arXiv subjects

Marco D'Amico

Publications and source records attributed to Marco D'Amico.

3 recordsLinked to original sources

arXiv2026

Identifying the MPC-Liquidity Gradient in High-Quality Data

We estimate the gradient of the Marginal Propensity to Consume (MPC) with respect to liquidity using a new estimator designed for administrative data with negligible measurement error in income. We derive a state-dependent consumption pass-through equation from the canonical buffer-stock model, and show that the pass-through coefficient of this equation can be used to construct tight bounds on the MPC conditional on the relevant state. We recover latent permanent and transitory income shocks with a Kalman smoother and use them as regressors in an empirical representation of the consumption equation. The Kalman-shock estimator identifies the theoretical pass-through coefficient under the assumption of negligible measurement error in income, and attains the minimum variance within the class of estimators that are linear in household income histories, including the canonical GMM estimator by Blundell et al (2008) and recent refinements thereof. Applying the method to Swedish administrative tax registers, we show that consumption responses to transitory shocks have a sharp negative and convex gradient with respect to cash-on-hand; the associated annual MPC falls from 0.7 in the lowest cash-on-hand decile to 0.3 in the top decile. The permanent-shock pass-through is close to one across the cash-on-hand distribution. These patterns are not visible when using traditional, less efficient, estimators.

econ.GN↗

arXiv2021

Energy hardware and workload aware job scheduling towards interconnected HPC environments

New HPC machines are getting close to the exascale. Power consumption for those machines has been increasing, and researchers are studying ways to reduce it. A second trend is HPC machines' growing complexity, with increasing heterogeneous hardware components and different clusters architectures cooperating in the same machine. We refer to these environments with the term heterogeneous multi-cluster environments. With the aim of optimizing performance and energy consumption in these environments, this paper proposes an Energy-Aware-Multi-Cluster (EAMC) job scheduling policy. EAMC-policy is able to optimize the scheduling and placement of jobs by predicting performance and energy consumption of arriving jobs for different hardware architectures and processor frequencies, reducing workload's energy consumption, makespan, and response time. The policy assigns a different priority to each job-resource combination so that the most efficient ones are favored, while less efficient ones are still considered on a variable degree, reducing response time and increasing cluster utilization. We implemented EAMC-policy in Slurm, and we evaluated a scenario in which two CPU clusters collaborate in the same machine. Simulations of workloads running applications modeled from real-world show a reduction of response time and makespan by up to 25% and 6% while saving up to 20% of total energy consumed when compared to policies minimizing runtime, and by 49%, 26%, and 6% compared to policies minimizing energy.

cs.DC↗

arXiv2020

Holistic Slowdown Driven Scheduling and Resource Management for Malleable Jobs

In job scheduling, the concept of malleability has been explored since many years ago. Research shows that malleability improves system performance, but its utilization in HPC never became widespread. The causes are the difficulty in developing malleable applications, and the lack of support and integration of the different layers of the HPC software stack. However, in the last years, malleability in job scheduling is becoming more critical because of the increasing complexity of hardware and workloads. In this context, using nodes in an exclusive mode is not always the most efficient solution as in traditional HPC jobs, where applications were highly tuned for static allocations, but offering zero flexibility to dynamic executions. This paper proposes a new holistic, dynamic job scheduling policy, Slowdown Driven (SD-Policy), which exploits the malleability of applications as the key technology to reduce the average slowdown and response time of jobs. SD-Policy is based on backfill and node sharing. It applies malleability to running jobs to make room for jobs that will run with a reduced set of resources, only when the estimated slowdown improves over the static approach. We implemented SD-Policy in SLURM and evaluated it in a real production environment, and with a simulator using workloads of up to 198K jobs. Results show better resource utilization with the reduction of makespan, response time, slowdown, and energy consumption, up to respectively 7%, 50%, 70%, and 6%, for the evaluated workloads.

cs.DC↗