Porting and tuning of the Mont-Blanc benchmarks to the multicore ARM 64bit architecture

This project is about porting and tuning the Mont-Blanc benchmarks to the multicore ARM 64 bits architecture. The Mont-Blanc benchmarks are part of the Mont-Blanc European project and they have been developed internally in the BSC (Barcelona Supercomputing Center). The project will explore the possibilities that an ARM architecture can offer running in a HPC (High Performance Computing) setup, this includes to learn how to tune and adapt a parallelized computer program and analyze its execution behavior. As part of the project, we will analyze the performance of each benchmark using instrumentation tools such like Extrae and Paraver. Each benchmark will be adapted, tuned and executed mainly in the three new Mont-Blanc mini-clusters, Thunder (ARMv8 custom), Merlin (ARMv8 custom) and Jetson TX (ARMv8 cortex-a57) using the OmpSs programming model. The evolution of the performance obtained will be shown followed by a brief analysis of the results after each optimization.Aquest projecte es basa en adaptar i afinar els Mont-Blanc benchmarks a l’arquitectura multinucli ARM 64 bits. Els Mont-Blanc benchmarks formen part del projecte Europeu Mont-Blanc i han estat desenvolupats internament en el BSC (Barcelona Supercomputing Center). Aquest projecte explorarà el potencial d’usar l’arquitectura ARM en un entorn HPC (High Performance Computing), això inclou aprendre a adaptar i afinar un programa paral·lel, i analitzar el seu comportament durant l’execució. Com a part del projecte, s’analitzarà el rendiment de cada benchmark usant eines d’instrumentació com Extrae o Paraver. Cada benchmark serà adaptat, afinat i executat en els tres nous miniclústers de Mont-Blanc, Thunder (ARMv8 personalitzat), Merlin (ARMv8 personalitzat) i Jetson TX (ARMv8 cortex-a57) usant el model de programació OmpSs. Es mostrarà l’evolució del rendiment, seguit d’una breu explicació dels resultats després de cada optimització.Este proyecto se basa en adaptar y afinar los Mont-blanc benchmarks a la arquitectura multi-núcleo ARM 64 bits. Los Mont-Blanc benchmarks forman parte del proyecto Europeo Mont-Blanc y han sido desarrollados internamente en el BSC (Barcelona Supercomputing Center). Este proyecto explorará el potencial de usar la arquitectura ARM en un entorno HPC (High Performance Computing), esto incluye aprender a adaptar y afinar un programa paralelo, y analizar su comportamiento durante la ejecución. Como parte del proyecto, se analizará el rendimiento de cada benchmark usando herramientas de instrumentación como Extrae o Paraver. Cada benchmark será adaptado, afinado y ejecutado en los tres nuevos mini-clústeres de Mont-Blanc, Thunder (ARMv8 personalizado), Merlin (ARMv8 personalizado) y Jetson TX (ARMv8 cortex-a57) usando el modelo de programación OmpSs. Se mostrará la evolución del rendimiento obtenido, y una breve explicación de los resultados después de cada optimización

Small polaron with generic open boundary conditions revisit: exact solution via the off-diagonal Bethe ansatz

The small polaron, an one-dimensional lattice model of interacting spinless fermions, with generic non-diagonal boundary terms is studied by the off-diagonal Bethe ansatz method. The presence of the Grassmann valued non-diagonal boundary fields gives rise to a typical $U(1)$-symmetry-broken fermionic model. The exact spectra of the Hamiltonian and the associated Bethe ansatz equations are derived by constructing an inhomogeneous $T-Q$ relation.Comment: 12 pages, no figure, published versio

Characteristics of optical multi-peak solitons induced by higher-order effects in an erbium-doped fiber system

We study multi-peak solitons \textit{on a plane-wave background} in an erbium-doped fiber system with some higher-order effects, which is governed by a coupled Hirota and Maxwel-Bloch (H-MB) model. The important characteristics of multi-peak solitons induced by the higher-order effects, such as the velocity changes, localization or periodicity attenuation, and state transitions, are revealed in detail. In particular, our results demonstrate explicitly that a multi-peak soliton can be converted to an anti-dark soliton when the periodicity vanishes; on the other hand, a multi-peak soliton is transformed to a periodic wave when the localization vanishes. Numerical simulations are performed to confirm the propagation stability of multi-peak solitons riding on a plane-wave background. Finally, we compare and discuss the similarity and difference of multi-peak solitons in special degenerate cases of the H-MB system with general existence conditions.Comment: 7 pages, 4 figure

