Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Dataflow Programming Paradigms for Computational Chemistry Methods

The transition to multicore and heterogeneous architectures has shaped the High Performance Computing (HPC) landscape over the past decades. With the increase in scale, complexity, and heterogeneity of modern HPC platforms, one of the grim challenges for traditional programming models is to sustain the expected performance at scale. By contrast, dataflow programming models have been growing in popularity as a means to deliver a good balance between performance and portability in the post-petascale era. This work introduces dataflow programming models for computational chemistry methods, and compares different dataflow executions in terms of programmability, resource utilization, and scalability. This effort is driven by computational chemistry applications, considering that they comprise one of the driving forces of HPC. In particular, many-body methods, such as Coupled Cluster methods (CC), which are the gold standard to compute energies in quantum chemistry, are of particular interest for the applied chemistry community. On that account, the latest development for CC methods is used as the primary vehicle for this research, but our effort is not limited to CC and can be applied across other application domains. Two programming paradigms for expressing CC methods into a dataflow form, in order to make them capable of utilizing task scheduling systems, are presented. Explicit dataflow, is the programming model where the dataflow is explicitly specified by the developer, is contrasted with implicit dataflow, where a task scheduling runtime derives the dataflow. An abstract model is derived to explore the limits of the different dataflow programming paradigms

Using program behaviour to exploit heterogeneous multi-core processors

Multi-core CPU architectures have become prevalent in recent years. A number of multi-core CPUs consist of not only multiple processing cores, but multiple different types of processing cores, each with different capabilities and specialisations. These heterogeneous multi-core architectures (HMAs) can deliver exceptional performance; however, they are notoriously difficult to program effectively. This dissertation investigates the feasibility of ameliorating many of the difficulties encountered in application development on HMA processors, by employing a behaviour aware runtime system. This runtime system provides applications with the illusion of executing on a homogeneous architecture, by presenting a homogeneous virtual machine interface. The runtime system uses knowledge of a program's execution behaviour, gained through explicit code annotations, static analysis or runtime monitoring, to inform its resource allocation and scheduling decisions, such that the application makes best use of the HMA's heterogeneous processing cores. The goal of this runtime system is to enable non-specialist application developers to write applications that can exploit an HMA, without the developer requiring in-depth knowledge of the HMA's design. This dissertation describes the development of a Java runtime system, called Hera-JVM, aimed at investigating this premise. Hera-JVM supports the execution of unmodified Java applications on both processing core types of the heterogeneous IBM Cell processor. An application's threads of execution can be transparently migrated between the Cell's different core types by Hera-JVM, without requiring the application's involvement. A number of real-world Java benchmarks are executed across both of the Cell's core types, to evaluate the efficacy of abstracting a heterogeneous architecture behind a homogeneous virtual machine. By characterising the performance of each of the Cell processor's core types under different program behaviours, a set of influential program behaviour characteristics is uncovered. A set of code annotations are presented, which enable program code to be tagged with these behaviour characteristics, enabling a runtime system to track a program's behaviour throughout its execution. This information is fed into a cost function, which Hera-JVM uses to automatically estimate whether the executing program's threads of execution would benefit from being migrated to a different core type, given their current behaviour characteristics. The use of history, hysteresis and trend tracking, by this cost function, is explored as a means of increasing its stability and limiting detrimental thread migrations. The effectiveness of a number of different migration strategies is also investigated under real-world Java benchmarks, with the most effective found to be a strategy that can target code, such that a thread is migrated whenever it executes this code. This dissertation also investigates the use of runtime monitoring to enable a runtime system to automatically infer a program's behaviour characteristics, without the need for explicit code annotations. A lightweight runtime behaviour monitoring system is developed, and its effectiveness at choosing the most appropriate core type on which to execute a set of real-world Java benchmarks is examined. Combining explicit behaviour characteristic annotations with those characteristics which are monitored at runtime is also explored. Finally, an initial investigation is performed into the use of behaviour characteristics to improve application performance under a different type of heterogeneous architecture, specifically, a non-uniform memory access (NUMA) architecture. Thread teams are proposed as a method of automatically clustering communicating threads onto the same NUMA node, thereby reducing data access overheads. Evaluation of this approach shows that it is effective at improving application performance, if the application's threads can be partitioned across the available NUMA nodes of a system. The findings of this work demonstrate that a runtime system with a homogeneous virtual machine interface can reduce the challenge of application development for HMA processors, whilst still being able to exploit such a processor by taking program behaviour into account

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Algorithmic skeleton framework for the orchestration of GPU computations

