Autonomic and Latency-Aware Degree of Parallelism Management in SPar

Vogel, Adriano; Griebler, Dalvan; Sensi, Daniele De; Danelutto, Marco; Fernandes, Luiz Gustavo

doi:10.1007/978-3-030-10549-5_3

Cited by 8 publications

(12 citation statements)

References 13 publications

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“…We demonstrated how the degree of parallelism impacts the latency of stream items in a previous work, 28 where we proposed a strategy attempting to manage latency by adapting the degree of parallelism. However, latency is only relevant for specific applications.…”

Section: Introductionmentioning

confidence: 92%

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Providing high‐level self‐adaptive abstractions for stream parallelism on multicores

2021

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Stream processing applications are common computing workloads that demand parallelism to increase their performance. As in the past, parallel programming remains a difficult task for application programmers. The complexity increases when application programmers must set nonintuitive parallelism parameters, that is, the degree of parallelism. The main problem is that state-of-the-art libraries use a static degree of parallelism and are not sufficiently abstracted for developing stream processing applications. In this article, we propose a self-adaptive regulation of the degree of parallelism to provide higher-level abstractions. Flexibility is provided to programmers with two new self-adaptive strategies, one is for performance experts, and the other abstracts the need to set a performance goal. We evaluated our solution using compiler transformation rules to generate parallel code with the SPar domain-specific language. The experimental results with real-world applications highlighted higher abstraction levels without significant performance degradation in comparison to static executions. The strategy for performance experts achieved slightly higher performance than the one that works without user-defined performance goals.

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Section: Introductionmentioning

confidence: 92%

“…20% was the most suitable value for video applications during our empirical tests. 28 The threshold is used in line 6 of algorithm 3.…”

Section: A Strategy For User Defined Target Throughputmentioning

confidence: 99%

Providing high‐level self‐adaptive abstractions for stream parallelism on multicores

2021

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“…The entities used are also highly related to the approaches' runtime, specific solutions may or may not support adapting a given entity at run-time. For instance, several approaches adapt the parallelism degree at run-time 36,50,54,90 while others have to change the graph topology to adapt to the parallelism degree. 9 In Reference 9, the graph topology is transformed at run-time, but it remains unclear if the graph was transformed because of their runtime constraints or if their targeted advanced optimizations (e.g., efficiency).…”

Section: Adaptation Actions and Entities Managedmentioning

confidence: 99%

“…RQ6 and the category described in Section 4.2.5 relate to whether the existing approaches focus on providing parallelism abstractions to application programmers. Although nowadays we have frameworks providing high-level programming abstractions for stream processing, 4 a limited number of approaches 13,16,43,53,54,68,74,90,95 mentioned abstractions as relevant for using/implementing self-adaptation. Considering that it tends to be very complex and time-consuming for application programmers to achieve self-adaptation in their domain-specific applications, we believe that the tools/frameworks should come with ready-to-use abstractions.…”

Section: Self-adaptive Parallelism In Stream Processingmentioning

confidence: 99%

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Self‐adaptation on parallel stream processing: A systematic review

Vogel

Griebler

Danelutto

et al. 2021

Concurrency and Computation

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A recurrent challenge in real-world applications is autonomous management of the executions at run-time. In this vein, stream processing is a class of applications that compute data flowing in the form of streams (e.g., video feeds, images, and data analytics), where parallel computing can help accelerate the executions. On the one hand, stream processing applications are becoming more complex, dynamic, and long-running. On the other hand, it is unfeasible for humans to monitor and manually change the executions continuously. Hence, self-adaptation can reduce costs and human efforts by providing a higher-level abstraction with an autonomic/seamless management of executions. In this work, we aim at providing a literature review regarding self-adaptation applied to the parallel stream processing domain. We present a comprehensive revision using a systematic literature review method. Moreover, we propose a taxonomy to categorize and classify the existing self-adaptive approaches.Finally, applying the taxonomy made it possible to characterize the state-of-the-art, identify trends, and discuss open research challenges and future opportunities.

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Latency‐aware adaptive micro‐batching techniques for streamed data compression on graphics processing units

Stein¹,

Rockenbach

Griebler

et al. 2020

Concurrency and Computation

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Summary Stream processing is a parallel paradigm used in many application domains. With the advance of graphics processing units (GPUs), their usage in stream processing applications has increased as well. The efficient utilization of GPU accelerators in streaming scenarios requires to batch input elements in microbatches, whose computation is offloaded on the GPU leveraging data parallelism within the same batch of data. Since data elements are continuously received based on the input speed, the bigger the microbatch size the higher the latency to completely buffer it and to start the processing on the device. Unfortunately, stream processing applications often have strict latency requirements that need to find the best size of the microbatches and to adapt it dynamically based on the workload conditions as well as according to the characteristics of the underlying device and network. In this work, we aim at implementing latency‐aware adaptive microbatching techniques and algorithms for streaming compression applications targeting GPUs. The evaluation is conducted using the Lempel‐Ziv‐Storer‐Szymanski compression application considering different input workloads. As a general result of our work, we noticed that algorithms with elastic adaptation factors respond better for stable workloads, while algorithms with narrower targets respond better for highly unbalanced workloads.

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Autonomic and Latency-Aware Degree of Parallelism Management in SPar

Cited by 8 publications

References 13 publications

Providing high‐level self‐adaptive abstractions for stream parallelism on multicores

Providing high‐level self‐adaptive abstractions for stream parallelism on multicores

Self‐adaptation on parallel stream processing: A systematic review

Latency‐aware adaptive micro‐batching techniques for streamed data compression on graphics processing units

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