Reflex Barrier: A Scalable Network-Based Synchronization Barrier

被引:0
|
作者
Anbar, Ahmad [1 ]
Serres, Olivier [1 ]
El-Ghazawi, Tarek [1 ]
机构
[1] George Washington Univ, NSF Ctr High Performance Reconfigurable Comp CHRE, Dept Elect & Comp Engn, Washington, DC 20052 USA
关键词
Reflex barrier; Synchronization barrier; Many-cores; Distributed memory barrier; Many-core clusters; ALGORITHMS;
D O I
10.1109/ICPADS.2011.106
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
High-performance computing is witnessing the proliferation of multi-core processors in parallel architectures, and the trend is expected to increase further with the emerging many-core technology, leading to hundreds of processing cores within each compute node in the near future. Along side with this trend, it is also clear that total number of cores within the whole system is increasing. To be able to harvest the fruits of this massive parallelism, inter-process synchronization and communication should be as lightweight as they can be, and should be relying on as limited involvement as possible of the participating processors/cores. The synchronization algorithms that target shared memory processors are expected not to be able to scale on many-cores as they rely on atomics, locks, and/or cache coherence protocols, which all should be very costly operations on many-cores. In the same time, some many-core architectures provide user space networks on chip (NoCs) that operate similar to regular networks. In this paper, we are introducing the Reflex barrier, a new synchronization barrier algorithm that relies on fundamental networking concepts. As the barrier relies on the characteristics of the network, it requires very little intervention from the participating processors/cores. The algorithm can also be implemented as split phase, which furnish an opportunity to reduce the synchronization cost. We implemented the algorithm using Unified Parallel C (UPC), MPI and pThreads. We tested our implementation on TILE64, a 64-core processor. The performance of the Reflex barrier is also analyzed and compared to other algorithms using performance models.
引用
收藏
页码:204 / 211
页数:8
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