Analyzing time-dimension communication characterizations for representative scientific applications on supercomputer systems

Chen, Juan; Zhou, Wen‐Hao; Dong, Yong; Wang, Zhiyuan; Wu, Feihao; Zhou, Enqiang; Tang, Yuhua

doi:10.1007/s11704-018-7239-1

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…It inspires us to work on what we're going to do next: How to reduce resource contention between job processes. How job communication characterizations [15] affect communication latency.…”

Section: Discussionmentioning

confidence: 99%

Network-on-Chip Aware Task Mappings

Sun

Dong

Chen

et al. 2020

Communications in Computer and Information Science

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Energy and power density have forced the industry to introduce many-cores where a large number of processor cores are integrated into a single chip. In such settings, the communication latency of the network on chip (NoC) could be performance bottleneck of a multi-core and many-core processor. Unfortunately, existing approaches for mapping the running tasks to the underlying hardware resources often ignore the impact of the NoC, leading to sub-optimal performance and energy efficiency. This paper presents a novel approach to allocating NoC resource among running tasks. Our approach is based on the topology partitioning of the shared routers of the NoC. We evaluate our approach by comparing it against two state-of-the-art methods using simulation. Experimental results show that our approach reduces the NoC communication latency by 5.19% and 2.99%, and the energy consumption by 17.94% and 12.68% over two competitive approaches.

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“…It inspires us to work on what we're going to do next: How to reduce resource contention between job processes. How job communication characterizations [15] affect communication latency.…”

Section: Discussionmentioning

confidence: 99%

Network-on-Chip Aware Task Mappings

Sun

Dong

Chen

et al. 2020

Communications in Computer and Information Science

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“…We note that there is no particular structure; however, the system alternates between periods of low-and high-frequency events: they frequently occur in bursts (cf., Karsai et al, 2018) observed in various communication processes (Karsai et al, 2018) in particular in exchanging e-mails (Barabasi, 2005), phone calls (Wang et al, 2015), conflicts between Wikipedia editors (Yasseri et al, 2012), GitHub users activity (Yan et al, 2017), YouTube and Ding content popularity growth (Szabo & Huberman, 2010), paper updating intervals on arXiv (Jo et al, 2012), paper processing times in academic journals (Hartonen, 2013), to name a few. Bursty processes are not limited only to human behavior; they were observed in brain activity (Thompson et al, 2017) communication events on supercomputer systems (Chen et al, 2019), BGP routing anomalies (Moriano et al, 2021), and in the description of the temporal networks (Cencetti et al, 2021;Zou et al, 2021), especially in the context of epidemic contagions and diffusion of information (Unicomb et al, 2021).…”

Section: Posts)mentioning

confidence: 99%

Time to vote: Temporal clustering of user activity on Stack Overflow

Geras

Siudem

Gągolewski

2022

Asso for Info Science & Tech

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Question‐and‐answer (Q&A) sites improve access to information and ease transfer of knowledge. In recent years, they have grown in popularity and importance, enabling research on behavioral patterns of their users. We study the dynamics related to the casting of 7 M votes across a sample of 700 k posts on Stack Overflow, a large community of professional software developers. We employ log‐Gaussian mixture modeling and Markov chains to formulate a simple yet elegant description of the considered phenomena. We indicate that the interevent times can naturally be clustered into 3 typical time scales: those which occur within hours, weeks, and months and show how the events become rarer and rarer as time passes. It turns out that the posts' popularity in a short period after publication is a weak predictor of its overall success, contrary to what was observed, for example, in case of YouTube clips. Nonetheless, the sleeping beauties sometimes awake and can receive bursts of votes following each other relatively quickly.

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“…Some existing work has studied the parts that performance indicators cannot cover. As for network power consumption, Chen et al [3] focus on the statistical regularity in time-dimension communication characteristics for representative scientific applications on supercomputer systems, and then prove that the distribution of communication-event intervals has a power-law decay.…”

Section: Related Workmentioning

confidence: 99%

Power modeling for Phytium FT-2000+/64 multi-core architecture

Chen

Zhang

et al. 2020

Proceedings of the Workshop on Benchmarking in the Datacenter

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Power and energy consumption is the first-class constraint on high-performance-computing (HPC) systems. Understanding the power consumption on such systems is crucial for software optimization and hardware architecture design. Unfortunately, the subtle interactions between the CPU and the memory subsystem make precise power modeling highly challenging on emerging multi-core architectures. This paper presents the first software-based power model for Phytium FT-2000+/64, an ARM-based HPC multi-core architecture. It shows by carefully choosing and modeling a set of systemwide metrics, one can build an accurate power model for the multi-core CPU and the DRAM memory subsystem, two major energy consumers of an HPC system computing node. We evaluate our approach by applying it to HPCC benchmarks and comparing our results against real power measurement. Experimental results show that our approach is highly accurately in modeling power consumption of FT-2000+/64. The average error rate for CPU power modeling in all the scales (8, 16, 32, 64 processes) is 2%, and the average error rate for memory power modeling is about 7.5%. CCS Concepts. • Hardware → Power estimation and optimization.

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Analyzing time-dimension communication characterizations for representative scientific applications on supercomputer systems

Cited by 6 publications

References 40 publications

Network-on-Chip Aware Task Mappings

Network-on-Chip Aware Task Mappings

Time to vote: Temporal clustering of user activity on Stack Overflow

Power modeling for Phytium FT-2000+/64 multi-core architecture

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