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The performance of large-scale distributed compute systems is adversely impacted by stragglers when the execution time of a job is uncertain. To manage stragglers, we consider a multi-fork approach for job scheduling, where additional parallel servers are added at forking instants. In terms of the forking instants and the number of additional servers, we compute the job completion time and the cost of server utilization when the task processing times are assumed to have a shifted exponential distribution. We use this study to provide insights into the scheduling design of the forking instants and the associated number of additional servers to be started. Numerical results demonstrate orders of magnitude improvement in cost in the regime of low completion times as compared to the prior works.
In distributed synchronous gradient descent (GD) the main performance bottleneck for the per-iteration completion time is the slowest textit{straggling} workers. To speed up GD iterations in the presence of stragglers, coded distributed computation t
Distributed implementations of gradient-based methods, wherein a server distributes gradient computations across worker machines, need to overcome two limitations: delays caused by slow running machines called stragglers, and communication overheads.
Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing. The time required for computation at the agents is affected by the availability of local resources giving rise to
The growing size of data center and HPC networks pose unprecedented requirements on the scalability of simulation infrastructure. The ability to simulate such large-scale interconnects on a simple PC would facilitate research efforts. Unfortunately,
Master-worker distributed computing systems use task replication in order to mitigate the effect of slow workers, known as stragglers. Tasks are grouped into batches and assigned to one or more workers for execution. We first consider the case when t