Ruyi Ji, Jingjing Liang, Yingfei Xiong, Lu Zhang, Zhenjiang Hu
Interactive program synthesis aims to solve the ambiguity in specifications, and selecting the proper question to minimize the rounds of interactions is critical to the performance of interactive program synthesis. In this paper we address this question selection problem and propose two algorithms. SampleSy approximates a state-of-the-art strategy proposed for optimal decision tree and has a short response time to enable interaction. EpsSy further reduces the rounds of interactions by approximating SampleSy with a bounded error rate. To implement the two algorithms, we further propose VSampler, an approach to sampling programs from a probabilistic context-free grammar based on version space algebra. The evaluation shows the effectiveness of both algorithms.
{"title":"Question selection for interactive program synthesis","authors":"Ruyi Ji, Jingjing Liang, Yingfei Xiong, Lu Zhang, Zhenjiang Hu","doi":"10.1145/3385412.3386025","DOIUrl":"https://doi.org/10.1145/3385412.3386025","url":null,"abstract":"Interactive program synthesis aims to solve the ambiguity in specifications, and selecting the proper question to minimize the rounds of interactions is critical to the performance of interactive program synthesis. In this paper we address this question selection problem and propose two algorithms. SampleSy approximates a state-of-the-art strategy proposed for optimal decision tree and has a short response time to enable interaction. EpsSy further reduces the rounds of interactions by approximating SampleSy with a bounded error rate. To implement the two algorithms, we further propose VSampler, an approach to sampling programs from a probabilistic context-free grammar based on version space algebra. The evaluation shows the effectiveness of both algorithms.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86913072","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}
Quantified first-order formulas, often with quantifier alternations, are increasingly used in the verification of complex systems. While automated theorem provers for first-order logic are becoming more robust, invariant inference tools that handle quantifiers are currently restricted to purely universal formulas. We define and analyze first-order quantified separators and their application to inferring quantified invariants with alternations. A separator for a given set of positively and negatively labeled structures is a formula that is true on positive structures and false on negative structures. We investigate the problem of finding a separator from the class of formulas in prenex normal form with a bounded number of quantifiers and show this problem is NP-complete by reduction to and from SAT. We also give a practical separation algorithm, which we use to demonstrate the first invariant inference procedure able to infer invariants with quantifier alternations.
{"title":"First-order quantified separators","authors":"Jason R. Koenig, O. Padon, N. Immerman, A. Aiken","doi":"10.1145/3385412.3386018","DOIUrl":"https://doi.org/10.1145/3385412.3386018","url":null,"abstract":"Quantified first-order formulas, often with quantifier alternations, are increasingly used in the verification of complex systems. While automated theorem provers for first-order logic are becoming more robust, invariant inference tools that handle quantifiers are currently restricted to purely universal formulas. We define and analyze first-order quantified separators and their application to inferring quantified invariants with alternations. A separator for a given set of positively and negatively labeled structures is a formula that is true on positive structures and false on negative structures. We investigate the problem of finding a separator from the class of formulas in prenex normal form with a bounded number of quantifiers and show this problem is NP-complete by reduction to and from SAT. We also give a practical separation algorithm, which we use to demonstrate the first invariant inference procedure able to infer invariants with quantifier alternations.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84309847","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}
Georgios Sakkas, Madeline Endres, B. Cosman, Westley Weimer, Ranjit Jhala
We introduce Analytic Program Repair, a data-driven strategy for providing feedback for type-errors via repairs for the erroneous program. Our strategy is based on insight that similar errors have similar repairs. Thus, we show how to use a training dataset of pairs of ill-typed programs and their fixed versions to: (1) learn a collection of candidate repair templates by abstracting and partitioning the edits made in the training set into a representative set of templates; (2) predict the appropriate template from a given error, by training multi-class classifiers on the repair templates used in the training set; (3) synthesize a concrete repair from the template by enumerating and ranking correct (e.g. well-typed) terms matching the predicted template. We have implemented our approach in Rite: a type error reporting tool for OCaml programs. We present an evaluation of the accuracy and efficiency of Rite on a corpus of 4,500 ill-typed Ocaml programs drawn from two instances of an introductory programming course, and a user-study of the quality of the generated error messages that shows the locations and final repair quality to be better than the state-of-the-art tool in a statistically-significant manner.
