Networking and communication challenges for post-exascale systems

Dhabaleswar PANDA; Xiao-yi LU; Hari SUBRAMONI

doi:10.1631/FITEE.1800631

Your Location：

Home >

Browse articles >

Networking and communication challenges for post-exascale systems

Special Issue on Post-exascale Supercomputing | Updated：2022-05-19

- Networking and communication challenges for post-exascale systems
- 超百亿亿级系统面临的网络和通信挑战
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 10, Pages: 1230-1235(2018)
- Affiliations：
  
  Department of Computer Science and Engineering, The Ohio State University, Ohio 43210, USA
- Author bio：
  
  Dhabaleswar PANDA, E-mail: panda@cse.ohio-state.edu
  [ "Xiao-yi LU, E-mail: luxi@cse.ohio-state.edu" ]
  [ "Hari SUBRAMONI, E-mail: subramon@cse.ohio-state.edu" ]
- Funds：
- DOI：10.1631/FITEE.1800631
  CLC： TP311
- Received：09 October 2018，
  
  Revised：2018-10-15，
  
  Published：2018-10
- Accepted：
Scan QR Code
Dhabaleswar PANDA, Xiao-yi LU, Hari SUBRAMONI. Networking and communication challenges for post-exascale systems[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(10): 1230-1235.
DOI：

Dhabaleswar PANDA, Xiao-yi LU, Hari SUBRAMONI. Networking and communication challenges for post-exascale systems[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(10): 1230-1235. DOI： 10.1631/FITEE.1800631.

摘要

由于新兴处理器、内存和网络技术的显著进步，百亿亿级系统将在未来几年（2020-2022）推出。随着百亿亿级系统被配置和使用，具有更细粒度、更短时间步长和更大数据量的下一代应用程序将被持续需求。从发展趋势看，2025-2035年间，下一代应用程序将需要超百亿亿级系统。本文关注超百亿亿级系统在网络和通信方面面临的挑战。首先，提出超百亿亿级系统的设想架构。其次，从不同方面阐述面临的挑战，包括多种网络技术、高性能通信和同步协议、加速器和现场可编程门户阵列的集成支持、容错和服务质量支持、能量感知通信方案和协议、软件定义网络以及多种内存和存储器的可扩展通信协议。再次，指出在这些系统上进行支持高性能计算、大数据和深度学习的高效编程模型设计面临的挑战。最后，强调了这些系统的上层共同设计运行时间的关键需求，以实现最优性能和可扩展性。

Abstract

With the significant advancement in emerging processor

memory

and networking technologies

exascale systems will become available in the next few years (2020-2022). As the exascale systems begin to be deployed and used

there will be a continuous demand to run next-generation applications with finer granularity

finer time-steps

and increased data sizes. Based on historical trends

next-generation applications will require post-exascale systems during 2025-2035. In this study

we focus on the networking and communication challenges for post-exascale systems. Firstly

we present an envisioned architecture for post-exascale systems. Secondly

the challenges are summarized from different perspectives: heterogeneous networking technologies

high-performance communication and synchronization protocols

integrated support with accelerators and field-programmable gate arrays

fault-tolerance and quality-of-service support

energy-aware communication schemes and protocols

software-defined networking

and scalable communication protocols with heterogeneous memory and storage. Thirdly

we present the challenges in designing efficient programming model support for high-performance computing

big data

and deep learning on these systems. Finally

we emphasize the critical need for co-designing runtime with upper layers on these systems to achieve the maximum performance and scalability.

关键词

Keywords

references

ASCAC Subcommittee on Exascale Computing . . The Opportunities and Challenges of Exascale Computing . . 2010 . . https://science.energy.gov/media/ascr/ascac/pdf/reports/Exascale_subcommittee_report.pdf https://science.energy.gov/media/ascr/ascac/pdf/reports/Exascale_subcommittee_report.pdf , , . .

R Biswas , , , XY Lu , , , DK Panda . . Accelerating tensorFlow with adaptive RDMA-based gRPC . . 25 th IEEE Int Conf on High Performance Computing, Data, and Analytic , , 2018 . . .

YF Cui , , , R Moore , , , K Olsen , , , 等 . . Enabling verylarge scale earthquake simulations on parallel machines . . In: Shi Y, van Albada GD, Dongarra J, et al. (Eds.), Computational Science. Springer Berlin Heidelberg , , 2007 . . p.46 - - 53 . . . .

Energy Government . . Workshop on Terabits Networks for Extreme Scale Science . . 2011 . . https://science.energy.gov//media/ascr/pdf/programdocuments/docs/Terabit_networks_workshop_report.pdf https://science.energy.gov//media/ascr/pdf/programdocuments/docs/Terabit_networks_workshop_report.pdf , , . .

