Optimization of Computationally and I/O Intense Patterns in Electronic Structure and Machine Learning Algorithms

Development of scalable High-Performance Computing (HPC) applications is already a challenging task even in the pre-Exascale era. Utilization of the full potential of (near-)future supercomputers will most likely require the mastery of massively parallel heterogeneous architectures with multi-tier persistence systems, ideally in fault tolerant mode. With the change in hardware architectures HPC applications are also widening their scope to `Big data' processing and analytics using machine learning algorithms and neural networks. In this work, in cooperation with the INTERTWinE FET-HPC project, we demonstrate how the GASPI (Global Address Space Programming Interface) programming model helps to address these Exascale challenges on examples of tensor contraction, K-means and Terasort algorithms

Gestão e engenharia de CAP na nuvem híbrida

Doutoramento em InformáticaThe evolution and maturation of Cloud Computing created an opportunity for the emergence of new Cloud applications. High-performance Computing, a complex problem solving class, arises as a new business consumer by taking advantage of the Cloud premises and leaving the expensive datacenter management and difficult grid development. Standing on an advanced maturing phase, today’s Cloud discarded many of its drawbacks, becoming more and more efficient and widespread. Performance enhancements, prices drops due to massification and customizable services on demand triggered an emphasized attention from other markets. HPC, regardless of being a very well established field, traditionally has a narrow frontier concerning its deployment and runs on dedicated datacenters or large grid computing. The problem with common placement is mainly the initial cost and the inability to fully use resources which not all research labs can afford. The main objective of this work was to investigate new technical solutions to allow the deployment of HPC applications on the Cloud, with particular emphasis on the private on-premise resources – the lower end of the chain which reduces costs. The work includes many experiments and analysis to identify obstacles and technology limitations. The feasibility of the objective was tested with new modeling, architecture and several applications migration. The final application integrates a simplified incorporation of both public and private Cloud resources, as well as HPC applications scheduling, deployment and management. It uses a well-defined user role strategy, based on federated authentication and a seamless procedure to daily usage with balanced low cost and performance.O desenvolvimento e maturação da Computação em Nuvem abriu a janela de oportunidade para o surgimento de novas aplicações na Nuvem. A Computação de Alta Performance, uma classe dedicada à resolução de problemas complexos, surge como um novo consumidor no Mercado ao aproveitar as vantagens inerentes à Nuvem e deixando o dispendioso centro de computação tradicional e o difícil desenvolvimento em grelha. Situando-se num avançado estado de maturação, a Nuvem de hoje deixou para trás muitas das suas limitações, tornando-se cada vez mais eficiente e disseminada. Melhoramentos de performance, baixa de preços devido à massificação e serviços personalizados a pedido despoletaram uma atenção inusitada de outros mercados. A CAP, independentemente de ser uma área extremamente bem estabelecida, tradicionalmente tem uma fronteira estreita em relação à sua implementação. É executada em centros de computação dedicados ou computação em grelha de larga escala. O maior problema com o tipo de instalação habitual é o custo inicial e o não aproveitamento dos recursos a tempo inteiro, fator que nem todos os laboratórios de investigação conseguem suportar. O objetivo principal deste trabalho foi investigar novas soluções técnicas para permitir o lançamento de aplicações CAP na Nuvem, com particular ênfase nos recursos privados existentes, a parte peculiar e final da cadeia onde se pode reduzir custos. O trabalho inclui várias experiências e análises para identificar obstáculos e limitações tecnológicas. A viabilidade e praticabilidade do objetivo foi testada com inovação em modelos, arquitetura e migração de várias aplicações. A aplicação final integra uma agregação de recursos de Nuvens, públicas e privadas, assim como escalonamento, lançamento e gestão de aplicações CAP. É usada uma estratégia de perfil de utilizador baseada em autenticação federada, assim como procedimentos transparentes para a utilização diária com um equilibrado custo e performance

