[hpc-announce] two research grants available within the RIPARTI regional projects

[Massimo Cafaro]

The first research grant shall be developed in close collaboration with 
Planetek Italia (12 months out of 18 in total), and is related to the 
following topic:

Machine Learning for Space Weather

The proposed research project is concerned with the study of "Space 
Weather Phenomena" and the development of knowledge about the mechanisms 
and effects of solar-derived perturbative phenomena developing in 
circumterrestrial space and impacting the ionized atmosphere 
(ionosphere). In the project emphasis is given to the study and modeling 
of the dynamics of the ionospheric plasma and the electron density 
irregularities in it on a global scale, in order to improve the 
capability of long-term (24-48 hours in advance) nowcasting and 
forecasting of the ionospheric response to Space Weather events over the 
Mediterranean area. The modeling approach is developed through 
innovative "machine learning" techniques, recently introduced (Cesaroni 
et al 2020), the results of which point to this as a strategy to extend 
the time horizon of ionospheric forecasting, a fundamental requirement 
for increasing knowledge of Space Weather phenomena in near-Earth space. 
In addition, the growing demand for semi-empirical approaches for 
real-time mitigation of errors introduced by the ionosphere on 
positioning and navigation systems makes the proposed topic a 
significant contribution in the area of "services and research for 
society" in relation to the strategic objective "Development of a 
National Space Weather Service" in the context of developing 
countermeasures to contain the negative effect that the irregular and 
disturbed ionosphere can have on technological systems in use in modern 
society such as, for example, navigation and positioning satellite 
systems (GNSS, GLobal Navigation Satellite Systems), trans-horizon HF 
radio communications, and L-band satellite communication systems. Such 
systems are of interest to a variety of end users who can be identified 
as users of the service in which the developed products may be embedded. 
Examples of users may include: precision agriculture operators, 
operators in the field of mapping, aviation, and radio communications 
operators for emergency management in civil defense.

Cesaroni, C., Spogli, L., Aragon-Angel, A., Fiocca, M., Dear, V., De 
Franceschi, G., & Romano, V. (2020). Neural network based model for 
global Total Electron Content forecasting. Journal of Space Weather and 
Space Climate, 10, 11.

The second research grant shall be developed in close collaboration with 
GE Avio (12 months out of 18 in total), and is related too the following 
topic:

Operative Framework For HPC (Off-HPC)

High-performance computing (HPC) clouds are becoming a complement or, in 
some cases, an alternative to on-premise clusters for running 
scientific-technical, engineering, and business analytics service 
applications. Most research efforts in the area of cloud HPC aim to 
analyze and understand the cost-benefit of migrating computationally 
intensive applications from on-premise environments to public cloud 
platforms. Industry trends show that on-premise/cloud hybrid 
environments are the natural path to get the best out of on-premise and 
cloud resources. Workloads that are stable from the point of view of 
required computing resources and sensitive from the point of view of the 
need to protect processed information can be performed on on-premise 
resources, while peak computational loads can take advantage of remote 
computing resources available in the cloud typically under a 
"pay-as-you-go" consumption mode. The main difficulties in using cloud 
solutions to run HPC applications stem from their characteristics and 
properties compared to traditional cloud services to handle, for 
example, standard enterprise applications, Web applications, data 
storage or backup, or business intelligence. HPC applications tend to 
require more computing power than application services typically 
delivered in cloud environments. These processing requirements arise not 
only from the characteristics of the CPUs (Central Processing Units), 
but also from the amount of memory and network speed to support their 
proper execution. In addition, such applications may have a particular 
and different execution mechanism than dedicated cloud application 
services that instead run 24/7. HPC applications tend to run in batch 
mode. Users execute a series of computational jobs, consisting of 
instances of the application with different inputs, and wait until 
results are generated to decide whether new computational tasks need to 
be submitted and executed. Therefore, moving HPC applications to cloud 
platforms requires not only a focus on resource allocation in the 
infrastructure in use and its optimization, but also on how users 
interact with this new environment. Research in the area of cloud HPC 
can be classified into three broad categories: (i) feasibility studies 
on adopting the cloud to replace or complement on-premise computing 
clusters to run HPC applications; (ii) performance optimization of cloud 
resources for running HPC applications; and (iii) services to simplify 
the use of cloud HPC, particularly for users who are not specialized in 
data and information processing and processing technologies. This 
research project intends to focus on study activities within the first 
category, in which, more specifically, there are four main aspects that 
should be considered: (i) metrics used to assess how feasible the use of 
HPC cloud is; (ii) resources used in computational experiments; (iii) 
computational infrastructure; and (iv) software, which includes both 
well-known HPC benchmarks and computational tools, algorithms, or 
methodologies related to specific business application cases. Currently, 
the company uses HPC applications running mostly on on-premise systems 
but faces issues related to the need for greater computational resources 
that can be met through flexible and scalable architectures provided by 
cloud technologies. The need is to build clear technology and governance 
references for cloud or hybrid infrastructures. The research project 
will therefore aim to carefully analyze the state of the art of hybrid 
HPC solutions, define criteria for benchmarking different solutions, 
develop an operational framework that includes the operational and 
economic management aspects of a hybrid HPC solution, and finally 
implement one or more industrial pilots.



DEADLINE: June 24, 2022
ALL INCLUSIVE GROSS AMOUNT (for 18 months): 29050,50 euro (i.e., 19367 
euro annual gross amount)

NOTE: Foreign candidates are strongly encouraged to contact me by email 
if they need help/support in order to prepare their application: I will 
be glad to assist.

Here you can download an unofficial English translation of the call: 
RIPARTI-call








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Prof.  Massimo Cafaro, Ph.D.
Associate Professor of Parallel Algorithms and Data Mining/Machine Learning
Head of HPC Lab https://hpc-lab.unisalento.it
Director of Master in Applied Data Science

  Department of Engineering for Innovation
  University of Salento, Lecce, Italy
  Via per Monteroni
  73100 Lecce, Italy

  Voice/Fax  +39 0832 297371

  Web   http://sara.unisalento.it/~cafaro
  Web   https://www.unisalento.it/people/massimo.cafaro

  E-mail massimo.cafaro at unisalento.it
  E-mail cafaro at ieee.org
  E-mail cafaro at acm.org

INGV
National Institute of Geophysics and Volcanology
Via di Vigna Murata 605
Roma

  CMCC Foundation
  Euro-Mediterranean Center on Climate Change
  Via Augusto Imperatore, 16 - 73100 Lecce
  massimo.cafaro at cmcc.it

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