{"title":"Type error feedback via analytic program repair","authors":"Georgios Sakkas, Madeline Endres, B. Cosman, Westley Weimer, Ranjit Jhala","doi":"10.1145/3385412.3386005","DOIUrl":"https://doi.org/10.1145/3385412.3386005","url":null,"abstract":"We introduce Analytic Program Repair, a data-driven strategy for providing feedback for type-errors via repairs for the erroneous program. Our strategy is based on insight that similar errors have similar repairs. Thus, we show how to use a training dataset of pairs of ill-typed programs and their fixed versions to: (1) learn a collection of candidate repair templates by abstracting and partitioning the edits made in the training set into a representative set of templates; (2) predict the appropriate template from a given error, by training multi-class classifiers on the repair templates used in the training set; (3) synthesize a concrete repair from the template by enumerating and ranking correct (e.g. well-typed) terms matching the predicted template. We have implemented our approach in Rite: a type error reporting tool for OCaml programs. We present an evaluation of the accuracy and efficiency of Rite on a corpus of 4,500 ill-typed Ocaml programs drawn from two instances of an introductory programming course, and a user-study of the quality of the generated error messages that shows the locations and final repair quality to be better than the state-of-the-art tool in a statistically-significant manner.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73024820","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}
Sangeeta Chowdhary, Jay P. Lim, Santosh Nagarakatte
Posit is a recently proposed alternative to the floating point representation (FP). It provides tapered accuracy. Given a fixed number of bits, the posit representation can provide better precision for some numbers compared to FP, which has generated significant interest in numerous domains. Being a representation with tapered accuracy, it can introduce high rounding errors for numbers outside the above golden zone. Programmers currently lack tools to detect and debug errors while programming with posits. This paper presents PositDebug, a compile-time instrumentation that performs shadow execution with high precision values to detect various errors in computation using posits. To assist the programmer in debugging the reported error, PositDebug also provides directed acyclic graphs of instructions, which are likely responsible for the error. A contribution of this paper is the design of the metadata per memory location for shadow execution that enables productive debugging of errors with long-running programs. We have used PositDebug to detect and debug errors in various numerical applications written using posits. To demonstrate that these ideas are applicable even for FP programs, we have built a shadow execution framework for FP programs that is an order of magnitude faster than Herbgrind.
{"title":"Debugging and detecting numerical errors in computation with posits","authors":"Sangeeta Chowdhary, Jay P. Lim, Santosh Nagarakatte","doi":"10.1145/3385412.3386004","DOIUrl":"https://doi.org/10.1145/3385412.3386004","url":null,"abstract":"Posit is a recently proposed alternative to the floating point representation (FP). It provides tapered accuracy. Given a fixed number of bits, the posit representation can provide better precision for some numbers compared to FP, which has generated significant interest in numerous domains. Being a representation with tapered accuracy, it can introduce high rounding errors for numbers outside the above golden zone. Programmers currently lack tools to detect and debug errors while programming with posits. This paper presents PositDebug, a compile-time instrumentation that performs shadow execution with high precision values to detect various errors in computation using posits. To assist the programmer in debugging the reported error, PositDebug also provides directed acyclic graphs of instructions, which are likely responsible for the error. A contribution of this paper is the design of the metadata per memory location for shadow execution that enables productive debugging of errors with long-running programs. We have used PositDebug to detect and debug errors in various numerical applications written using posits. To demonstrate that these ideas are applicable even for FP programs, we have built a shadow execution framework for FP programs that is an order of magnitude faster than Herbgrind.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78555751","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}
Concurrent separation logics have had great success reasoning about concurrent data structures. This success stems from their application of modularity on multiple levels, leading to proofs that are decomposed according to program structure, program state, and individual threads. Despite these advances, it remains difficult to achieve proof reuse across different data structure implementations. For the large class of search structures, we demonstrate how one can achieve further proof modularity by decoupling the proof of thread safety from the proof of structural integrity. We base our work on the template algorithms of Shasha and Goodman that dictate how threads interact but abstract from the concrete layout of nodes in memory. Building on the recently proposed flow framework of compositional abstractions and the separation logic Iris, we show how to prove correctness of template algorithms, and how to instantiate them to obtain multiple verified implementations. We demonstrate our approach by mechanizing the proofs of three concurrent search structure templates, based on link, give-up, and lock-coupling synchronization, and deriving verified implementations based on B-trees, hash tables, and linked lists. These case studies include algorithms used in real-world file systems and databases, which have been beyond the capability of prior automated or mechanized verification techniques. In addition, our approach reduces proof complexity and is able to achieve significant proof reuse.
