Kaiyang Liu;Jingrong Wang;Zhiming Huang;Jianping Pan
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Sampling-Based Multi-Job Placement for Heterogeneous Deep Learning Clusters
Heterogeneous deep learning clusters commonly host a variety of distributed learning jobs. In such scenarios, the training efficiency of learning models is negatively affected by the slowest worker. To accelerate the training process, multiple learning jobs may compete for limited computational resources, posing significant challenges to multi-job placement among heterogeneous workers. This article presents a heterogeneity-aware scheduler to solve the multi-job placement problem while taking into account job sizing and load balancing, minimizing the average Job Completion Time (JCT) of deep learning jobs. A novel scheme based on proportional training workload assignment, feasible solution categorization, and matching markets is proposed with theoretical guarantees. To further reduce the computational complexity for low latency decision-making and improve scheduling fairness, we propose to construct the sparsification of feasible solution categories through sampling, which has negligible performance loss in JCT. We evaluate the performance of our design with real-world deep neural network benchmarks on heterogeneous computing clusters. Experimental results show that, compared to existing solutions, the proposed sampling-based scheme can achieve 1) results within 2.04% of the optimal JCT with orders-of-magnitude improvements in algorithm running time, and 2) high scheduling fairness among learning jobs.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.