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe Graphics Processing Unit (GPU) is gaining popularity as a co-processor to the Central Processing Unit (CPU), due to its ability to surpass the latter’s performance in certain application fields. Nonetheless, harnessing the GPU’s capabilities is a non-trivial exercise that requires good knowledge of parallel programming. Thus, providing ways to extract such computational power has become an emerging research topic. In this context, there have been several proposals in the field of GPGPU (Generalpurpose Computation on Graphics Processing Unit) development. However, most of these still offer a low-level abstraction of the GPU computing model, forcing the developer to adapt application computations in accordance with the SPMD model, as well as to orchestrate the low-level details of the execution. On the other hand, the higher-level approaches have limitations that prevent the full exploitation of GPUs when the purpose goes beyond the simple offloading of a kernel. To this extent, our proposal builds on the recent trend of applying the notion of algorithmic patterns (skeletons) to GPU computing. We propose Marrow, a high-level algorithmic skeleton framework that expands the set of skeletons currently available in this field. Marrow’s skeletons orchestrate the execution of OpenCL computations and introduce optimizations that overlap communication and computation, thus conjoining programming simplicity with performance gains in many application scenarios. Additionally, these skeletons can be combined (nested) to create more complex applications. We evaluated the proposed constructs by confronting them against the comparable skeleton libraries for GPGPU, as well as against hand-tuned OpenCL programs. The results are favourable, indicating that Marrow’s skeletons are both flexible and efficient in the context of GPU computing.FCT-MCTES - financing the equipmen

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

Architecture matérielle logicielle pour l'exécution à latence réduite d'applications de télécommunications émergentes sur centre de données

RÉSUMÉ L’industrie des technologies de l’information et des communications fait face à une demande croissante de services sans fil et Internet omniprésents. Cette demande est alimentée par une explosion du nombre d’appareils mobiles riches en multimédia. Il a été estimé qu’à partir de cette année, 2020, le volume de trafic de données mobiles doublera chaque année pour plusieurs années. En conséquence, il en résulte une augmentation significative des dépenses en capital pour les systèmes construits sur les technologies actuelles de réseau d’accès ra-dio qui sont essentiellement basées sur des architectures avec une structure fixe utilisant des plates-formes propriétaires et des mécanismes de contrôle et de gestion de réseau distribués. D’autre part, pour garantir la qualité de service requise, les sous-systèmes sont dimensionnés en fonction des demandes de pointe. Par conséquent, l’extension du réseau aura un impact considérable sur les dépenses d’exploitation. La recherche proposée vise à développer une architecture matérielle et logicielle adaptée à une grappe d’unités de traitement virtualisée pour les signaux en bande de base d’accès radio en nuagique. Ce type d’architecture de-vra prendre en charge le traitement en temps réel avec des processeurs généralistes sur une plateforme hétérogène. Cela soulève deux défis principaux : la planification des tâches en temps réel et leur exécution d’une manière plus déterministe par rapport aux plates-formes généralistes existantes. Ainsi, les mécanismes d’allocation et de gestion des ressources dans les grappes informatiques doivent être revus. Le deuxième défi est d’obtenir un comporte-ment à faible variance qui implique deux préoccupations majeures : le temps de calcul et le délai de communication. Essentiellement, la variation du temps de calcul est inhérente à tous les processeurs généralistes. Néanmoins, l’infrastructure de communication des grappes informatiques existantes ne fournit aucun soutien pour les communications à faible variance. La recherche proposée est divisée en deux principaux sujets : Le calcul dynamique, l’allocation et la gestion des ressources réseau dans une grappeinformatique (hétérogène) : les algorithmes d’allocation dynamique des ressources et de planification des tâches en temps réel formeront la fonctionnalité de base prise en charge par le plan de contrôle. Afin de répondre aux fortes contraintes en temps réel de cette classe d’applications, une implémentation matérielle parallèle basée sur circuit logique programmable (FPGA) du plan de contrôle est proposée.----------ABSTRACT The Information and Communications Technology industry is facing an increasing demand for ubiquitous wireless and Internet services introduced by an explosion of multimedia-rich mobile devices. It is estimated that starting this year, 2020, the volume of mobile data traÿcs will double every year. Consequently, it results in significant increases of capital expenditures for systems built on the current Radio Access Network technologies, which are essentially based on architectures with a fixed structure (not reconfigurable) using proprietary platforms with distributed network control and management mechanisms. To ensure the required quality of service, subsystems are dimensioned with respect to the peak demands. Therefore, network expansion will considerably impact on operating expenditures. This thesis aims at developing an architecture at both hardware and software levels suitable for a virtualized Baseband Processing Unit pool in Cloud Radio Acces Network in order to support real-time processing in a General Purpose Processor based platform. This raises two main challenges: scheduling tasks in real-time and executing them in a manner that is reduces variance compared to the existing General Purpose Processor based platforms. Real-time tasks from radio air interface in the Cloud Radio Access Network must be scheduled at a finer grain and must be completed within a given timeslot. Thus, mechanisms for resource allocation and management in computing clusters must be revisited. The second challenge is obtaining a behavior with reduced variability that involves two major concerns: computing time and communication delay. Nevertheless, the communication infrastructure of existing computing clusters does not provide any support for low variance communications. The proposed research is divided into the following main subjects:Adaptive computing and network resource allocation and management in (hetero-geneous) computing clusters: The algorithms for dynamic resources allocation and real-time task scheduling will form the core functionality that the control plane will support. In order to meet the hard real-time constraints of that class of applications, a parallel Field Programable Gate Array based hardware implementation of the control plane is proposed

A computing origami: Optimized code generation for emerging parallel platforms

Ph.DDOCTOR OF PHILOSOPH