A type theory for memory allocation and data layout

PetersenLeaf,; HarperRobert,; CraryKarl,; PfenningFrank,

doi:10.1145/640128.604147

Cited by 9 publications

(17 citation statements)

References 19 publications

(18 reference statements)

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“…In other words, linear functions between two coherence spaces are less than stable functions between the same domains. Since the syntactical constraints of S PCF forbid useless duplications of redexes, we are utterly convinced that S PCF can be fruitfully exploited in research fields like optimal evaluation (see for instance [2,31,34]), implicit computational complexity (see for instance [4,5,14]), linear computation (see for instance [1,13]). Further motivations are theoretical.…”

Section: Introductionmentioning

confidence: 99%

Semantically linear programming languages

Paolini¹,

Piccolo²

2008

Proceedings of the 10th International ACM SIGPLAN Conference on Principles and Practice of Declarative Programming

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We propose a paradigmatic programming language (called S PCF) which is linear in a semantic sense. S PCF is not syntactically linear, namely its programs can contain more than one occurrencies of the same variable. We give an interpretation of S PCF into a model of linear coherence spaces and we show that such semantics is fully abstract with respect to our language. Furthermore, we discuss the independence of new syntactical operators and we address the universality problem.

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Section: Introductionmentioning

confidence: 99%

Semantically linear programming languages

Paolini¹,

Piccolo²

2008

Proceedings of the 10th International ACM SIGPLAN Conference on Principles and Practice of Declarative Programming

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“…Data layout is the way data is structured in a data file or simply stored [3]. There are two basic data layouts; row oriented storage layout and column oriented storage layout.…”

Section: Background and Related Studymentioning

confidence: 99%

Balancing scalability, performance and fault tolerance for structured data (BSPF)

Khalid¹,

Afzal²,

Aftab³

2014

16th International Conference on Advanced Communication Technology

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Abstract-Analytical business applications generate reports that give a trend predicting insight into the organization's future, estimating the financial graphs and risk factors. These applications work on huge amounts of data, which comprises of decades of market and company records, and decision logs of an organization. Today, limit of big data is touching zeta-bytes and the structured data makes only 20% of today's data. 20% of a giga-byte can be ignorable in comparison to big data but 20% of big data itself cannot be neglected. Traditional data management tools are like step-dads when it comes to running cross table analytical queries on structured data in distributed processing environment; response time to these data management tools are high because of the ill-aligned data sets and complex hierarchy of distributed computing environment. Data alignment requires a complete shift in data deployment paradigm from row oriented storage layout to column oriented storage layout, and complex hierarchy of distributed computing environment can be handled by keeping metadata of entire data set. Paper proposes an approach to ease the deployment of structured data into the distributed processing environment by arranging data into column-wise combinational entities. Response time to analytical queries can be lowered with the support of two concepts; Shared architecture and Multi path query execution. Highly scalable systems are Shared Nothing architecture based but degradation in performance and fault tolerance are the side effects that came with high scalability. Proposed method is an effort to balance the equation between scalability, performance and fault tolerance. And due to the limited scope of this paper we concentrate on issues and solutions for structured data only. Shared architecture and active backup helps improving the system's performance by sharing the work-load-per-node. BSPF's clustering methodology sheds the data pressure points to minimize the data loss per node crash.

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“…Peterson et al [22] proposed a programming language with a linear type system to support memory allocation and data layout. Their "ordered multiplicative" is in fact an adjacency type constructor.…”

Section: Other Workmentioning

confidence: 99%

The Logical Approach to Low-Level Stack Reasoning

Jiang

Guo

Chen

2009

2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering

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Formal verification of low-level programs often requires explicit reasoning and specification of runtime stacks. Treating stacks naively as parts of ordinary heaps can lead to very complex specifications and make proof construction more difficult. In this paper, we develop a new logic system based on the idea of memory adjacency from previous work on stack typing. It is a variant of bunched logic and it can be easily integrated into separation logic to reason about the interaction between stacks and heaps. Our logic is especially convenient for reasoning about stack operations, and it greatly simplifies both the specification and the proof of properties about stack-allocated data.

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A type theory for memory allocation and data layout

Cited by 9 publications

References 19 publications

Semantically linear programming languages

Semantically linear programming languages

Balancing scalability, performance and fault tolerance for structured data (BSPF)

The Logical Approach to Low-Level Stack Reasoning

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