Nephele/PACTs

Battré, Dominic; Ewen, Stephan; Hueske, Fabian; Kao, Odej; Markl, Volker; Warneke, Daniel

doi:10.1145/1807128.1807148

Cited by 199 publications

(19 citation statements)

References 16 publications

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“…Examples of these patterns include MapReduce [4], All-Pairs [5], Sector/Sphere [6] and PACT [7]. The advantages of these patterns include: (i) support data distribution and parallel data processing with distributed data on multiple nodes/cores; (ii) provide a higher-level programming model to facilitate user program parallelization; (iii) follow a "moving computation to data" principle that reduces data movement overheads; (iv) have good scalability and performance acceleration when executing on distributed resources; (v) support run-time features such as fault-tolerance; (vi) simplify the difficulty for parallel programming in comparison to traditional parallel programming interfaces such MPI [8] and OpenMP [9].…”

Section: Distributed Data-parallel Patterns and Execution Enginesmentioning

confidence: 99%

“…In Stratosphere, DDP patterns are expressed using the Parallelization Contract (PACTs) programming model [7], and the PACT compiler converts PACT programs into jobs for the Nephele execution engine [19]. DryadLINQ [20] is a high-level programming language consisting of .NET constructs for operating on datasets and runs on Dryad [21], a distributed data flow execution engine.…”

Section: Related Workmentioning

confidence: 99%

“…The Stratosphere system is comprised of two main parts: the Parallelization Contract (PACT) programming model [7] and Nephele execution engine [19]. A PACT contains three components: (i) an input contract which specifies the data-parallel pattern, e.g., Map, Reduce, etc.…”

Section: Ddp Execution Enginesmentioning

confidence: 99%

“…Each DDP actor corresponds to a particular data-parallel pattern, and there are actors for Map, Reduce, Cross, CoGroup, and Match. The semantics of these data-parallel patterns are the same with the input contracts in the PACT programming model [7]. Similar to other actors, the DDP actors can be linked together to form a chain of tasks, and can be nested hierarchically as part of larger workflows.…”

Section: Ddp Actorsmentioning

confidence: 99%

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A Framework for Distributed Data-Parallel Execution in the Kepler Scientific Workflow System

Wang

Crawl

Altıntaş

2012

Procedia Computer Science

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Distributed Data-Parallel (DDP) patterns such as MapReduce have become increasingly popular as solutions to facilitate data-intensive applications, resulting in a number of systems supporting DDP workflows. Yet, applications or workflows built using these patterns are usually tightly-coupled with the underlying DDP execution engine they select. We present a framework for distributed data-parallel execution in the Kepler scientific workflow system that enables users to easily switch between different DDP execution engines. We describe a set of DDP actors based on DDP patterns and directors for DDP workflow executions within the presented framework. We demonstrate how DDP workflows can be easily composed in the Kepler graphic user interface through the reuse of these DDP actors and directors and how the generated DDP workflows can be executed in different distributed environments. Via a bioinformatics usecase, we discuss the usability of the proposed framework and validate its execution scalability.

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Section: Distributed Data-parallel Patterns and Execution Enginesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Ddp Execution Enginesmentioning

confidence: 99%

Section: Ddp Actorsmentioning

confidence: 99%

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A Framework for Distributed Data-Parallel Execution in the Kepler Scientific Workflow System

Wang

Crawl

Altıntaş

2012

Procedia Computer Science

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“…Our Stratosphere system is together with Hadoop one of the first stacks in this area, as it combines an execution engine Nephele for massively-parallel cloud infrastructures, PACT programming model [6] for parallel description and execution of generalized map / reduce operators including primitives that allow the easy specification, automatic optimization, and efficient execution of joins as well as a declarative and query language. Extensive experimental evaluations with different use-case scenario placed Stratosphere among the most prominent data analytics stack that enables the extraction, analysis, and integration of heterogeneous data sets, ranging from strictly structured relational data, to unstructured text data, and semi-structured data.…”

Section: Introductionmentioning

confidence: 99%

Memorization of Materialization Points

Höger

Kao

2013

2013 IEEE 16th International Conference on Computational Science and Engineering

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Data streaming frameworks, constructed to work on large numbers of processing nodes in order to analyze big data, are fault-prone. Not only the large amount of nodes and network components that could fail are a source of errors. Development of data analyzing jobs has the disadvantage that errors or wrong assumptions about the input data may only be detected in productive processing. This usually leads to a reexecution of the entire job and re-computing all input data. This can be a tremendous profuseness of computing time if most of the job's tasks are not affected by these changes and therefore process and produce the same exact data again.This paper describes an approach to use materialized intermediate data from previous job executions to reduce the number of tasks that have to be re-executed in case of an updated job. Saving intermediate data to disk is a common technique to achieve fault tolerance in data streaming systems. These intermediate results can be used for memoization to avoid needless re-execution of tasks. We show that memoization can decrease the runtime of an updated job distinctly.

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