EXTRA: Towards the exploitation of eXascale technology for reconfigurable architectures

© 2016 IEEE. To handle the stringent performance requirements of future exascale-class applications, High Performance Computing (HPC) systems need ultra-efficient heterogeneous compute nodes. To reduce power and increase performance, such compute nodes will require hardware accelerators with a high degree of specialization. Ideally, dynamic reconfiguration will be an intrinsic feature, so that specific HPC application features can be optimally accelerated, even if they regularly change over time. In the EXTRA project, we create a new and flexible exploration platform for developing reconfigurable architectures, design tools and HPC applications with run-time reconfiguration built-in as a core fundamental feature instead of an add-on. EXTRA covers the entire stack from architecture up to the application, focusing on the fundamental building blocks for run-time reconfigurable exascale HPC systems: new chip architectures with very low reconfiguration overhead, new tools that truly take reconfiguration as a central design concept, and applications that are tuned to maximally benefit from the proposed run-time reconfiguration techniques. Ultimately, this open platform will improve Europe's competitive advantage and leadership in the field

Efficiency analysis methodology of FPGAs based on lost frequencies, area and cycles

We propose a methodology to study and to quantify efficiency and the impact of overheads on runtime performance. Most work on High-Performance Computing (HPC) for FPGAs only studies runtime performance or cost, while we are interested in how far we are from peak performance and, more importantly, why. The efficiency of runtime performance is defined with respect to the ideal computational runtime in absence of inefficiencies. The analysis of the difference between actual and ideal runtime reveals the overheads and bottlenecks. A formal approach is proposed to decompose the efficiency into three components: frequency, area and cycles. After quantification of the efficiencies, a detailed analysis has to reveal the reasons for the lost frequencies, lost area and lost cycles. We propose a taxonomy of possible causes and practical methods to identify and quantify the overheads. The proposed methodology is applied on a number of use cases to illustrate the methodology. We show the interaction between the three components of efficiency and show how bottlenecks are revealed

A protocol reconfiguration and optimization system for MPI

Modern high performance computing (HPC) applications, for example adaptive mesh refinement and multi-physics codes, have dynamic communication characteristics which result in poor performance on current Message Passing Interface (MPI) implementations. The degraded application performance can be attributed to a mismatch between changing application requirements and static communication library functionality. To improve the performance of these applications, MPI libraries should change their protocol functionality in response to changing application requirements, and tailor their functionality to take advantage of hardware capabilities. This dissertation describes Protocol Reconfiguration and Optimization system for MPI (PRO-MPI), a framework for constructing profile-driven reconfigurable MPI libraries; these libraries use past application characteristics (profiles) to dynamically change their functionality to match the changing application requirements. The framework addresses the challenges of designing and implementing the reconfigurable MPI libraries, which include collecting and reasoning about application characteristics to drive the protocol reconfiguration and defining abstractions required for implementing these reconfigurations. Two prototype reconfigurable MPI implementations based on the framework - Open PRO-MPI and Cactus PRO-MPI - are also presented to demonstrate the utility of the framework. To demonstrate the effectiveness of reconfigurable MPI libraries, this dissertation presents experimental results to show the impact of using these libraries on the application performance. The results show that PRO-MPI improves the performance of important HPC applications and benchmarks. They also show that HyperCLaw performance improves by approximately 22% when exact profiles are available, and HyperCLaw performance improves by approximately 18% when only approximate profiles are available

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter

Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft

The use of direct current distribution systems in delivering scalable charging infrastructure for battery electric vehicles