{"title":"Verifying concurrent search structure templates","authors":"Siddharth Krishna, Nisarg Patel, D. Shasha","doi":"10.1145/3385412.3386029","DOIUrl":"https://doi.org/10.1145/3385412.3386029","url":null,"abstract":"Concurrent separation logics have had great success reasoning about concurrent data structures. This success stems from their application of modularity on multiple levels, leading to proofs that are decomposed according to program structure, program state, and individual threads. Despite these advances, it remains difficult to achieve proof reuse across different data structure implementations. For the large class of search structures, we demonstrate how one can achieve further proof modularity by decoupling the proof of thread safety from the proof of structural integrity. We base our work on the template algorithms of Shasha and Goodman that dictate how threads interact but abstract from the concrete layout of nodes in memory. Building on the recently proposed flow framework of compositional abstractions and the separation logic Iris, we show how to prove correctness of template algorithms, and how to instantiate them to obtain multiple verified implementations. We demonstrate our approach by mechanizing the proofs of three concurrent search structure templates, based on link, give-up, and lock-coupling synchronization, and deriving verified implementations based on B-trees, hash tables, and linked lists. These case studies include algorithms used in real-world file systems and databases, which have been beyond the capability of prior automated or mechanized verification techniques. In addition, our approach reduces proof complexity and is able to achieve significant proof reuse.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90584749","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}
The efficient implementation of function calls and non-local control transfers is a critical part of modern language implementations and is important in the implementation of everything from recursion, higher-order functions, concurrency and coroutines, to task-based parallelism. In a compiler, these features can be supported by a variety of mechanisms, including call stacks, segmented stacks, and heap-allocated continuation closures. An implementor of a high-level language with advanced control features might ask the question ``what is the best choice for my implementation?'' Unfortunately, the current literature does not provide much guidance, since previous studies suffer from various flaws in methodology and are outdated for modern hardware. In the absence of recent, well-normalized measurements and a holistic overview of their implementation specifics, the path of least resistance when choosing a strategy is to trust folklore, but the folklore is also suspect. This paper attempts to remedy this situation by providing an ``apples-to-apples'' comparison of six different approaches to implementing call stacks and continuations. This comparison uses the same source language, compiler pipeline, LLVM-backend, and runtime system, with the only differences being those required by the differences in implementation strategy. We compare the implementation challenges of the different approaches, their sequential performance, and their suitability to support advanced control mechanisms, including supporting heavily threaded code. In addition to the comparison of implementation strategies, the paper's contributions also include a number of useful implementation techniques that we discovered along the way.