Enabling Scalability: Graph Hierarchies and Fault Tolerance

In this dissertation, we explore approaches to two techniques for building scalable algorithms. First, we look at different graph problems. We show how to exploit the input graph\u27s inherent hierarchy for scalable graph algorithms. The second technique takes a step back from concrete algorithmic problems. Here, we consider the case of node failures in large distributed systems and present techniques to quickly recover from these. In the first part of the dissertation, we investigate how hierarchies in graphs can be used to scale algorithms to large inputs. We develop algorithms for three graph problems based on two approaches to build hierarchies. The first approach reduces instance sizes for NP-hard problems by applying so-called reduction rules. These rules can be applied in polynomial time. They either find parts of the input that can be solved in polynomial time, or they identify structures that can be contracted (reduced) into smaller structures without loss of information for the specific problem. After solving the reduced instance using an exponential-time algorithm, these previously contracted structures can be uncontracted to obtain an exact solution for the original input. In addition to a simple preprocessing procedure, reduction rules can also be used in branch-and-reduce algorithms where they are successively applied after each branching step to build a hierarchy of problem kernels of increasing computational hardness. We develop reduction-based algorithms for the classical NP-hard problems Maximum Independent Set and Maximum Cut. The second approach is used for route planning in road networks where we build a hierarchy of road segments based on their importance for long distance shortest paths. By only considering important road segments when we are far away from the source and destination, we can substantially speed up shortest path queries. In the second part of this dissertation, we take a step back from concrete graph problems and look at more general problems in high performance computing (HPC). Here, due to the ever increasing size and complexity of HPC clusters, we expect hardware and software failures to become more common in massively parallel computations. We present two techniques for applications to recover from failures and resume computation. Both techniques are based on in-memory storage of redundant information and a data distribution that enables fast recovery. The first technique can be used for general purpose distributed processing frameworks: We identify data that is redundantly available on multiple machines and only introduce additional work for the remaining data that is only available on one machine. The second technique is a checkpointing library engineered for fast recovery using a data distribution method that achieves balanced communication loads. Both our techniques have in common that they work in settings where computation after a failure is continued with less machines than before. This is in contrast to many previous approaches that---in particular for checkpointing---focus on systems that keep spare resources available to replace failed machines. Overall, we present different techniques that enable scalable algorithms. While some of these techniques are specific to graph problems, we also present tools for fault tolerant algorithms and applications in a distributed setting. To show that those can be helpful in many different domains, we evaluate them for graph problems and other applications like phylogenetic tree inference

Space Transportation System and associated payloads: Glossary, acronyms, and abbreviations

A collection of acronyms in everyday use concerning shuttle activities is presented. A glossary of terms pertaining to the Space Transportation System is included

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

Space transportation system and associated payloads: Glossary, acronyms, and abbreviations

A collection of some of the acronyms and abbreviations now in everyday use in the shuttle world is presented. It is a combination of lists that were prepared at Marshall Space Flight Center and Kennedy and Johnson Space Centers, places where intensive shuttle activities are being carried out. This list is intended as a guide or reference and should not be considered to have the status and sanction of a dictionary

Accelerating Network Communication and I/O in Scientific High Performance Computing Environments

High performance computing has become one of the major drivers behind technology inventions and science discoveries. Originally driven through the increase of operating frequencies and technology scaling, a recent slowdown in this evolution has led to the development of multi-core architectures, which are supported by accelerator devices such as graphics processing units (GPUs). With the upcoming exascale era, the overall power consumption and the gap between compute capabilities and I/O bandwidth have become major challenges. Nowadays, the system performance is dominated by the time spent in communication and I/O, which highly depends on the capabilities of the network interface. In order to cope with the extreme concurrency and heterogeneity of future systems, the software ecosystem of the interconnect needs to be carefully tuned to excel in reliability, programmability, and usability. This work identifies and addresses three major gaps in today's interconnect software systems. The I/O gap describes the disparity in operating speeds between the computing capabilities and second storage tiers. The communication gap is introduced through the communication overhead needed to synchronize distributed large-scale applications and the mixed workload. The last gap is the so called concurrency gap, which is introduced through the extreme concurrency and the inflicted learning curve posed to scientific application developers to exploit the hardware capabilities. The first contribution is the introduction of the network-attached accelerator approach, which moves accelerators into a "stand-alone" cluster connected through the Extoll interconnect. The novel communication architecture enables the direct accelerators communication without any host interactions and an optimal application-to-compute-resources mapping. The effectiveness of this approach is evaluated for two classes of accelerators: Intel Xeon Phi coprocessors and NVIDIA GPUs. The next contribution comprises the design, implementation, and evaluation of the support of legacy codes and protocols over the Extoll interconnect technology. By providing TCP/IP protocol support over Extoll, it is shown that the performance benefits of the interconnect can be fully leveraged by a broader range of applications, including the seamless support of legacy codes. The third contribution is twofold. First, a comprehensive analysis of the Lustre networking protocol semantics and interfaces is presented. Afterwards, these insights are utilized to map the LNET protocol semantics onto the Extoll networking technology. The result is a fully functional Lustre network driver for Extoll. An initial performance evaluation demonstrates promising bandwidth and message rate results. The last contribution comprises the design, implementation, and evaluation of two easy-to-use load balancing frameworks, which transparently distribute the I/O workload across all available storage system components. The solutions maximize the parallelization and throughput of file I/O. The frameworks are evaluated on the Titan supercomputing systems for three I/O interfaces. For example for large-scale application runs, POSIX I/O and MPI-IO can be improved by up to 50% on a per job basis, while HDF5 shows performance improvements of up to 32%

Space Transportation System and associated payloads: Glossary, acronyms, and abbreviations

A collection of acronyms now in everyday use in the Shuttle world are listed. It is a combination of lists that were prepared at the Kennedy and Johnson Space Centers and by the Air Force