arXiv subjects

Laércio Lima Pilla

Publications and source records attributed to Laércio Lima Pilla.

3 recordsLinked to original sources

arXiv2026

Energy-Aware Scheduling Strategies for Partially-Replicable Task Chains on Heterogeneous Processors

The arrival of heterogeneous (or hybrid) multicore architectures has brought new performance trade-offs for applications, and efficiency opportunities to systems. They have also increased the challenges related to thread scheduling, as tasks' execution times will vary depending if they are placed on big (performance) cores or little (efficient) ones. In this paper, we focus on the challenges heterogeneous multicore processors bring to partially-replicable task chains, such as the ones that implement digital communication standards in Software-Defined Radio (SDR). Our objective is to maximize the throughput of these task chains while also minimizing their power consumption. We model this problem as a pipelined workflow scheduling problem using pipelined and replicated parallelism on two types of resources whose objectives are to minimize the period and to use as many little cores as necessary. We propose two greedy heuristics (FERTAC and 2CATAC) and one optimal dynamic programming (HeRAD) solution to the problem. We study an open source implementation of the DVB-S2 communication standard based on the StreamPU runtime. Leading processor vendors are covered with ARM, Apple, AMD, and Intel platforms. Both the achieved throughput and the energy consumption are evaluated. Our results demonstrate the benefits and drawbacks of the different proposed solutions. On average, FERTAC and 2CATAC achieve near-optimal solutions, with periods that are less than 10% worse than the optimal (HeRAD). These three scheduling strategies now enable programmers and users of StreamPU to transparently make use of heterogeneous multicore processors and achieve a throughput that differs from its theoretical maximum by less than 6% on average. On the DVB-S2 receiver, it is also shown that the heterogeneous solutions outperform the best homogeneous ones in terms of energy efficiency by 8% on average.

cs.DC↗

arXiv2022

Scheduling Algorithms for Federated Learning with Minimal Energy Consumption

Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private.With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques).Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective.In this paper, we investigate the problem of minimizing the energy consumption of FL training on heterogeneous devices by controlling the workload distribution.We model this as the Minimal Cost FL Schedule problem, a total cost minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with arbitrary cost functions.We propose a pseudo-polynomial optimal solution to the problem based on the previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem.We also provide four algorithms for scenarios where cost functions are monotonically increasing and follow the same behavior.These solutions are likewise applicable on the minimization of other kinds of costs, and in other one-dimensional data partition problems.

cs.LG↗

arXiv2020

Optimal Task Assignment to Heterogeneous Federated Learning Devices

Federated Learning provides new opportunities for training machine learning models while respecting data privacy. This technique is based on heterogeneous devices that work together to iteratively train a model while never sharing their own data. Given the synchronous nature of this training, the performance of Federated Learning systems is dictated by the slowest devices, also known as stragglers. In this paper, we investigate the problem of minimizing the duration of Federated Learning rounds by controlling how much data each device uses for training. We formulate this problem as a makespan minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with non-decreasing cost functions while respecting lower and upper limits of tasks per resource. Based on this formulation, we propose a polynomial-time algorithm named OLAR and prove that it provides optimal schedules. We evaluate OLAR in an extensive experimental evaluation using simulation that includes comparisons to other algorithms from the state of the art and new extensions to them. Our results indicate that OLAR provides optimal solutions with a small execution time. They also show that the presence of lower and upper limits of tasks per resource erase any benefits that suboptimal heuristics could provide in terms of algorithm execution time.

cs.AI↗