{"title":"From folklore to fact: comparing implementations of stacks and continuations","authors":"K. Farvardin, John H. Reppy","doi":"10.1145/3385412.3385994","DOIUrl":"https://doi.org/10.1145/3385412.3385994","url":null,"abstract":"The efficient implementation of function calls and non-local control transfers is a critical part of modern language implementations and is important in the implementation of everything from recursion, higher-order functions, concurrency and coroutines, to task-based parallelism. In a compiler, these features can be supported by a variety of mechanisms, including call stacks, segmented stacks, and heap-allocated continuation closures. An implementor of a high-level language with advanced control features might ask the question ``what is the best choice for my implementation?'' Unfortunately, the current literature does not provide much guidance, since previous studies suffer from various flaws in methodology and are outdated for modern hardware. In the absence of recent, well-normalized measurements and a holistic overview of their implementation specifics, the path of least resistance when choosing a strategy is to trust folklore, but the folklore is also suspect. This paper attempts to remedy this situation by providing an ``apples-to-apples'' comparison of six different approaches to implementing call stacks and continuations. This comparison uses the same source language, compiler pipeline, LLVM-backend, and runtime system, with the only differences being those required by the differences in implementation strategy. We compare the implementation challenges of the different approaches, their sequential performance, and their suitability to support advanced control mechanisms, including supporting heavily threaded code. In addition to the comparison of implementation strategies, the paper's contributions also include a number of useful implementation techniques that we discovered along the way.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88401975","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}
Batteryless energy-harvesting devices eliminate the need in batteries for deployed sensor systems, enabling longer lifetime and easier maintenance. However, such devices cannot support an event-driven execution model (e.g., periodic or reactive execution), restricting the use cases and hampering real-world deployment. Without knowing exactly how much energy can be harvested in the future, robustly scheduling periodic and reactive workloads is challenging. We introduce CatNap, an event-driven energy-harvesting system with a new programming model that asks the programmer to express a subset of the code that is time-critical. CatNap isolates and reserves energy for the time-critical code, reliably executing it on schedule while deferring execution of the rest of the code. CatNap degrades execution quality when a decrease in the incoming power renders it impossible to maintain its schedule. Our evaluation on a real energy-harvesting setup shows that CatNap works well with end-to-end, real-world deployment settings. CatNap reliably runs periodic events when a prior system misses the deadline by 7.3x and supports reactive applications with a 100% success rate when a prior work shows less than a 2% success rate.
{"title":"Adaptive low-overhead scheduling for periodic and reactive intermittent execution","authors":"Kiwan Maeng, Brandon Lucia","doi":"10.1145/3385412.3385998","DOIUrl":"https://doi.org/10.1145/3385412.3385998","url":null,"abstract":"Batteryless energy-harvesting devices eliminate the need in batteries for deployed sensor systems, enabling longer lifetime and easier maintenance. However, such devices cannot support an event-driven execution model (e.g., periodic or reactive execution), restricting the use cases and hampering real-world deployment. Without knowing exactly how much energy can be harvested in the future, robustly scheduling periodic and reactive workloads is challenging. We introduce CatNap, an event-driven energy-harvesting system with a new programming model that asks the programmer to express a subset of the code that is time-critical. CatNap isolates and reserves energy for the time-critical code, reliably executing it on schedule while deferring execution of the rest of the code. CatNap degrades execution quality when a decrease in the incoming power renders it impossible to maintain its schedule. Our evaluation on a real energy-harvesting setup shows that CatNap works well with end-to-end, real-world deployment settings. CatNap reliably runs periodic events when a prior system misses the deadline by 7.3x and supports reactive applications with a 100% success rate when a prior work shows less than a 2% success rate.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90906585","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}
The hierarchical memory system with increasingly small and increasingly fast memory closer to the CPU has for long been at the heart of hiding, or mitigating the performance gap between memories and processors. To utilise this hardware, programs must be written to exhibit good object locality. In languages like C/C++, programmers can carefully plan how objects should be laid out (albeit time consuming and error-prone); for managed languages, especially ones with moving garbage collectors, a manually created optimal layout may be destroyed in the process of object relocation. For managed languages that present an abstract view of memory, the solution lies in making the garbage collector aware of object locality, and strive to achieve and maintain good locality, even in the face of multi-phased programs that exhibit different behaviour across different phases. This paper presents a GC design that dynamically reorganises objects in the order mutators access them, and additionally strives to separate frequently and infrequently used objects in memory. This improves locality and the efficiency of hardware prefetching. Identifying frequently used objects is done at run-time, with small overhead. HCSGC also offers tunability, for shifting relocation work towards mutators, or for more or less aggressive object relocation. The ideas are evaluated in the context of the ZGC collector on OpenJDK and yields performance improvements of 5% (tradebeans), 9% (h2) and an impressive 25–45% (JGraphT), all with 95% confidence. For SPECjbb, results are inconclusive due to a fluctuating baseline.
