scholarly journals On the Optimization of Self-Organization and Self-Management Hardware Resource Allocation for Heterogeneous Clouds

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 147
Author(s):  
Konstantinos M. Giannoutakis ◽  
Christos K. Filelis-Papadopoulos ◽  
George A. Gravvanis ◽  
Dimitrios Tzovaras
Keyword(s):  
High Performance ◽  
Evaluation Criteria ◽  
End User ◽  
Suitability Index ◽  
Hardware Resource ◽  
Heterogeneous Clouds ◽  
Specialized Hardware ◽  
Heterogeneous Cloud ◽  
Performance Computing ◽  
Selection Of

There is a tendency, during the last years, to migrate from the traditional homogeneous clouds and centralized provisioning of resources to heterogeneous clouds with specialized hardware governed in a distributed and autonomous manner. The CloudLightning architecture proposed recently introduced a dynamic way to provision heterogeneous cloud resources, by shifting the selection of underlying resources from the end-user to the system in an efficient way. In this work, an optimized Suitability Index and assessment function are proposed, along with their theoretical analysis, for improving the computational efficiency, energy consumption, service delivery and scalability of the distributed orchestration. The effectiveness of the proposed scheme is being evaluated with the use of simulation, by comparing the optimized methods with the original approach and the traditional centralized resource management, on real and synthetic High Performance Computing applications. Finally, numerical results are presented and discussed regarding the improvements over the defined evaluation criteria.

scholarly journals Controllers: An abstraction to ease the use of hardware accelerators

2017 ◽  
Vol 32 (6) ◽  
pp. 838-853 ◽  
Author(s):  
Ana Moreton–Fernandez ◽  
Hector Ortega–Arranz ◽  
Arturo Gonzalez–Escribano
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Abstract Entity ◽  
Hardware Accelerators ◽  
Processing Unit ◽  
Central Processing ◽  
Computing Platforms ◽  
Graphics Processing ◽  
Performance Computing ◽  
Selection Of

Nowadays the use of hardware accelerators, such as the graphics processing units or XeonPhi coprocessors, is key in solving computationally costly problems that require high performance computing. However, programming solutions for an efficient deployment for these kind of devices is a very complex task that relies on the manual management of memory transfers and configuration parameters. The programmer has to carry out a deep study of the particular data that needs to be computed at each moment, across different computing platforms, also considering architectural details. We introduce the controller concept as an abstract entity that allows the programmer to easily manage the communications and kernel launching details on hardware accelerators in a transparent way. This model also provides the possibility of defining and launching central processing unit kernels in multi-core processors with the same abstraction and methodology used for the accelerators. It internally combines different native programming models and technologies to exploit the potential of each kind of device. Additionally, the model also allows the programmer to simplify the proper selection of values for several configuration parameters that can be selected when a kernel is launched. This is done through a qualitative characterization process of the kernel code to be executed. Finally, we present the implementation of the controller model in a prototype library, together with its application in several case studies. Its use has led to reductions in the development and porting costs, with significantly low overheads in the execution times when compared to manually programmed and optimized solutions which directly use CUDA and OpenMP.

scholarly journals From the desktop to the grid and cloud: conversion of KNIME workflows to WS-PGRADE

2017 ◽  
Author(s):  
Luis de la Garza ◽  
Fabian Aicheler ◽  
Oliver Kohlbacher
Keyword(s):  
High Performance ◽  
Desktop Computer ◽  
Software Libraries ◽  
Data Flows ◽  
Combined Features ◽  
User Friendly ◽  
Computational Analyses ◽  
Performance Computing ◽  
Complex Activities ◽  
Selection Of

Computational analyses for research usually consist of a complicated orchestration of data flows, software libraries, visualization, selection of adequate parameters, etc. Structuring these complex activities into a collaboration of simple, reproducible and well defined tasks brings down complexity and increases reproducibility. This is the basic notion of workflows. Workflow engines allow users to create and execute workflows, each having unique features. In some cases, certain features offered by platforms are royalty-based, hindering use in the scientific community. We present our efforts to convert whole workflows created in the Konstanz Information Miner Analytics Platform to the Web Services Parallel Grid Runtime and Developer Environment. We see the former as a great workflow editor due to its considerable user base and user-friendly graphical interface. We deem the latter as a great backend engine able to interact with most major distributed computing interfaces. We introduce work that provides a platform-independent tool representation, thus assisting in the conversion of whole workflows. We also present the challenges inherent to workflow conversion across systems, as well as the ones posed by the conversion between the chosen workflow engines, along with our proposed solution to overcome these challenges. The combined features of these two platforms (i.e., intuitive workflow design on a desktop computer and execution of workflows on distributed high performance computing interfaces) greatly benefit researchers and minimize time spent in technical chores not directly related to their area of research.

