Gacer: Granularity-Aware Concurrency Regulation for Multi-Tenant Deep Learning

As deep learning continues to advance and is applied to increasingly complex scenarios, the demand for concurrent deployment of multiple neural network models has arisen. This demand, commonly referred to as multi-tenant computing, is becoming more and more important. However, even the most mature G...

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Veröffentlicht in:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design S. 01 - 09
Hauptverfasser: Yu, Yongbo, Yu, Fuxun, Tian, Zhi, Chen, Xiang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 27.10.2024
Schlagworte:
Computational modeling
Deep learning
Low latency communication
Optimization
Regulation
Resource management
Runtime
Solids
Throughput
Training
ISSN:1558-2434
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Zusammenfassung:As deep learning continues to advance and is applied to increasingly complex scenarios, the demand for concurrent deployment of multiple neural network models has arisen. This demand, commonly referred to as multi-tenant computing, is becoming more and more important. However, even the most mature GPU-based computing systems struggle to adequately address the significant heterogeneity and complexity among concurrent models in terms of resource allocation and runtime scheduling. And this usually results in considerable resource utilization and throughput issues. To tackle these issues, this work proposes a set of optimization techniques that advance the granularity of computing management from both the spatial and temporal perspectives, specifically tailored to heterogeneous model compositions for deep learning inference and training. These techniques are further integrated as GACER - an automated optimization framework that provides high-utilization, high-throughput, and low-latency multi-tenant computing support. And our experiments demonstrate that GACER significantly improves the overall resource utilization and consistently achieves outstanding speedups compared to native GPU computing frameworks and existing state-of-the-art optimization works.
ISSN:1558-2434
DOI:10.1145/3676536.3676718