{"title":"Improving program locality in the GC using hotness","authors":"A. Yang, Erik Österlund, Tobias Wrigstad","doi":"10.1145/3385412.3385977","DOIUrl":"https://doi.org/10.1145/3385412.3385977","url":null,"abstract":"The hierarchical memory system with increasingly small and increasingly fast memory closer to the CPU has for long been at the heart of hiding, or mitigating the performance gap between memories and processors. To utilise this hardware, programs must be written to exhibit good object locality. In languages like C/C++, programmers can carefully plan how objects should be laid out (albeit time consuming and error-prone); for managed languages, especially ones with moving garbage collectors, a manually created optimal layout may be destroyed in the process of object relocation. For managed languages that present an abstract view of memory, the solution lies in making the garbage collector aware of object locality, and strive to achieve and maintain good locality, even in the face of multi-phased programs that exhibit different behaviour across different phases. This paper presents a GC design that dynamically reorganises objects in the order mutators access them, and additionally strives to separate frequently and infrequently used objects in memory. This improves locality and the efficiency of hardware prefetching. Identifying frequently used objects is done at run-time, with small overhead. HCSGC also offers tunability, for shifting relocation work towards mutators, or for more or less aggressive object relocation. The ideas are evaluated in the context of the ZGC collector on OpenJDK and yields performance improvements of 5% (tradebeans), 9% (h2) and an impressive 25–45% (JGraphT), all with 95% confidence. For SPECjbb, results are inconclusive due to a fluctuating baseline.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85962213","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}
Graph analytics is an important way to understand relationships in real-world applications. At the age of big data, graphs have grown to billions of edges. This motivates distributed graph processing. Graph processing frameworks ask programmers to specify graph computations in user- defined functions (UDFs) of graph-oriented programming model. Due to the nature of distributed execution, current frameworks cannot precisely enforce the semantics of UDFs, leading to unnecessary computation and communication. In essence, there exists a gap between programming model and runtime execution. This paper proposes SympleGraph, a novel distributed graph processing framework that precisely enforces loop-carried dependency, i.e., when a condition is satisfied by a neighbor, all following neighbors can be skipped. SympleGraph instruments the UDFs to express the loop-carried dependency, then the distributed execution framework enforces the precise semantics by performing dependency propagation dynamically. Enforcing loop-carried dependency requires the sequential processing of the neighbors of each vertex distributed in different nodes. Therefore, the major challenge is to enable sufficient parallelism to achieve high performance. We propose to use circulant scheduling in the framework to allow different machines to process disjoint sets of edges/vertices in parallel while satisfying the sequential requirement. It achieves a good trade-off between precise semantics and parallelism. The significant speedups in most graphs and algorithms indicate that the benefits of eliminating unnecessary computation and communication overshadow the reduced parallelism. Communication efficiency is further optimized by 1) selectively propagating dependency for large-degree vertices to increase net benefits; 2) double buffering to hide communication latency. In a 16-node cluster, SympleGraph outperforms the state-of-the-art system Gemini and D-Galois on average by 1.42× and 3.30×, and up to 2.30× and 7.76×, respectively. The communication reduction compared to Gemini is 40.95% on average and up to 67.48%.