scholarly journals An Integrated Modeling Environment to Study the Co-evolution of Networks, Individual Behavior and Epidemics

AI Magazine ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 75 ◽  
Author(s):  
Christopher Barrett ◽  
Keith Bisset ◽  
Jonathan Leidig ◽  
Achla Marathe ◽  
Madhav V. Marathe
Keyword(s):  
High Performance Computing ◽  
Disease Progression ◽  
Case Studies ◽  
High Performance ◽  
Critical Role ◽  
End User ◽  
Real World Data ◽  
Modeling Environment ◽  
Individual Behaviors ◽  
Performance Computing

We discuss an interaction-based approach to study the coevolution between socio-technical networks, individual behaviors, and contagion processes on these networks. We use epidemics in human population as an example of this phenomenon. The methods consist of developing synthetic yet realistic national-scale networks using a first principles approach. Unlike simple random graph techniques, these methods combine real world data sources with behavioral and social theories to synthesize detailed social contact (proximity) networks. Individual-based models of within-host disease progression and inter-host transmission are then used to model the contagion process. Finally, models of individual behaviors are composed with disease progression models to develop a realistic representation of the complex system in which individual behaviors and the social network adapt to the contagion. These methods are embodied within Simdemics – a general purpose modeling environment to support pandemic planning and response. Simdemics is designed specifically to be scalable to networks with 300 million agents – the underlying algorithms and methods in Simdemics are all high-performance computing oriented methods. New advances in network science, machine learning, high performance computing, data mining and behavioral modeling were necessary to develop Simdemics. Simdemics is combined with two other environments, Simfrastructure and Didactic, to form an integrated cyberenvironment. The integrated cyber-environment provides the end-user flexible and seamless Internet based access to Simdemics. Service-oriented architectures play a critical role in delivering the desired services to the end user. Simdemics, in conjunction with the integrated cyber-environment, has been used in over a dozen user defined case studies. These case studies were done to support specific policy questions that arose in the context of planning the response to pandemics (e.g., H1N1, H5N1) and human initiated bio-terrorism events. These studies played a crucial role in the continual development and improvement of the cyber-environment.

scholarly journals The architectures analyzing for computational workflows designing in a distributed environment

2020 ◽  
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Distributed Environment ◽  
End User ◽  
Distributed Resources ◽  
Computing Architecture ◽  
Performance Computing

The paper presents a model of computational workflows based on end-user understanding and provides an overview of various computational architectures, such as computing cluster, Grid, Cloud Computing, and SOA, for building workflows in a distributed environment. A comparative analysis of the capabilities of the architectures for the implementation of computational workflows have been shown that the workflows should be implemented based on SOA, since it meets all the requirements for the basic infrastructure and provides a high degree of compute nodes distribution, as well as their migration and integration with other systems in a heterogeneous environment. The Cloud Computing architecture using may be efficient when building a basic information infrastructure for the organization of distributed high-performance computing, since it supports the general and coordinated usage of dynamically allocated distributed resources, allows in geographically dispersed data centers to create and virtualize high-performance computing systems that are able to independently support the necessary QoS level and, if necessary, to use the Software as a Service (SaaS) model for end-users. The advantages of the Cloud Computing architecture do not allow the end user to realize business processes design automatically, designing them "on the fly". At the same time, there is the obvious need to create semantically oriented computing workflows based on a service-oriented architecture using a microservices approach, ontologies and metadata structures, which will allow to create workflows “on the fly” in accordance with the current request requirements.

scholarly journals JURECA: General-purpose supercomputer at Jülich Supercomputing Centre

2016 ◽  
Vol 2 ◽  
Author(s):  
Dorian Krause ◽  
Philipp Thörnig
Keyword(s):  
High Performance Computing ◽  
Data Analytics ◽  
High Performance ◽  
General Purpose ◽  
Cluster Architecture ◽  
Performance Computing ◽  
Selection Of

