Concurrent Collections

Budimlić, Zoran; Burke, Michael G.; Cavé, Vincent; Knobe, Kathleen; Lowney, Geoff; Newton, Ryan; Palsberg, Jens; Peixotto, David M.; Sarkar, Vivek; Schlimbach, F.; Taşırlar, Sağnak

doi:10.1155/2010/521797

Cited by 106 publications

(87 citation statements)

References 10 publications

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“…The Concurrent Collections (CnC) family of languages [3] is a coordination language which is very much inspired by dynamic dataflow. CnC utilizes a separation of concerns, providing a tuning specification to achieve performance.…”

Section: Related Workmentioning

confidence: 99%

An Implementation of the Codelet Model

Suettlerlein

Zuckerman

Gao

2013

Euro-Par 2013 Parallel Processing

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Abstract. Chip architectures are shifting from few, faster, functionally heavy cores to abundant, slower, simpler cores to address pressing physical limitations such as energy consumption and heat expenditure. As architectural trends continue to fluctuate, we propose a novel program execution model, the Codelet model, which is designed for new systems tasked with efficiently managing varying resources. The Codelet model is a fine-grained dataflow inspired model extended to address the cumbersome resources available in new architectures. In the following, we define the Codelet execution model as well as provide an implementation named DARTS. Utilizing DARTS and two predominant kernels, matrix multiplication and the Graph 500's breadth first search, we explore the validity of fine-grain execution as a promising and viable execution model for future and current architectures. We show that our runtime is on par or performs better than AMD's highly-optimized parallel library for matrix multication, outperforming it on average by 1.40× with a speedup up to 4×. Our implementation of the parallel BFS outperforms Graph 500's reference implementation (with or without dynamic scheduling) on average by 1.50× with a speed up of up to 2.38×.

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Section: Related Workmentioning

confidence: 99%

An Implementation of the Codelet Model

Suettlerlein

Zuckerman

Gao

2013

Euro-Par 2013 Parallel Processing

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“…The Concurrent Collections(CnC) programming model [8] on which Streaming Concurrent Collection is a prerequisite to understanding the paper and is described in short in this section. CnC has three main building blocks: item collections, control collections and step collections.…”

Section: Cnc Terminologymentioning

confidence: 99%

SCnC: Efficient Unification of Streaming with Dynamic Task Parallelism

Sbîrlea

Shirako

Newton

et al. 2015

Int J Parallel Prog

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Stream processing is a special form of the dataflow execution model that offers extensive opportunities for optimization and automatic parallelization. To take full advantage of the paradigm, however, typically requires programmers to learn a new language and re-implement their applications. This work shows that it is possible to exploit streaming as a safe and automatic optimization of a more general dataflow-based model-one in which computation kernels are written in standard, general-purpose languages and organized as a coordination graph.We propose Streaming Concurrent Collections (SCnC), a streaming system that can efficiently run a subset of programs supported by Concurrent Collections (CnC). CnC is a general purpose parallel programming paradigm with a task-parallel look and feel but based on dataflow graph principles. Its expressivity extends to any arbitrary task graph. Integration of these models would allow application developers to benefit from the performance and tight memory footprint of stream parallelism for eligible subgraphs of their application.In this paper we formally define the requirements (streaming access patterns) needed for using SCnC, and outline a static decision procedure for identifying and processing eligible SCnC subgraphs. We present initial results on an prototype implementation that show that transitioning from general CnC to SCnC leads to a throughput increase of up to 40× for certain benchmarks, and also enable programs with large data sizes to execute in available memory for cases where CnC execution may run out of memory.

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“…Single-assignment variables have been generalized to I-Structures [1] which are essentially single-assignment arrays. CnC [4,2] is a parallel programming model influenced by dynamic dataflow, stream-processing and tuple spaces [8]. In CnC the user provides high-level operations along with the ordering constraints that form a computation dependency graph.…”

Section: Related Workmentioning

confidence: 99%

“…Programming models based on dataflow [1,2] have the potential to simplify parallel programming, since the resulting programs are deterministic. Moreover, dataflow programs can be expressed more declaratively than programs based on mainstream concurrency constructs, such as shared-memory threads and locks, as programmers are only required to specify data and control dependencies.…”

Section: Introductionmentioning

confidence: 99%

FlowPools: A Lock-Free Deterministic Concurrent Dataflow Abstraction

Prokopec

Miller

Schlatter

et al. 2013

Languages and Compilers for Parallel Computing

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Abstract. Implementing correct and deterministic parallel programs is challenging. Even though concurrency constructs exist in popular programming languages to facilitate the task of deterministic parallel programming, they are often too low level, or do not compose well due to underlying blocking mechanisms. In this paper, we present the design and implementation of a fundamental data structure for composable deterministic parallel dataflow computation through the use of functional programming abstractions. Additionally, we provide a correctness proof, showing that the implementation is linearizable, lock-free, and deterministic. Finally, we show experimental results which compare our FlowPool against corresponding operations on other concurrent data structures, and show that in addition to offering new capabilities, FlowPools reduce insertion time by 49 − 54% on a 4-core i7 machine with respect to comparable concurrent queue data structures in the Java standard library.

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Concurrent Collections

Cited by 106 publications

References 10 publications

An Implementation of the Codelet Model

An Implementation of the Codelet Model

SCnC: Efficient Unification of Streaming with Dynamic Task Parallelism

FlowPools: A Lock-Free Deterministic Concurrent Dataflow Abstraction

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