Shared Memory Model over a Switchless PCIe NTB Interconnect Network


Seung-Ho Lim, Kwangho Cha, Journal of Information Processing Systems Vol. 18, No. 1, pp. 159-172, Feb. 2022  

10.3745/JIPS.01.0085
Keywords: Interconnect Network, HPC, Multi-Thread, NTB, OpenSHMEM, PCIe
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Abstract

The role of the interconnect network, which connects computing nodes to each other, is important in high- performance computing (HPC) systems. In recent years, the peripheral component interconnect express (PCIe) has become a promising interface as an interconnection network for high-performance and cost-effective HPC systems having the features of non-transparent bridge (NTB) technologies. OpenSHMEM is a programming model for distributed shared memory that supports a partitioned global address space (PGAS). Currently, little work has been done to develop the OpenSHMEM library for PCIe-interconnected HPC systems. This paper introduces a prototype implementation of the OpenSHMEM library through a switchless interconnect network using PCIe NTB to provide a PGAS programming model. In particular, multi-interrupt, multi-thread-based data transfer over the OpenSHMEM shared memory model is applied at the implementation level to reduce the latency and increase the throughput of the switchless ring network system. The implemented OpenSHMEM programming model over the PCIe NTB switchless interconnection network provides a feasible, cost-effective HPC system with a PGAS programming model.


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Cite this article
[APA Style]
Seung-Ho Lim and Kwangho Cha (2022). Shared Memory Model over a Switchless PCIe NTB Interconnect Network. Journal of Information Processing Systems, 18(1), 159-172. DOI: 10.3745/JIPS.01.0085.

[IEEE Style]
S. Lim and K. Cha, "Shared Memory Model over a Switchless PCIe NTB Interconnect Network," Journal of Information Processing Systems, vol. 18, no. 1, pp. 159-172, 2022. DOI: 10.3745/JIPS.01.0085.

[ACM Style]
Seung-Ho Lim and Kwangho Cha. 2022. Shared Memory Model over a Switchless PCIe NTB Interconnect Network. Journal of Information Processing Systems, 18, 1, (2022), 159-172. DOI: 10.3745/JIPS.01.0085.