Data‐driven programming models such as many‐task computing (MTC) have been prevalent for running data‐intensive scientific applications. MTC applies over‐decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a fully distributed task scheduling architecture that employs as many schedulers as the compute nodes to make scheduling decisions. Achieving distributed load balancing and best exploiting data locality are two important goals for the best performance of distributed scheduling of data‐intensive applications. Our previous research proposed a data‐aware work‐stealing technique to optimize both load balancing and data locality by using both dedicated and shared task ready queues in each scheduler. Tasks were organized in queues based on the input data size and location. Distributed key‐value store was applied to manage task metadata. We implemented the technique in MATRIX, a distributed MTC task execution framework. In this work, we devise an analytical suboptimal upper bound of the proposed technique, compare MATRIX with other scheduling systems, and explore the scalability of the technique at extreme scales. Results show that the technique is not only scalable but can achieve performance within 15% of the suboptimal solution. Copyright © 2015 John Wiley & Sons, Ltd.
Optimizing Work Stealing Communication with Structured Atomic Operations
Applications that rely on sparse or irregular data are often challenging to scale on modern distributed-memory systems. As a result, these systems typically require continuous load balancing in order to maintain efficiency. Work stealing is a common technique to remedy imbalance. In this work we present a strategy for work stealing that reduces the amount of communication required for a steal operation by half. We show that in exchange for a small amount of additional complexity to manage the local queue state we can combine both discovering and claiming work into a single step. Conventionally, workstealing uses a two step process of discovering work and then claiming it. Our system, SWS, provides a mechanism where both processes are performed in a singular communication without the need for multiple synchronization messages. This reduction in communication is possible with the novel application of atomic operations that manipulate a compact representation of task queue metadata. We demonstrate the effectiveness of this strategy using known benchmarks for testing dynamic load balancing systems and for performing unbalanced tree searches. Our results show the reduction in communication reduces task acquisition time and steal time, which in turn improves overall performance on sparse computations.
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- Award ID(s):
- 2018758
- NSF-PAR ID:
- 10256915
- Date Published:
- Journal Name:
- 50th International Conference on Parallel Processing (ICPP '21)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3472456.3472522
