Journal of Information Processing
Online ISSN : 1882-6652
Scalable Work Stealing of Native Threads on an x86-64 Infiniband Cluster
Shigeki AkiyamaKenjiro Taura
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2016 Volume 24 Issue 3 Pages 583-596


Task parallelism on large-scale distributed memory environments is still a challenging problem. The focuses of our work are flexibility of task model and scalability of inter-node load balancing. General task models provide functionalities for suspending and resuming tasks at any program point, and such a model enables us flexible task scheduling to achieve higher processor utilization, locality-aware task placement, etc. To realize such a task model, we have to employ a thread—an execution context containing register values and stack frames—as a representation of a task, and implement thread migration for inter-node load balancing. However, an existing thread migration scheme, iso-address, has a scalability limitation: it requires virtual memory proportional to the number of processors in each node. In large-scale distributed memory environments, this results in a huge virtual memory usage beyond the virtual address space limit of current 64bit CPUs. Furthermore, this huge virtual memory consumption makes it impossible to implement one-sided work stealing with Remote Direct Memory Access (RDMA) operations. One-sided work stealing is a popular approach to achieving high efficiency of load balancing; therefore this also limits scalability of distributed memory task parallelism. In prior work, we propose uni-address, a new thread migration scheme which significantly reduces virtual memory usage for thread stacks and enables RDMA-based work stealing, and implements a lightweight multithread library supporting RDMA-based work stealing on top of Fujitsu FX10 system. In this paper, we port the library to an x86-64 Infiniband cluster with GASNet communication library. We develop one-sided and non one-sided implementations of inter-node work stealing, and evaluate the performance and efficiency of the work stealing implementations.

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© 2016 by the Information Processing Society of Japan
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