and the efficiency of custom-tailored data structures. We exemplify this concept by designing and implementing a library supporting transactions on any number of maps, sets (implemented as skiplists), and queues. Our library offers efficient and scalable transactions, which are an order of magnitude faster than state-of-the-art transactional memory toolkits. Moreover, our approach treats stand-alone data structure operations (like put and enqueue) as first class citizens, and allows them to execute with virtually no overhead, at the speed of the original data structure library.
{"title":"Transactional data structure libraries","authors":"A. Spiegelman, Guy Golan-Gueta, I. Keidar","doi":"10.1145/2908080.2908112","DOIUrl":"https://doi.org/10.1145/2908080.2908112","url":null,"abstract":"We introduce transactions into libraries of concurrent data structures; such transactions can be used to ensure atomicity of sequences of data structure operations. By focusing on transactional access to a well-defined set of data structure operations, we strike a balance between the ease-of-programming of transactions and the efficiency of custom-tailored data structures. We exemplify this concept by designing and implementing a library supporting transactions on any number of maps, sets (implemented as skiplists), and queues. Our library offers efficient and scalable transactions, which are an order of magnitude faster than state-of-the-art transactional memory toolkits. Moreover, our approach treats stand-alone data structure operations (like put and enqueue) as first class citizens, and allows them to execute with virtually no overhead, at the speed of the original data structure library.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121508501","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}
Programmable analog devices have emerged as a powerful computing substrate for performing complex neuromorphic and cytomorphic computations. We present Arco, a new solver that, given a dynamical system specification in the form of a set of differential equations, generates physically realizable configurations for programmable analog devices that are algebraically equivalent to the specified system. On a set of benchmarks from the biological domain, Arco generates configurations with 35 to 534 connections and 28 to 326 components in 1 to 54 minutes.
{"title":"Configuration synthesis for programmable analog devices with Arco","authors":"Sara Achour, R. Sarpeshkar, M. Rinard","doi":"10.1145/2908080.2908116","DOIUrl":"https://doi.org/10.1145/2908080.2908116","url":null,"abstract":"Programmable analog devices have emerged as a powerful computing substrate for performing complex neuromorphic and cytomorphic computations. We present Arco, a new solver that, given a dynamical system specification in the form of a set of differential equations, generates physically realizable configurations for programmable analog devices that are algebraically equivalent to the specified system. On a set of benchmarks from the biological domain, Arco generates configurations with 35 to 534 connections and 28 to 326 components in 1 to 54 minutes.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124719426","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}
Most modern applications interact with external services and access data in structured formats such as XML, JSON and CSV. Static type systems do not understand such formats, often making data access more cumbersome. Should we give up and leave the messy world of external data to dynamic typing and runtime checks? Of course, not! We present F# Data, a library that integrates external structured data into F#. As most real-world data does not come with an explicit schema, we develop a shape inference algorithm that infers a shape from representative sample documents. We then integrate the inferred shape into the F# type system using type providers. We formalize the process and prove a relative type soundness theorem. Our library significantly reduces the amount of data access code and it provides additional safety guarantees when contrasted with the widely used weakly typed techniques.
{"title":"Types from data: making structured data first-class citizens in F#","authors":"T. Petříček, Gustavo Guerra, Don Syme","doi":"10.1145/2908080.2908115","DOIUrl":"https://doi.org/10.1145/2908080.2908115","url":null,"abstract":"Most modern applications interact with external services and access data in structured formats such as XML, JSON and CSV. Static type systems do not understand such formats, often making data access more cumbersome. Should we give up and leave the messy world of external data to dynamic typing and runtime checks? Of course, not! We present F# Data, a library that integrates external structured data into F#. As most real-world data does not come with an explicit schema, we develop a shape inference algorithm that infers a shape from representative sample documents. We then integrate the inferred shape into the F# type system using type providers. We formalize the process and prove a relative type soundness theorem. Our library significantly reduces the amount of data access code and it provides additional safety guarantees when contrasted with the widely used weakly typed techniques.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130999076","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}
Kayvan Memarian, Justus Matthiesen, J. Lingard, Kyndylan Nienhuis, D. Chisnall, R. Watson, Peter Sewell
C remains central to our computing infrastructure. It is notionally defined by ISO standards, but in reality the properties of C assumed by systems code and those implemented by compilers have diverged, both from the ISO standards and from each other, and none of these are clearly understood. We make two contributions to help improve this error-prone situation. First, we describe an in-depth analysis of the design space for the semantics of pointers and memory in C as it is used in practice. We articulate many specific questions, build a suite of semantic test cases, gather experimental data from multiple implementations, and survey what C experts believe about the de facto standards. We identify questions where there is a consensus (either following ISO or differing) and where there are conflicts. We apply all this to an experimental C implemented above capability hardware. Second, we describe a formal model, Cerberus, for large parts of C. Cerberus is parameterised on its memory model; it is linkable either with a candidate de facto memory object model, under construction, or with an operational C11 concurrency model; it is defined by elaboration to a much simpler Core language for accessibility, and it is executable as a test oracle on small examples. This should provide a solid basis for discussion of what mainstream C is now: what programmers and analysis tools can assume and what compilers aim to implement. Ultimately we hope it will be a step towards clear, consistent, and accepted semantics for the various use-cases of C.
