SPBench: a framework for creating benchmarks of stream processing applications

Garcia, Adriano Marques; Griebler, Dalvan; Schepke, Claudio; Fernandes, Luiz Gustavo

doi:10.1007/s00607-021-01025-6

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…It depends on the type of application, the workload, the architecture, and the available resources, among other factors [8]. The configuration can be done to benefit the throughput or latency more, as shown in previous work [13]. On the other hand, programmers have more freedom to fine-tune FastFlow applications compared to TBB.…”

Section: Results Discussion and Final Remarksmentioning

confidence: 99%

“…In previous work we have discussed in detail benchmarks and frameworks related to SPBench [13]. Here, we discuss related work that investigated micro-batching and data stream frequency in stream processing.…”

Section: Related Workmentioning

confidence: 99%

“…SPBench was first introduced on [12], and the first release was fully described and evaluated on [13]. It is open-source and available under the GNU General Public License version 3 (GPLv3).…”

Section: Spbenchmentioning

confidence: 99%

“…We built benchmarks using four applications supported by SPBench [13]: Bzip2, Lane Detection, Face Recognizer, and Ferret (from PARSEC) [16]. We parallelize these applications with Intel TBB and FastFlow.…”

Section: Parallelism Strategiesmentioning

confidence: 99%

“…In the past [12,13], we proposed the SPBench, a framework for creating benchmarks of stream processing applications that makes it easier to assess multiple parallel programming interfaces. In those works, we evaluated the performance of different real-world stream applications under multiple PPIs in terms of latency, throughput, and resource utilization.…”

Section: Introductionmentioning

confidence: 99%

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Micro-batch and data frequency for stream processing on multi-cores

et al. 2023

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Latency or throughput are often critical performance metrics in stream processing. Applications' performance can fluctuate depending on the input stream. This unpredictability is tied to variations in data arrival frequency, data size, complexity, and other factors. Researchers are constantly investigating new ways to mitigate the impact of these variations on performance with self-adaptive techniques involving elasticity or micro-batching. However, there is a lack of benchmarks capable of creating test scenarios to further evaluate these techniques. This work extends and improves the SPBench benchmarking framework to support dynamic micro-batching and data stream frequency management. We also propose a set of algorithms that generate the most commonly used frequency patterns for benchmarking stream processing in related work. It allows the creation of a wide variety of test scenarios. To validate our solution, we use SPBench to create custom benchmarks and evaluate the impact of micro-batching and data stream Micro-batch and Data Frequency for Stream Processing on Multi-cores frequency on the performance of Intel TBB and FastFlow. These are two libraries that leverage stream parallelism for multi-core architectures. Our results demonstrated that most test cases benefited from micro-batches, especially high throughput applications with ordering constraints. For different data stream frequency configurations, TBB ensured the lowest latency, while FastFlow assured higher throughput in shorter pipelines.

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Section: Results Discussion and Final Remarksmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Spbenchmentioning

confidence: 99%

Section: Parallelism Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Micro-batch and data frequency for stream processing on multi-cores

et al. 2023

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Performance and programmability of GrPPI for parallel stream processing on multi-cores

Garcia,

Griebler,

Schepke

et al. 2024

J Supercomput

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GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times.

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