Kinetic magnetization by fast electrons in laser-produced plasmas at sub-relativistic intensities

The problem of spontaneous magnetic field generation with nanosecond laser pulses raises a series of fundamental questions, including the intrinsic magnetization mechanisms in laser-driven plasmas and the understanding of charge-discharge processes in the irradiated target. These two issues are tightly bound as the charge-discharge processes are defined by the currents, which have in turn a feedback by magnetic fields in the plasma. Using direct polaro-interferometric measurements and theoretical analysis, we show that at parameters related to the PALS laser system (1.315 μ1.315 μm, 350 350 ps, and 1016 1016 W/cm2), fast electrons play a decisive role in the generation of magnetic fields in the laser-driven plasma. Spatial distributions of electric currents were calculated from the measured magnetic field and plasma density distributions. The obtained results revealed the characteristics of strong currents observed in capacitor-coil magnetic generation schemes and open a new approach to fundamental studies related to magnetized plasmas.<br/

Chapter 4 Data System and Data Management in a Federation of HPC/ Cloud Centers

Artificial Intelligence, Deep Learning, Machine Learning, Supercomputin

LIGATE - LIgand Generator and portable drug discovery platform AT Exascale

The COVID-19 pandemic demonstrates that a top priority for society, now and in the future, is to be able to respond quickly to diseases with effective treatments. Among the new tools that pharmaceutical industries and researchers have in their hands nowadays, there are the extensive computer simulations capable of evaluating in-silico the interaction between possible drugs and the target proteins. The central goal of the LIGATE project is to create and validate a leading application solution for drug discovery in High-Performance Computing (HPC) systems up to the exascale level. The overall project purpose is the automation of the drug design process, which is currently performed with substantial human effort throughout the different phases of the process: preparation of input parameters, management of data sets with billions of molecules, interaction with HPC queue management systems to handle jobs, and optimization of scoring function parameters and thresholds

Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces, compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system

Expressing and Applying C plus plus Code Transformations for the HDF5 API Through a DSL

Hierarchical Data Format (HDF5) is a popular binary storage solution in high performance computing (HPC) and other scientific fields. It has bindings for many popular programming languages, including C++, which is widely used in the HPC field. Its C++ API requires mapping of the native C++ data types to types native to the HDF5 API. This task can be error prone, especially when working with complex data structures, which are usually stored using HDF5 compound data types. Due to the lack of a comprehensive reflection mechanism in C++, the mapping code for data manipulation has to be hand-written for each compound type separately. This approach is vulnerable to bugs and mistakes, which can be eliminated by using an automated code generation phase. In this paper we present an approach implemented in the LARA language and supported by the tool Clava, which allows us to automate the generation of the HDF5 data access code for complex data structures in C++

IO - Software for Exascale Architectures -- Final report on applications experience

In the last year of the IO-SEA project, the IO-SEA Workflow Manager has been installed on the DEEP System at Jülich Supercomputer Centre, giving use case applications access to three IO-SEA ephemeral storage services hosted on the IO-SEA Prototype system. In this environment, the IO-SEA scientific use cases have tested their workflows to understand the advantages and challenges of running with the SBB, NFS and DASI ephemeral services. This report details the results of these tests and the experiences of each use case. We conclude with a report of our recommendations and lessons learned in the process

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. The corresponding speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Using an adaptive and time predictable runtime system for power-aware HPC-oriented applications

reserved9siAn increasing number of High-Performance Applications demand some form of time predictability, in particular in scenarios where correctness depends on both performance and timing requirements, and the failure to meet either of them is critical. Consequently, a more predictable HPC system is required, particularly for an emerging class of adaptive real-time HPC applications. Here we present our runtime approach which produces the results in the predictable time with the minimized allocation of hardware resources. The paper describes the advantages in terms of execution time reliability and the trade-offs regarding power/energy consumption and temperature of the system compared with the current GNU/Linux governors.Portero, A.; Sevcik, J.; Golasowski, M.; Vavrik, R.; Libutti, S.; Massari, G.; Catthoor, F.; Fornaciari, W.; Vondrak, V.Portero, A.; Sevcik, J.; Golasowski, M.; Vavrik, R.; Libutti, Simone; Massari, Giuseppe; Catthoor, F.; Fornaciari, William; Vondrak, V