Capturing the impact of external interference on HPC application performance

HPC applications are large software packages with high computation and storage requirements. To meet these requirements, the architectures of supercomputers are continuously evolving and their capabilities are continuously increasing. Present-day supercomputers have achieved petaflops of computational power by utilizing thousands to millions of compute cores, connected through specialized communication networks, and are equipped with petabytes of storage using a centralized I/O subsystem. While fulfilling the high resource demands of HPC applications, such a design also entails its own challenges. Applications running on these systems own the computation resources exclusively, but share the communication interconnect and the I/O subsystem with other concurrently running applications. Simultaneous access to these shared resources causes contention and inter-application interference, leading to degraded application performance. Inter-application interference is one of the sources of run-to-run variation. While other sources of variation, such as operating system jitter, have been investigated before, this doctoral thesis specifically focuses on inter-application interference and studies it from the perspective of an application. Variation in execution time not only causes uncertainty and affects user expectations (especially during performance analysis), but also causes suboptimal usage of HPC resources. Therefore, this thesis aims to evaluate inter-application interference, establish trends among applications under contention, and approximate the impact of external influences on the runtime of an application. To this end, this thesis first presents a method to correlate the performance of applications running side-by-side. The method divides the runtime of a system into globally synchronized, fine-grained time slices for which application performance data is recorded separately. The evaluation of the method demonstrates that correlating application performance data can identify inter-application interference. The thesis further uses the method to study I/O interference and shows that file access patterns are a significant factor in determining the interference potential of an application. This thesis also presents a technique to estimate the impact of external influences on an application run. The technique introduces the concept of intrinsic performance characteristics to cluster similar application execution segments. Anomalies in the cluster are the result of external interference. An evaluation with several benchmarks shows high accuracy in estimating the impact of interference from a single application run. The contributions of this thesis will help establish interference trends and devise interference mitigation techniques. Similarly, estimating the impact of external interference will restore user expectations and help performance analysts separate application performance from external influence

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Contributions to Edge Computing

Efforts related to Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, Industrial Internet, and Smart Cities aim to improve society through the coordination of distributed devices and analysis of resulting data. By the year 2020 there will be an estimated 50 billion network connected devices globally and 43 trillion gigabytes of electronic data. Current practices of moving data directly from end-devices to remote and potentially distant cloud computing services will not be sufficient to manage future device and data growth. Edge Computing is the migration of computational functionality to sources of data generation. The importance of edge computing increases with the size and complexity of devices and resulting data. In addition, the coordination of global edge-to-edge communications, shared resources, high-level application scheduling, monitoring, measurement, and Quality of Service (QoS) enforcement will be critical to address the rapid growth of connected devices and associated data. We present a new distributed agent-based framework designed to address the challenges of edge computing. This actor-model framework implementation is designed to manage large numbers of geographically distributed services, comprised from heterogeneous resources and communication protocols, in support of low-latency real-time streaming applications. As part of this framework, an application description language was developed and implemented. Using the application description language a number of high-order management modules were implemented including solutions for resource and workload comparison, performance observation, scheduling, and provisioning. A number of hypothetical and real-world use cases are described to support the framework implementation

Performance analysis for parallel programs from multicore to petascale

Cutting-edge science and engineering applications require petascale computing. Petascale computing platforms are characterized by both extreme parallelism (systems of hundreds of thousands to millions of cores) and hybrid parallelism (nodes with multicore chips). Consequently, to effectively use petascale resources, applications must exploit concurrency at both the node and system level --- a difficult problem. The challenge of developing scalable petascale applications is only partially aided by existing languages and compilers. As a result, manual performance tuning is often necessary to identify and resolve poor parallel and serial efficiency. Our thesis is that it is possible to achieve unique, accurate, and actionable insight into the performance of fully optimized parallel programs by measuring them with asynchronous-sampling-based call path profiles; attributing the resulting binary-level measurements to source code structure; analyzing measurements on-the-fly and postmortem to highlight performance inefficiencies; and presenting the resulting context- sensitive metrics in three complementary views. To support this thesis, we have developed several techniques for identifying performance problems in fully optimized serial, multithreaded and petascale programs. First, we describe how to attribute very precise (instruction-level) measurements to source-level static and dynamic contexts in fully optimized applications --- all for an average run-time overhead of a few percent. We then generalize this work with the development of logical call path profiling and apply it to work-stealing-based applications. Second, we describe techniques for pinpointing and quantifying parallel inefficiencies such as parallel idleness, parallel overhead and lock contention in multithreaded executions. Third, we show how to diagnose scalability bottlenecks in petascale applications by scaling our our measurement, analysis and presentation tools to support large-scale executions. Finally, we provide a coherent framework for these techniques by sketching a unique and comprehensive performance analysis methodology. This work forms the basis of Rice University's HPCTOOLKIT performance tools

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Network-on-Chip

Limitations of bus-based interconnections related to scalability, latency, bandwidth, and power consumption for supporting the related huge number of on-chip resources result in a communication bottleneck. These challenges can be efficiently addressed with the implementation of a network-on-chip (NoC) system. This book gives a detailed analysis of various on-chip communication architectures and covers different areas of NoCs such as potentials, architecture, technical challenges, optimization, design explorations, and research directions. In addition, it discusses current and future trends that could make an impactful and meaningful contribution to the research and design of on-chip communications and NoC systems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

SpiNNaker - A Spiking Neural Network Architecture

20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come