RL Graham , , , D Bureddy , , , P Lui , , , 等 . . Scalable Hierarchical Aggregation Protocol (SHArP): a hardware architecture for efficient data reduction . . Proc 1 st Workshop on Optimization of Communication in HPC , , 2016 . . p.1 - - 10 . . DOI: 10.1109/COMHPC.2016.006 http://doi.org/10.1109/COMHPC.2016.006 . .

Intel . . Intel Omni-Path Architecture Driving Exascale Computing and HPC . . 2016 . . https://www.intel.com/content/www/us/en/highperformance-computing-fabrics/omni-path-drivingexascale-computing.html https://www.intel.com/content/www/us/en/highperformance-computing-fabrics/omni-path-drivingexascale-computing.html , , . .

RZ Li , , , C DeTar , , , S Gottlieb , , , 等 . . MILC code performance on high end CPU and GPU supercomputer clusters . . 2017 . . http://cn.arxiv.org/abs/1712.00143 http://cn.arxiv.org/abs/1712.00143 , , . .

XY Lu , , , D Shankar , , , S Gugnani , , , 等 . . Highperformance design of Apache Spark with RDMA and its benefits on various workloads . . Proc IEEE Int Conference on Big Data , , 2016 . . p.253 - - 262 . . DOI: 10.1109/BigData.2016.7840611 http://doi.org/10.1109/BigData.2016.7840611 . .

BlueField Mellanox . . Multicore System on Chip . . 2017 . . http://www.mellanox.com/related-docs/npu-multicoreprocessors/PB_Bluefield_SoC.pdf http://www.mellanox.com/related-docs/npu-multicoreprocessors/PB_Bluefield_SoC.pdf , , . .

NVMe Express . . NVMe over Fabrics . . 2016 . . http://www.nvmexpress.org/wp-content/uploads/NVMe_Over_Fabrics.pdf http://www.nvmexpress.org/wp-content/uploads/NVMe_Over_Fabrics.pdf , , . .

ORNL . . Summit: America’s Newest and Smartest Supercomputer . . 2018 . . https://www.olcf.ornl.gov/summit/ https://www.olcf.ornl.gov/summit/ , , . .

MWU Rahman , , , XY Lu , , , NS Islam , , , 等 . . HOMR: a hybrid approach to exploit maximum overlapping in MapReduce over high performance interconnects . . Proc 28 th ACM Int Conf on Supercomputing , , 2014 . . p.33 - - 42 . . DOI: 10.1145/2597652.2597684 http://doi.org/10.1145/2597652.2597684 . .

R Rajachandrasekar , , , J Jaswani , , , H Subramoni , , , 等 . . Minimizing network contention in InfiniBand clusters with a QoS-aware data-staging framework . . IEEE Int Conf on Cluster Computing , , 2012 . . p.329 - - 336 . . DOI: 10.1109/CLUSTER.2012.90 http://doi.org/10.1109/CLUSTER.2012.90 . .

AMK Sarvestani , , , C Bailey , , , J Austin . . Performance analysis of a 3D wireless massively parallel computer . . J Sens Actuat Netw , , 2018 . . 7 ( ( 2 ): ): 18 DOI: 10.3390/jsan7020018 http://doi.org/10.3390/jsan7020018 . .

D Shankar , , , X Lu , , , N Islam , , , 等 . . High-performance hybrid key-value store on modern clusters with RDMA interconnects and SSDs: non-blocking extensions, designs, and benefits . . IEEE Int Parallel and Distributed Processing Symp , , 2016 . . p.393 - - 402 . . DOI: 10.1109/IPDPS.2016.112 http://doi.org/10.1109/IPDPS.2016.112 . .

H Subramoni , , , P Lai , , , S Sur , , , 等 . . Improving application performance and predictability using multiple virtual lanes in modern multi-core InfiniBand clusters . . Int Conf on Parallel Processing , , 2010 . . .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

MltAuxTSPP: a unified benchmark for deep learning-based traffic state prediction with multi-source auxiliary data

Q-space-coordinate-guided neural networks for high-fidelity diffusion tensor estimation from minimal diffusion-weighted images

CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection

Federated model with contrastive learning and adaptive control variates for human activity recognition

A comprehensive survey of physical adversarial vulnerabilities in autonomous driving systems

Related Author

Yusong ZHOU

Xiaoyu JIANG

Shu SUN

Xinmin ZHANG

Yuanqiu MO

Zhihuan SONG

Maokun ZHENG

Zhi LI

Related Institution

School of Mathematics, Southeast University

State Key Laboratory of Industrial Control Technology, Zhejiang University

College of Control Science and Engineering, Zhejiang University

State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