An efficient method to derive statistical mechanical properties of concrete reinforced with spiral-shaped steel fibres in dynamic tension

Steel-fibre-reinforced concrete (SFRC) has been recognised as an effective solution to resist impact loading on structures. The reliable application and efficient design of SFRC structures depends on the knowledge of its mechanical properties. Since many important factors, including the locations and orientations of fibres and aggregates in concrete and the material properties of concrete matrix, are intrinsically random, the mechanical properties of SFRC present a high level of randomness. To accurately quantify them, effective statistical techniques are indispensable. Using traditional statistical techniques, a large quantity of data, from either experiments or numerical simulations, are needed to derive the correlation between the mechanical properties and the random factors. However, both ways are time-consuming and costly. Therefore, very little information regarding the statistical mechanical properties of SFRC can be found in the current literature. In this study, a kernel-based nonparametric statistical method is proposed to derive the statistical mechanical properties of SFRC with limited number of data. The behaviours of SFRC with randomly distributed spiral-shaped fibres and aggregates under impact loading are simulated using commercial software LS-DYNA. The simulation accuracy is validated by the experimental results. The influences of various volume fractions of fibres on dynamic increase factor (DIF) of the tensile strength of SFRC specimens under dynamic loadings at different strain rates are quantified through a prediction model obtained from kernel regression. The results demonstrate that the proposed method is able to estimate the DIF value of SFRC based on the tensile strength and strain rate, and to derive the statistical mechanical properties of SFRC

An architectural journey into RISC architectures for HPC workloads

The thesis evaluates the current state-of-the-art of RISC architectures in HPC. Studying the performance, power, and energy to solution in heterogeneous SoCs. For the evaluation 2 arm platforms (CPU+GPU, CPU+FPGA), 1 RISC-V platform and 1 Open Source RISC-V core running in an FPGA have been tested

A graphical simulator for modeling complex crowd behaviors

Abnormal crowd behaviors of varied real-world settings could represent or pose serious threat to public safety. The video data required for relevant analysis are often difficult to acquire due to security, privacy and data protection issues. Without large amounts of realistic crowd data, it is difficult to develop and verify crowd behavioral models, event detection techniques, and corresponding test and evaluations. This paper presented a synthetic method for generating crowd movements and tendency based on existing social and behavioral studies. Graph and tree searching algorithms as well as game engine-enabled techniques have been adopted in the study. The main outcomes of this research include a categorization model for entity-based behaviors following a linear aggregation approach; and the construction of an innovative agent-based pipeline for the synthesis of A-Star path-finding algorithm and an enhanced Social Force Model. A Spatial-Temporal Texture (STT) technique has been adopted for the evaluation of the model's effectiveness. Tests have highlighted the visual similarities between STTs extracted from the simulations and their counterparts - video recordings - from the real-world

Extracting Spatio-temporal Texture Signatures for Crowd Abnormality Detection

In order to achieve automatic prediction and warning of hazardous crowd behaviors, a Spatio-Temporal Volume (STV) analysis method is proposed in this research to detect crowd abnormality recorded in CCTV streams. The method starts from building STV models using video data. STV slices – called Spatio-Temporal Textures (STT) - can then be analyzed to detect crowded regions. After calculating the Gray Level Co-occurrence Matrix (GLCM) among those regions, abnormal crowd behavior can be identified, including panic behaviors and other behavioral patterns. In this research, the proposed STT signatures have been defined and experimented on benchmarking video databases. The proposed algorithm has shown a promising accuracy and efficiency for detecting crowd-based abnormal behaviors. It has been proved that the STT signatures are suitable descriptors for detecting certain crowd events, which provide an encouraging direction for real-time surveillance and video retrieval applications

An Approach to Detect Crowd Panic Behavior using Flow-based Feature

With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brandnew approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed