Model and simulation of power consumption and power saving potential of energy efficient cluster hardware

In the last years the power consumption of high performance computing clusters has become a growing problem because number and size of cluster installations raised and still is raising. The high power consumption of the clusters results from the main goal of these clusters: High performance. With a low utilization the cluster hardware consumes nearly as much energy as when it is fully utilized. In these low utilization phases the cluster hardware can theoretically turned off or switched to an lower power consuming mode. In this thesis a model is designed to estimate the power consumption of the hardware with and without energy saving mechanism. With the resulting software it is possible to estimate the cluster power consumption for different configurations of a parallel program. Further energy aware hardware can be simulated to determine an upper bound for energy savings without performance leakage. The results show that is a great energy saving potential for energy aware hardware even in high performance computing. This potential should motivate research in mechanism to control the energy aware hardware in high performance clusters

Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics

Neural networks are emerging as a tool for scalable data-driven simulation of high-dimensional dynamical systems, especially in settings where numerical methods are infeasible or computationally expensive. Notably, it has been shown that incorporating domain symmetries in deterministic neural simulators can substantially improve their accuracy, sample efficiency, and parameter efficiency. However, to incorporate symmetries in probabilistic neural simulators that can simulate stochastic phenomena, we need a model that produces equivariant distributions over trajectories, rather than equivariant function approximations. In this paper, we propose Equivariant Probabilistic Neural Simulation (EPNS), a framework for autoregressive probabilistic modeling of equivariant distributions over system evolutions. We use EPNS to design models for a stochastic n-body system and stochastic cellular dynamics. Our results show that EPNS considerably outperforms existing neural network-based methods for probabilistic simulation. More specifically, we demonstrate that incorporating equivariance in EPNS improves simulation quality, data efficiency, rollout stability, and uncertainty quantification. We conclude that EPNS is a promising method for efficient and effective data-driven probabilistic simulation in a diverse range of domains.Comment: Accepted to NeurIPS 202

Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates

Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications or exhaustive parameter scans only simple approximations are currently fast enough. We address this lack of fast simulators using neural PDE surrogates, data-driven neural network-based surrogate models trained using solutions generated with a classical numerical method. The surrogate approximates a time-stepping operator that evolves the full spatial solution of a reference physics-based model over time. We use DIV1D, a 1D dynamic model of the divertor plasma, as reference model to generate data. DIV1D's domain covers a 1D heat flux tube from the X-point (upstream) to the target. We simulate a realistic TCV divertor plasma with dynamics induced by upstream density ramps and provide an exploratory outlook towards fast transients. State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data. We evaluate (1) the speed-accuracy trade-off; (2) recreating non-linear behavior; (3) data efficiency; and (4) parameter inter- and extrapolation. Once trained, neural PDE surrogates can faithfully approximate DIV1D's divertor plasma dynamics at sub real-time computation speeds: In the proposed configuration, 2ms of plasma dynamics can be computed in $\approx$0.63ms of wall-clock time, several orders of magnitude faster than DIV1D.Comment: Published in Nuclear Fusio

Asomuar

Émile Zola'nın Tanin'de yayımlanan Asomuar adlı romanının ilanıRoman forma şeklinde gazeteyle beraber verilmiştir. Taranan arşivlerde formalara rastlanmamıştır