JURECA is a petaflop-scale, general-purpose supercomputer operated by Jülich Supercomputing Centre at Forschungszentrum Jülich. Utilizing a flexible cluster architecture based on T-Platforms V-Class blades and a balanced selection of best of its kind components the system supports a wide variety of high-performance computing and data analytics workloads and offers a low entrance barrier for new users.

scholarly journals Optimal Selection of High-Performance Concrete for Post-Tensioned Girder Bridge Using Advanced Hybrid MCDA Method

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6553
Author(s):  
Elżbieta Janowska-Renkas ◽  
Przemysław Jakiel ◽  
Dariusz Fabianowski ◽  
Damian Matyjaszczyk
Keyword(s):  
Decision Making ◽  
High Performance ◽  
High Performance Concrete ◽  
Evaluation Criteria ◽  
Analytic Hierarchy ◽  
Solution Methods ◽  
Post Tension ◽  
Girder Bridge ◽  
Research On Application ◽  
Selection Of

The selection of material solutions is a basic decision-making problem that occurs in engineering issues. It affects the entire life cycle of a building structure, its safe use, maintenance costs, and a need to meet requirements for sustainable development, including recycling. This paper aims at selection of the optimum composition of HPC designed for monolithic girder structures of post-tension bridges. For the analysis, a set of 12 new-generation concretes (HPC) was designed, made, and tested. A full-scope set of evaluation criteria was created and then the optimal alternative was selected. For this purpose, an advanced hybrid algorithm combining EA FAHP (Extent Analysis Fuzzy Analytic Hierarchy Process) and FuzzyTOPSIS (Fuzzy Technique for Order Preference by Similarity to an Ideal Solution) methods was used. The obtained results indicate a possibility for the practical application of the proposed algorithm by decision-making engineering staff. It can also be the basis for further research on application compared to other material and design solutions and, depending on the issue, different combination of aggregated methods.

Effective Open-Source Performance Analysis Tools

2012 ◽  
pp. 98-118
Author(s):  
Prashobh Balasundaram
Keyword(s):  
Performance Analysis ◽  
Open Source ◽  
High Performance ◽  
Background Information ◽  
End User ◽  
Performance Bottlenecks ◽  
Analysis Tools ◽  
Critical Components ◽  
Performance Analysis Tools ◽  
Performance Computing

This chapter presents a study of leading open source performance analysis tools for high performance computing (HPC). The first section motivates the necessity of open source tools for performance analysis. Background information on performance analysis of computational software is presented discussing the various performance critical components of computers. Metrics useful for performance analysis of common performance bottleneck patterns observed in computational codes are enumerated and followed by an evaluation of open source tools useful for extracting these metrics. The tool’s features are analyzed from the perspective of an end user. Important factors are discussed, such as the portability of tuning applied after identification of performance bottlenecks, the hardware/software requirements of the tools, the need for additional metrics for novel hardware features, and identification of these new metrics and techniques for measuring them. This chapter focuses on open source tools since they are freely available to anyone at no cost.

scholarly journals Algorithm for Clustering the Moduli of RNS for the Application of Optimization of Time Complexity in Standard Cipher System

2020 ◽  
Vol 9 (7) ◽  
pp. 92-97
Keyword(s):  
High Speed ◽  
High Performance ◽  
Fault Tolerant ◽  
Number System ◽  
Residue Number System ◽  
New Method ◽  
Residue Number ◽  
Residue Numbers ◽  
Performance Computing ◽  
Selection Of

Residue number system (RNS) has emerged as a knocking field of research due to its high speed, fault tolerant, carry free and parallel characteristics. Due to these features it has got important role in high performance computing especially with reduced delay. There are various algorithms have been found as a result of the research with respect to RNS. Additionally, since RNS reduces word length due to the modular operations, its computations are faster compared to binary computations. But the major challenges are the selection of moduli sets for the forward (decimal to residue numbers) and reverse (residue numbers to decimal) conversion. RNS performance is purely depending on how efficiently an algorithm computes / chooses the moduli sets [1]-[6]. This paper proposes new method for selecting the moduli sets and its usage in cryptographic applications based on Schonhage modular factorization. The paper proposes six moduli sets {6qk1, 6qk+1, 6qk+3, 6qk+5, 6qk+7, 6qk+11} for the RNS conversions but the Schonhage moduli sets are expressed as the exponents that creates a large gap between the moduli’s computed. Hence, a new method is proposed to for computing moduli sets that helps in representing all the decomposed values approximately in the same range.

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