{"title":"Into the depths of C: elaborating the de facto standards","authors":"Kayvan Memarian, Justus Matthiesen, J. Lingard, Kyndylan Nienhuis, D. Chisnall, R. Watson, Peter Sewell","doi":"10.1145/2908080.2908081","DOIUrl":"https://doi.org/10.1145/2908080.2908081","url":null,"abstract":"C remains central to our computing infrastructure. It is notionally defined by ISO standards, but in reality the properties of C assumed by systems code and those implemented by compilers have diverged, both from the ISO standards and from each other, and none of these are clearly understood. We make two contributions to help improve this error-prone situation. First, we describe an in-depth analysis of the design space for the semantics of pointers and memory in C as it is used in practice. We articulate many specific questions, build a suite of semantic test cases, gather experimental data from multiple implementations, and survey what C experts believe about the de facto standards. We identify questions where there is a consensus (either following ISO or differing) and where there are conflicts. We apply all this to an experimental C implemented above capability hardware. Second, we describe a formal model, Cerberus, for large parts of C. Cerberus is parameterised on its memory model; it is linkable either with a candidate de facto memory object model, under construction, or with an operational C11 concurrency model; it is defined by elaboration to a much simpler Core language for accessibility, and it is executable as a test oracle on small examples. This should provide a solid basis for discussion of what mainstream C is now: what programmers and analysis tools can assume and what compilers aim to implement. Ultimately we hope it will be a step towards clear, consistent, and accepted semantics for the various use-cases of C.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124654020","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}
Current algorithms for context-free parsing inflict a trade-off between ease of understanding, ease of implementation, theoretical complexity, and practical performance. No algorithm achieves all of these properties simultaneously. Might et al. introduced parsing with derivatives, which handles arbitrary context-free grammars while being both easy to understand and simple to implement. Despite much initial enthusiasm and a multitude of independent implementations, its worst-case complexity has never been proven to be better than exponential. In fact, high-level arguments claiming it is fundamentally exponential have been advanced and even accepted as part of the folklore. Performance ended up being sluggish in practice, and this sluggishness was taken as informal evidence of exponentiality. In this paper, we reexamine the performance of parsing with derivatives. We have discovered that it is not exponential but, in fact, cubic. Moreover, simple (though perhaps not obvious) modifications to the implementation by Might et al. lead to an implementation that is not only easy to understand but also highly performant in practice.
{"title":"On the complexity and performance of parsing with derivatives","authors":"Michael D. Adams, Celeste Hollenbeck, M. Might","doi":"10.1145/2908080.2908128","DOIUrl":"https://doi.org/10.1145/2908080.2908128","url":null,"abstract":"Current algorithms for context-free parsing inflict a trade-off between ease of understanding, ease of implementation, theoretical complexity, and practical performance. No algorithm achieves all of these properties simultaneously. Might et al. introduced parsing with derivatives, which handles arbitrary context-free grammars while being both easy to understand and simple to implement. Despite much initial enthusiasm and a multitude of independent implementations, its worst-case complexity has never been proven to be better than exponential. In fact, high-level arguments claiming it is fundamentally exponential have been advanced and even accepted as part of the folklore. Performance ended up being sluggish in practice, and this sluggishness was taken as informal evidence of exponentiality. In this paper, we reexamine the performance of parsing with derivatives. We have discovered that it is not exponential but, in fact, cubic. Moreover, simple (though perhaps not obvious) modifications to the implementation by Might et al. lead to an implementation that is not only easy to understand but also highly performant in practice.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123151031","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}
Dynamic languages such as Ruby, Python, and JavaScript have many compelling benefits, but the lack of static types means subtle errors can remain latent in code for a long time. While many researchers have developed various systems to bring some of the benefits of static types to dynamic languages, prior approaches have trouble dealing with metaprogramming, which generates code as the program executes. In this paper, we propose Hummingbird, a new system that uses a novel technique, just-in-time static type checking, to type check Ruby code even in the presence of metaprogramming. In Hummingbird, method type signatures are gathered dynamically at run-time, as those methods are created. When a method is called, Hummingbird statically type checks the method body against current type signatures. Thus, Hummingbird provides thorough static checks on a per-method basis, while also allowing arbitrarily complex metaprogramming. For performance, Hummingbird memoizes the static type checking pass, invalidating cached checks only if necessary. We formalize Hummingbird using a core, Ruby-like language and prove it sound. To evaluate Hummingbird, we applied it to six apps, including three that use Ruby on Rails, a powerful framework that relies heavily on metaprogramming. We found that all apps typecheck successfully using Hummingbird, and that Hummingbird's performance overhead is reasonable. We applied Hummingbird to earlier versions of one Rails app and found several type errors that had been introduced and then fixed. Lastly, we demonstrate using Hummingbird in Rails development mode to typecheck an app as live updates are applied to it.
