IEEE Transactions on Parallel and Distributed Systems

IEEE Transactions on Parallel and Distributed Systems (TPDS) is a scholarly archival journal published monthly. Parallelism and distributed computing are foundational research and technology to rapidly advance computer systems and their applications. Read the full scope of TPDS.

Expand your horizons with Colloquium, a monthly survey of abstracts from all CS transactions! Replaces OnlinePlus in January 2017.

From the March 2019 Issue

Exploiting Hardware Multicast and GPUDirect RDMA for Efficient Broadcast

By Ching-Hsiang Chu, Xiaoyi Lu, Ammar A. Awan, Hari Subramoni, Bracy Elton, and Dhabaleswar K. Panda

Broadcast is a widely used operation in many streaming and deep learning applications to disseminate large amounts of data on emerging heterogeneous High-Performance Computing (HPC) systems. However, traditional broadcast schemes do not fully utilize hardware features for Graphics Processing Unit (GPU)-based applications. In this paper, a model-oriented analysis is presented to identify performance bottlenecks of existing broadcast schemes on GPU clusters. Next, streaming-based broadcast schemes are proposed to exploit InfiniBand hardware multicast (IB-MCAST) and NVIDIA GPUDirect technology for efficient message transmission. The proposed designs are evaluated in the context of using Message Passing Interface (MPI) based benchmarks and applications. The experimental results indicate improved scalability and up to 82 percent reduction of latency compared to the state-of-the-art solutions in

the benchmark-level evaluation. Furthermore, compared to the state-of-the-art, the proposed design yields stable higher throughput for a synthetic streaming workload, and 1.3x faster training time for a deep learning framework.

View the PDF of this article View this issue in the digital library

Editorials and Announcements

Announcements

TPDS is committed to enabling reproducible research so that other researchers can validate published work and reach the same scientific conclusions, potentially using different datasets or methods. To support reproducibility, TPDS has partnered with Code Ocean, a cloud-based computational reproducibility platform, to pilot the post-publication peer review of code associated with articles published in TPDS. Authors who have published in TPDS can make their published article more reproducible and earn a reproducibility badge by submitting their associated code for post-publication peer review. Visit the author instructions for more information.
We are pleased to announce that Manish Parashar, a Distinguished Professor of Computer Science at Rutgers, The State University of New Jersey University, has been selected as the new Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems starting in 2018.
We are pleased to announce that Xian-He Sun, a Distinguished Professor of Computer Science at The Illinois Institute of Technology, has been selected as the new Associate Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems starting in 2018.
According to Clarivate Analytics' 2016 Journal Citation Report, TPDS has an impact factor of 4.181.

Editorials