In the case of building facades, introducing weighting factor in SVF calculation leads to outputs close to those obtained by PVSyst. Such good validation results make the proposed model a reliable tool to: (i) automatically process solar cadaster on building rooftops and facades at large urban scales and (ii) support solar energy planning and energy transition policies.
Cities play an increasingly important role with regards to energy transition. Main goal is to reach international and national (Swiss) targets related to energy efficiency and CO2 emission reduction. As a contribution to these global challenges, during the last 6 years the State of Geneva has been producing a detailed solar cadaster. In order to facilitate periodical updates of this solar cadaster, the iCeBOUND project was launched. Around 10 public and private stakeholders, all linked within the Geneva Territorial Information System (SITG), collaborated on the project. Its aim was to design and develop a cloud-based Decision Support System (DSS) that leverages 3D digital urban data with high computing performance, hence facilitating environmental analyses in large built areas, like solar energy potential assessment. As result of the project, an official geoportal and a newfangled public web interface were made widely available early 2017, so as to strengthen decision making with regards to solar installation investment
Abstract-Parallel applications are highly irregular and high performance computing (HPC) infrastructures are very complex. The HPC applications of interest herein are timestepping scientific applications (TSSA). Often, TSSA involve the repeated execution of multiple parallel loops with thousands of iterations and irregular behavior. Dynamic loop scheduling (DLS) techniques were developed over time and have proven to be effective in scheduling parallel loops for achieving load balancing of TSSA. Using a single particular DLS technique throughout the entire execution of a time-step, or even over the entire application, does not guarantee optimal performance due to the unpredictable variations in problem and algorithmic characteristics as well as those of the infrastructure capabilities. For that reason, an autonomic selection of DLS techniques as function of the parallel loop execution time has shown to improve application performance. Recently, a robustness metric of DLS techniques, named "flexibility", has been proposed to estimate the capability of a DLS technique to resist to variations in the loop iterations execution time. To improve the performance of TSSA, we propose in this work an approach that involves the autonomic selection of DLS techniques as function of the flexibility of DLS techniques. The first major novelty of our approach lies in the use of state-of-the-art reinforcement learning (RL) algorithms as smart agents. The second novelty lies in the design of a modified flexibility metric. The third major novelty resides in using the new modified flexibility metric as a reward for the smart agents. The fourth novelty is the evaluation of the proposed approach within a simulated environment, in particular using the SimGrid-SMPI interface to execute DLS algorithms. We discuss the advantages and the limitations of the new proposed flexibility metric as a reward.
In parallel iterative applications, computational efficiency is essential for addressing large problems. Load imbalance is one of the major performance degradation factors of parallel applications. Therefore, distributing, cleverly, and as evenly as possible, the workload among processing elements (PE) maximizes application performance. So far, the standard load balancing method consists in distributing the workload evenly between PEs and, when load imbalance appears, redistributing the extra load from overloaded PEs to underloaded PEs. However, this does not anticipate the load imbalance growth that may continue during the next iterations. In this paper, we present a first step toward a novel philosophy of load balancing that unloads the PEs that will be overloaded in the near future to let the application rebalance itself via its own dynamics. Herein, we present a formal definition of our new approach using a simple mathematical model and discuss its advantages compared to the standard load balancing method. In addition to the theoretical study, we apply our method to an application that reproduces the computation of a fluid model with non-uniform erosion. The performance validates the benefit of anticipating load imbalance. We observed up to 16% performance improvement compared to the standard load balancing method.
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