RaftLib

Beard, Jonathan Curtis; Li, Peng; Chamberlain, Roger D.

doi:10.1145/2712386.2712400

Cited by 14 publications

(1 citation statement)

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“…We believe that such systems can be built on top of our parallelization and scheduling framework without much effort. We also note that several architectural proposals [3,13,21,30] exist in the literature for a shared-memory streaming parallelization framework, but none of them address dynamic scheduling in the ordered setting or compare different scheduling heuristics empirically, which is a key contribution of this paper.…”

Section: Dynamic Solutionsmentioning

confidence: 99%

Scaling Ordered Stream Processing on Shared-Memory Multicores

Prasaad

Ramalingam

Rajan

2019

Proceedings of Real-Time Business Intelligence and Analytics

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Many modern applications require real-time processing of large volumes of high-speed data. Such data processing needs can be modeled as a streaming computation. A streaming computation is specified as a dataflow graph that exposes multiple opportunities for parallelizing its execution, in the form of data, pipeline and task parallelism. On the other hand, many important applications require that processing of the stream be ordered, where inputs are processed in the same order as they arrive. There is a fundamental conflict between ordered processing and parallelizing the streaming computation. This paper focuses on the problem of effectively parallelizing ordered streaming computations on a shared-memory multicore machine.We first address the key challenges in exploiting data parallelism in the ordered setting. We present a low-latency, non-blocking concurrent data structure to order outputs produced by concurrent workers on an operator. We also propose a new approach to parallelizing partitioned stateful operators that can handle load imbalance across partitions effectively and mostly avoid delays due to ordering. We illustrate the trade-offs and effectiveness of our concurrent data-structures on micro-benchmarks and streaming queries from the TPCx-BB [16] benchmark. We then present an adaptive runtime that dynamically maps the exposed parallelism in the computation to that of the machine. We propose several intuitive scheduling heuristics and compare them empirically on the TPCx-BB queries. We find that for streaming computations, heuristics that exploit as much pipeline parallelism as possible perform better than those that seek to exploit data parallelism.

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Section: Dynamic Solutionsmentioning

confidence: 99%

Scaling Ordered Stream Processing on Shared-Memory Multicores

Prasaad

Ramalingam

Rajan

2019

Proceedings of Real-Time Business Intelligence and Analytics

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A generic parallel pattern interface for stream and data processing

Astorga

Dolz

Fernández

et al. 2017

Concurrency and Computation

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SummaryCurrent parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge.Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations. An approach to relieve developers from this burden is the use of pattern-based parallel programming frameworks, such as SkePU, 2FastFlow 3 , or Intel TBB. 4 In this sense, design patterns provide a way to encapsulate (using a building blocks approach) algorithmic aspects, allowing users to implement more robust, readable, and portable solutions with such a high-level of abstraction. Basically, these patterns instantiate parallelism while hide away the complexity of concurrency mechanisms, eg, thread management, synchronizations, or data sharing. Examples of applications coming from multiple domains (eg, financial, medical, and mathematical) and improving their performance through parallel programming design patterns, can be widely found in the literature. [5][6][7] Nevertheless, although all these skeletons aim to simplify the development of parallel applications, there is not a unified standard. 8 Therefore, users require understanding different frameworks, not only to decide which fits best for their purposes, but also to properly use them. Not to mention the migration efforts of applications among frameworks, which becomes as well an arduous task.In order to mitigate this situation, this paper presents GRPPI, a generic and reusable high-level C++ parallel pattern interface that comprises both stream and data-parallel patterns. In general, the goal of

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