Desynchronization and Speedup in an Asynchronous Conservative Parallel Update Protocol
A. Kolakowska, M. A. Novotny
Abstract
In a state-update protocol for a system of L asynchronous parallel processes that communicate only with nearest neighbors, global desynchronization in operation times can be deduced from kinetic roughening of the corresponding virtual-time horizon (VTH). The utilization of the parallel processing environment can be deduced by analyzing the microscopic structure of the VTH. We give an overview of how the methods of non-equilibrium surface growth (physics of complex systems) can be applied to uncover some properties of state update algorithms used in distributed parallel discrete-event simulations (PDES). In particular, we focus on the asynchronous conservative PDES algorithm in a ring communication topology. The time evolution of its VTH is simulated numerically as asynchronous cellular automaton whose update rule corresponds to the update rule followed by this algorithm. We give theoretical estimates of the performance as a function of L and the load per processor, i.e., approximate formulas for the mean speedup and for the desynchronization. It is established that, for a given simulation size, there is a theoretical upper bound for the desynchronization and a theoretical non-zero lower bound for the utilization. The new approach to performance studies, outlined in this chapter, is particularly useful in the search for the design of a new-generation of algorithms that would efficiently carry out an autonomous or tunable synchronization.
Create a lesson
Related papers
PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving
Zhexiang Zhang, Minchen Yu, Yifan Sun et al.
A Kubernetes-Native Request Router for Quality-Aware Inference Serving in the Computing Continuum
Ignjat Karanovic, Pantelis A. Frangoudis, Ivan Čilić et al.
Accelerating Sharded Data Parallelism at Scale with Federated Learning
Gianluca Mittone, Marco Aldinucci
Distributed Edge Inference: an Experimental Study on Multiview Detection
Gianluca Mittone, Giulio Malenza, Marco Aldinucci et al.
P-GADMM: Parallel Group-Based ADMM for Asynchronous Optimization in Heterogeneous Edge Networks
Gaiguo Wei, Qingying Zhang, Heqiang Wang et al.
VERA: Reinforcement Learning for Dynamic Memory Scaling of HPC Workloads in Kubernetes
Ade Pramono, Jie Ren, Ivy Peng