The use of low voltage direct current (LVDC) distribution is becoming recognised as a technology enabler that can be used to efficiently network native DC generators with DC loads, offer improved power sharing capabilities, reduce power system material resource requirements and enhance the performance of variable speed machinery. Practical deployment opportunities for LVDC range from small-scale microgrids in the context of energy for development to sophisticated, modern building-level power distribution systems for commercial office spaces, manufacturing applications and industrial processes. However, the incumbent AC distribution system benefits from existing technical product and safety standards, which makes the early adoption of LVDC systems challenging from a risk and cost perspective. Concurrently, the demand for native DC loads such as Battery Electric Transportation Systems is growing. This is especially significant in the area of private electric vehicles (EVs), taxis and buses, but the prospect of electric trucks, ferries and shortrange aircraft are also tangible opportunities. The success of this electric transport revolution depends on several factors, one of which is the availability of battery charging infrastructure that can cost effectively integrate with the existing electrical network, deliver adequate energy transfer rates and adapt to the rapid technical development of this industry. This thesis explores the application of two, novel LVDC distribution systems for the development of scalable EV charging networks; where charging infrastructure has the ability to scale with increasing EV adoption and has a lower risk of becoming a stranded asset in the future. The modelling is supported by real, rapid DC charger utilisation data from the national charging network in Scotland, comprising over 192 chargers and 400,000 charging events. During the work of this thesis, it was found that a combined heat and power (CHP) system can economically support short duration charging scenarios by providing additional power capacity in a congested electrical grid. In this case the highest system efficiency and Net Present Value (NPV) is achieved with a fuel cell directly connected to the DC charging network, compared to other gas reciprocating CHP options. Furthermore, the proposition of a reconfigurable LVDC charging network, interfaced to the public AC distribution network, reduces the capital outlay, offers a higher NPV and improved scalability compared to other charging solutions. For charging system designers and operators, it was found that rapid DC chargers can be classified by specific locations, each possessing a distinct Gaussian arrival pattern and Gamma distribution for charging energy delivered.The use of low voltage direct current (LVDC) distribution is becoming recognised as a technology enabler that can be used to efficiently network native DC generators with DC loads, offer improved power sharing capabilities, reduce power system material resource requirements and enhance the performance of variable speed machinery. Practical deployment opportunities for LVDC range from small-scale microgrids in the context of energy for development to sophisticated, modern building-level power distribution systems for commercial office spaces, manufacturing applications and industrial processes. However, the incumbent AC distribution system benefits from existing technical product and safety standards, which makes the early adoption of LVDC systems challenging from a risk and cost perspective. Concurrently, the demand for native DC loads such as Battery Electric Transportation Systems is growing. This is especially significant in the area of private electric vehicles (EVs), taxis and buses, but the prospect of electric trucks, ferries and shortrange aircraft are also tangible opportunities. The success of this electric transport revolution depends on several factors, one of which is the availability of battery charging infrastructure that can cost effectively integrate with the existing electrical network, deliver adequate energy transfer rates and adapt to the rapid technical development of this industry. This thesis explores the application of two, novel LVDC distribution systems for the development of scalable EV charging networks; where charging infrastructure has the ability to scale with increasing EV adoption and has a lower risk of becoming a stranded asset in the future. The modelling is supported by real, rapid DC charger utilisation data from the national charging network in Scotland, comprising over 192 chargers and 400,000 charging events. During the work of this thesis, it was found that a combined heat and power (CHP) system can economically support short duration charging scenarios by providing additional power capacity in a congested electrical grid. In this case the highest system efficiency and Net Present Value (NPV) is achieved with a fuel cell directly connected to the DC charging network, compared to other gas reciprocating CHP options. Furthermore, the proposition of a reconfigurable LVDC charging network, interfaced to the public AC distribution network, reduces the capital outlay, offers a higher NPV and improved scalability compared to other charging solutions. For charging system designers and operators, it was found that rapid DC chargers can be classified by specific locations, each possessing a distinct Gaussian arrival pattern and Gamma distribution for charging energy delivered

Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives

© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, Vol. 53, No. 5, Article 95. Publication date: September 2020. https://doi.org/10.1145/3403956[EN] Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.This work has received funding from the European Union's Horizon 2020 (H2020) research and innovation program under the FET-HPC Grant Agreement No. 801137 (RECIPE). Jaume Abella was also partially supported by the Ministry of Economy and Competitiveness of Spain under Contract No. TIN2015-65316-P and under Ramon y Cajal Postdoctoral Fellowship No. RYC-2013-14717, as well as by the HiPEAC Network of Excellence. Ramon Canal is partially supported by the Generalitat de Catalunya under Contract No. 2017SGR0962.Canal, R.; Hernández Luz, C.; Tornero-Gavilá, R.; Cilardo, A.; Massari, G.; Reghenzani, F.; Fornaciari, W.... (2020). Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives. ACM Computing Surveys. 53(5):1-32. https://doi.org/10.1145/3403956S132535Abella, J., Hernandez, C., Quinones, E., Cazorla, F. J., Conmy, P. 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