{"title":"SympleGraph: distributed graph processing with precise loop-carried dependency guarantee","authors":"Youwei Zhuo, Jingji Chen, Qinyi Luo, Yanzhi Wang, Hailong Yang, D. Qian, Xuehai Qian","doi":"10.1145/3385412.3385961","DOIUrl":"https://doi.org/10.1145/3385412.3385961","url":null,"abstract":"Graph analytics is an important way to understand relationships in real-world applications. At the age of big data, graphs have grown to billions of edges. This motivates distributed graph processing. Graph processing frameworks ask programmers to specify graph computations in user- defined functions (UDFs) of graph-oriented programming model. Due to the nature of distributed execution, current frameworks cannot precisely enforce the semantics of UDFs, leading to unnecessary computation and communication. In essence, there exists a gap between programming model and runtime execution. This paper proposes SympleGraph, a novel distributed graph processing framework that precisely enforces loop-carried dependency, i.e., when a condition is satisfied by a neighbor, all following neighbors can be skipped. SympleGraph instruments the UDFs to express the loop-carried dependency, then the distributed execution framework enforces the precise semantics by performing dependency propagation dynamically. Enforcing loop-carried dependency requires the sequential processing of the neighbors of each vertex distributed in different nodes. Therefore, the major challenge is to enable sufficient parallelism to achieve high performance. We propose to use circulant scheduling in the framework to allow different machines to process disjoint sets of edges/vertices in parallel while satisfying the sequential requirement. It achieves a good trade-off between precise semantics and parallelism. The significant speedups in most graphs and algorithms indicate that the benefits of eliminating unnecessary computation and communication overshadow the reduced parallelism. Communication efficiency is further optimized by 1) selectively propagating dependency for large-degree vertices to increase net benefits; 2) double buffering to hide communication latency. In a 16-node cluster, SympleGraph outperforms the state-of-the-art system Gemini and D-Galois on average by 1.42× and 3.30×, and up to 2.30× and 7.76×, respectively. The communication reduction compared to Gemini is 40.95% on average and up to 67.48%.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76745372","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}
Bernhard Kragl, C. Enea, T. Henzinger, Suha Orhun Mutluergil, S. Qadeer
Asynchronous programs are notoriously difficult to reason about because they spawn computation tasks which take effect asynchronously in a nondeterministic way. Devising inductive invariants for such programs requires understanding and stating complex relationships between an unbounded number of computation tasks in arbitrarily long executions. In this paper, we introduce inductive sequentialization, a new proof rule that sidesteps this complexity via a sequential reduction, a sequential program that captures every behavior of the original program up to reordering of coarse-grained commutative actions. A sequential reduction of a concurrent program is easy to reason about since it corresponds to a simple execution of the program in an idealized synchronous environment, where processes act in a fixed order and at the same speed. We have implemented and integrated our proof rule in the CIVL verifier, allowing us to provably derive fine-grained implementations of asynchronous programs. We have successfully applied our proof rule to a diverse set of message-passing protocols, including leader election protocols, two-phase commit, and Paxos.
{"title":"Inductive sequentialization of asynchronous programs","authors":"Bernhard Kragl, C. Enea, T. Henzinger, Suha Orhun Mutluergil, S. Qadeer","doi":"10.1145/3385412.3385980","DOIUrl":"https://doi.org/10.1145/3385412.3385980","url":null,"abstract":"Asynchronous programs are notoriously difficult to reason about because they spawn computation tasks which take effect asynchronously in a nondeterministic way. Devising inductive invariants for such programs requires understanding and stating complex relationships between an unbounded number of computation tasks in arbitrarily long executions. In this paper, we introduce inductive sequentialization, a new proof rule that sidesteps this complexity via a sequential reduction, a sequential program that captures every behavior of the original program up to reordering of coarse-grained commutative actions. A sequential reduction of a concurrent program is easy to reason about since it corresponds to a simple execution of the program in an idealized synchronous environment, where processes act in a fixed order and at the same speed. We have implemented and integrated our proof rule in the CIVL verifier, allowing us to provably derive fine-grained implementations of asynchronous programs. We have successfully applied our proof rule to a diverse set of message-passing protocols, including leader election protocols, two-phase commit, and Paxos.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75452840","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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