动态语言(如Ruby、Python和JavaScript)有许多引人注目的优点，但缺乏静态类型意味着代码中可能长时间潜伏着细微的错误。虽然许多研究人员已经开发了各种系统，将静态类型的一些优点引入动态语言，但以前的方法在处理元编程方面存在问题，元编程在程序执行时生成代码。在本文中，我们提出了Hummingbird，一个使用新技术的新系统，即时静态类型检查，即使在存在元编程的情况下也可以对Ruby代码进行类型检查。在Hummingbird中，方法类型签名是在运行时创建这些方法时动态收集的。调用方法时，Hummingbird根据当前类型签名对方法体进行静态类型检查。因此，Hummingbird在每个方法的基础上提供了彻底的静态检查，同时还允许任意复杂的元编程。为了提高性能，Hummingbird记忆静态类型检查传递，仅在必要时才使缓存的检查无效。我们使用类似ruby的核心语言形式化了Hummingbird，并证明了它的正确性。为了评估Hummingbird，我们将其应用于六个应用程序，其中三个使用Ruby on Rails，这是一个非常依赖元编程的强大框架。我们发现所有的应用程序使用Hummingbird都能成功地进行类型检查，而且Hummingbird的性能开销是合理的。我们将Hummingbird应用于一个Rails应用程序的早期版本，发现了几个引入的类型错误，然后进行了修复。最后，我们将演示在Rails开发模式下使用Hummingbird在应用实时更新时对应用程序进行类型检查。
{"title":"Just-in-time static type checking for dynamic languages","authors":"Brianna M. Ren, J. Foster","doi":"10.1145/2908080.2908127","DOIUrl":"https://doi.org/10.1145/2908080.2908127","url":null,"abstract":"Dynamic languages such as Ruby, Python, and JavaScript have many compelling benefits, but the lack of static types means subtle errors can remain latent in code for a long time. While many researchers have developed various systems to bring some of the benefits of static types to dynamic languages, prior approaches have trouble dealing with metaprogramming, which generates code as the program executes. In this paper, we propose Hummingbird, a new system that uses a novel technique, just-in-time static type checking, to type check Ruby code even in the presence of metaprogramming. In Hummingbird, method type signatures are gathered dynamically at run-time, as those methods are created. When a method is called, Hummingbird statically type checks the method body against current type signatures. Thus, Hummingbird provides thorough static checks on a per-method basis, while also allowing arbitrarily complex metaprogramming. For performance, Hummingbird memoizes the static type checking pass, invalidating cached checks only if necessary. We formalize Hummingbird using a core, Ruby-like language and prove it sound. To evaluate Hummingbird, we applied it to six apps, including three that use Ruby on Rails, a powerful framework that relies heavily on metaprogramming. We found that all apps typecheck successfully using Hummingbird, and that Hummingbird's performance overhead is reasonable. We applied Hummingbird to earlier versions of one Rails app and found several type errors that had been introduced and then fixed. Lastly, we demonstrate using Hummingbird in Rails development mode to typecheck an app as live updates are applied to it.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127552958","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}
We present Refined TypeScript (RSC), a lightweight refinement type system for TypeScript, that enables static verification of higher-order, imperative programs. We develop a formal system for RSC that delineates the interaction between refinement types and mutability, and enables flow-sensitive reasoning by translating input programs to an equivalent intermediate SSA form. By establishing type safety for the intermediate form, we prove safety for the input programs. Next, we extend the core to account for imperative and dynamic features of TypeScript, including overloading, type reflection, ad hoc type hierarchies and object initialization. Finally, we evaluate RSC on a set of real-world benchmarks, including parts of the Octane benchmarks, D3, Transducers, and the TypeScript compiler. We show how RSC successfully establishes a number of value dependent properties, such as the safety of array accesses and downcasts, while incurring a modest overhead in type annotations and code restructuring.
{"title":"Refinement types for TypeScript","authors":"Panagiotis Vekris, B. Cosman, Ranjit Jhala","doi":"10.1145/2908080.2908110","DOIUrl":"https://doi.org/10.1145/2908080.2908110","url":null,"abstract":"We present Refined TypeScript (RSC), a lightweight refinement type system for TypeScript, that enables static verification of higher-order, imperative programs. We develop a formal system for RSC that delineates the interaction between refinement types and mutability, and enables flow-sensitive reasoning by translating input programs to an equivalent intermediate SSA form. By establishing type safety for the intermediate form, we prove safety for the input programs. Next, we extend the core to account for imperative and dynamic features of TypeScript, including overloading, type reflection, ad hoc type hierarchies and object initialization. Finally, we evaluate RSC on a set of real-world benchmarks, including parts of the Octane benchmarks, D3, Transducers, and the TypeScript compiler. We show how RSC successfully establishes a number of value dependent properties, such as the safety of array accesses and downcasts, while incurring a modest overhead in type annotations and code restructuring.","PeriodicalId":178839,"journal":{"name":"Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134314914","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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