Final TANGO Toolbox is released as Open Source
On behalf of Heterogeneity Alliance, TANGO has the opportunity to attend HiPEAC 2019, this time in Valencia (Spain).
The last communication of TANGO is about to announce that TANGO comes to the end of its life!
All the objectives has been succesfully achieved due to the efforts made by its team. We want to finish sharing this press note to show the main results and comments from our team about the project.
For further details you can contact the project coordinator; Clara Pezuela (email@example.com)
This video explains the benefits of TANGO for HPC scenarios and how the Self Adaptation Manager, one of the TANGO toolbox components, works to facilitate the job of developers in this domain.
TANGO project publishes this time an article in the HIPEAC Magazine about how to exploit heterogeneity through collaboration in the Heterogeneity Alliance
TANGO project has just launched the Heterogeneity Alliance, and this arcticle present this initiative to HIPEAC community.
Clara Pezuela, the TANGO project coordinator and current chairman of the Heterogeneity Alliance, is interviewed about objective, benefits and activities of the initiative.
Please, find the articles in the following link: www.hipeac.net/media/public/magazine/HIPEACinfo56_lowres.pdf
This document (D2.3 TANGO Requirements and Architecture Specification Final Version) progresses with this TANGO vision by analysing again the user requirements and updating how to best shape the TANGO architecture to increase chance of successful industry adoption. The document is an update of the Year 2 deliverable D2.2 TANGO Requirements and Architecture Specification Beta Version, which in turn builds upon Deliverable D2.1 TANGO Requirements and Architecture Specification Alpha Version.
This document (D5.1) accompanies the software release of the TANGO project at the end of Year-3 under Work Package 5. It presents the installation manuals of the different software components found in the overall TANGO architecture. During this period, several components have been updated to provide new features implemented. These new features have focused on enhancing programmer productivity, holistic self-adaptation and examining additional adjunct features such as security, robustness and maintainability.
The scientific contributions in Year 3 have focussed on the application design, development and deployment productivity and the holistic self-adaptation. In addition to these main topics, this deliverable also includes other scientific contributions such as the TANGO security solution.
This document presents the verification and validation tests performed on the TANGO toolbox conducted at the end of year 3 of the TANGO project in the frame of work package 6.
Document presents first the integration tests that have been conducted in order to verify interoperability of TANGO components.
Cloud datacenters are compute facilities formed by hundreds and thousands of heterogeneous servers requiring significant power requirements to operate effectively. Servers are composed by multiple interacting sub-systems including applications, microelectronic processors, and cooling which reflect their respective power profiles via different parameters. What is presently unknown is how to accurately model the holistic power usage of the entire server when including all these sub-systems together.
Scheduling is a core component within distributed systems to determine optimal allocation of tasks within servers. This is challenging within modern Cloud computing systems - comprising millions of tasks executing in thousands of heterogeneous servers. Theoretical scheduling is capable of providing complete and sophisticated algorithms towards a single objective function. However, Cloud computing systems pursue multiple and oftentimes conflicting objectives towards provisioning high levels of performance, availability, reliability and energy-efficiency.
Towards an Energy-aware Framework for application development and execution in Heterogeneous parallel architectures
Hardware in HPC environments in recent years has become ever more heterogeneous in order to improve computational performance and as an aspect of managing power and energy constraints. This increase in heterogeneity requires middleware abstractions to eliminate additional complexities that it brings. In this paper we present a self-adaptation framework which includes aspects such as automated configuration, deployment and redeployment of applications to different heterogeneous infrastructure.
Cloud computing providers resort to a variety of techniques to improve energy consumption at each level of the cloud computing stack. Most of these techniques consider resource-level energy optimisation at IaaS layer. This paper argues energy gains can be obtained by creating a cooperation between the PaaS layer (in charge of hosting the application/service) and the IaaS layer (in charge of handling the computing resources).
Python has been adopted as programming language by a large number of scientific communities. Additionally to the easy programming interface, the large number of libraries and modules that have been made available by a large number of contributors, have taken this language to the top of the list of the most popular programming languages in scientific applications. However, one main drawback of Python is the lack of support for concurrency or parallelism.
Python is a popular programming language due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. The adoption from multiple scientific communities has evolved in the emergence of a large number of libraries and modules, which has helped to put Python on the top of the list of the programming languages . Task-based programming has been proposed in the recent years as an alternative parallel programming model.
The persistent development of Cloud computing attracts individuals and organisations to change their IT strategies. According to this development and the incremental demand of using Cloud computing, Cloud providers continuously update the Cloud infrastructure to fit the incremental demands. Recently, accelerator units, such as Graphics Processing Units (GPUs) have been introduced in Cloud computing. This updated existence leads to provide an increase in hardware heterogeneity in the Cloud infrastructure. With the increase in hardware heterogeneity, new issues will appear.
Virtual Machines (VMs) auto-scaling is an important technique to provision additional resource capacity in a Cloud environment. It allows the VMs to dynamically increase or decrease the amount of resources as needed in order to meet Quality of Service (QoS) requirements. However, the auto-scaling mechanism can be time-consuming to initiate (e.g. in the order of a minute), which is unacceptable for VMs that need to scale up/out during the computation, besides additional costs due to the increase of the energy overhead.
Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energybased Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance.
Python is a popular programming language due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. The adoption from multiple scientific communities has evolved in the emergence of a large number of libraries and modules, which has helped to put Python on the top of the list of the programming languages. Task-based programming has been proposed in the recent years as an alternative parallel programming model.