A distributed-memory package for dense Hierarchically Semi-Separable matrix computations using randomization

We present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable representations (HSS). Such matrices appear in many applications, e.g., finite element methods, boundary element methods, etc. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, relies on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. This work is part of a more global effort, the STRUMPACK (STRUctured Matrices PACKage) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver

Development of Grid e-Infrastructure in South-Eastern Europe

Over the period of 6 years and three phases, the SEE-GRID programme has established a strong regional human network in the area of distributed scientific computing and has set up a powerful regional Grid infrastructure. It attracted a number of user communities and applications from diverse fields from countries throughout the South-Eastern Europe. From the infrastructure point view, the first project phase has established a pilot Grid infrastructure with more than 20 resource centers in 11 countries. During the subsequent two phases of the project, the infrastructure has grown to currently 55 resource centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16 participating countries. Inclusion of new resource centers to the existing infrastructure, as well as a support to new user communities, has demanded setup of regionally distributed core services, development of new monitoring and operational tools, and close collaboration of all partner institution in managing such a complex infrastructure. In this paper we give an overview of the development and current status of SEE-GRID regional infrastructure and describe its transition to the NGI-based Grid model in EGI, with the strong SEE regional collaboration.Comment: 22 pages, 12 figures, 4 table

A grid-enabled problem solving environment for parallel computational engineering design

This paper describes the development and application of a piece of engineering software that provides a problem solving environment (PSE) capable of launching, and interfacing with, computational jobs executing on remote resources on a computational grid. In particular it is demonstrated how a complex, serial, engineering optimisation code may be efficiently parallelised, grid-enabled and embedded within a PSE. The environment is highly flexible, allowing remote users from different sites to collaborate, and permitting computational tasks to be executed in parallel across multiple grid resources, each of which may be a parallel architecture. A full working prototype has been built and successfully applied to a computationally demanding engineering optimisation problem. This particular problem stems from elastohydrodynamic lubrication and involves optimising the computational model for a lubricant based on the match between simulation results and experimentally observed data

A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters

In this work, we consider the solution of boundary integral equations by means of a scalable hierarchical matrix approach on clusters equipped with graphics hardware, i.e. graphics processing units (GPUs). To this end, we extend our existing single-GPU hierarchical matrix library hmglib such that it is able to scale on many GPUs and such that it can be coupled to arbitrary application codes. Using a model GPU implementation of a boundary element method (BEM) solver, we are able to achieve more than 67 percent relative parallel speed-up going from 128 to 1024 GPUs for a model geometry test case with 1.5 million unknowns and a real-world geometry test case with almost 1.2 million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6 minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the setup phase and 20 seconds for the iterative solver. To the best of the authors' knowledge, we here discuss the first fully GPU-based distributed-memory parallel hierarchical matrix Open Source library using the traditional H-matrix format and adaptive cross approximation with an application to BEM problems

UPC++: A high-performance communication framework for asynchronous computation

UPC++ is a C++ library that supports high-performance computation via an asynchronous communication framework. This paper describes a new incarnation that differs substantially from its predecessor, and we discuss the reasons for our design decisions. We present new design features, including future-based asynchrony management, distributed objects, and generalized Remote Procedure Call (RPC). We show microbenchmark performance results demonstrating that one-sided Remote Memory Access (RMA) in UPC++ is competitive with MPI-3 RMA; on a Cray XC40 UPC++ delivers up to a 25% improvement in the latency of blocking RMA put, and up to a 33% bandwidth improvement in an RMA throughput test. We showcase the benefits of UPC++ with irregular applications through a pair of application motifs, a distributed hash table and a sparse solver component. Our distributed hash table in UPC++ delivers near-linear weak scaling up to 34816 cores of a Cray XC40. Our UPC++ implementation of the sparse solver component shows robust strong scaling up to 2048 cores, where it outperforms variants communicating using MPI by up to 3.1x. UPC++ encourages the use of aggressive asynchrony in low-overhead RMA and RPC, improving programmer productivity and delivering high performance in irregular applications

DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge

The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for processing large astronomical datasets at a scale required by the Square Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex data reduction pipelines consisting of both data sets and algorithmic components and an implementation run-time to execute such pipelines on distributed resources. By mapping the logical view of a pipeline to its physical realisation, DALiuGE separates the concerns of multiple stakeholders, allowing them to collectively optimise large-scale data processing solutions in a coherent manner. The execution in DALiuGE is data-activated, where each individual data item autonomously triggers the processing on itself. Such decentralisation also makes the execution framework very scalable and flexible, supporting pipeline sizes ranging from less than ten tasks running on a laptop to tens of millions of concurrent tasks on the second fastest supercomputer in the world. DALiuGE has been used in production for reducing interferometry data sets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide Spectral Radioheliograph; and is being developed as the execution framework prototype for the Science Data Processor (SDP) consortium of the Square Kilometre Array (SKA) telescope. This paper presents a technical overview of DALiuGE and discusses case studies from the CHILES and MUSER projects that use DALiuGE to execute production pipelines. In a companion paper, we provide in-depth analysis of DALiuGE's scalability to very large numbers of tasks on two supercomputing facilities.Comment: 31 pages, 12 figures, currently under review by Astronomy and Computin

Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of computing resources. The pressure to maintain reasonable levels of performance and portability forces application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical manycore architectures. In this paper, we study the benefits and limits of replacing the highly specialized internal scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them the opportunity to process and optimize its traversal in order to maximize the algorithm efficiency for the targeted hardware platform. A comparative study of the performance of the PaStiX solver on top of its native internal scheduler, PaRSEC, and StarPU frameworks, on different execution environments, is performed. The analysis highlights that these generic task-based runtimes achieve comparable results to the application-optimized embedded scheduler on homogeneous platforms. Furthermore, they are able to significantly speed up the solver on heterogeneous environments by taking advantage of the accelerators while hiding the complexity of their efficient manipulation from the programmer.Comment: Heterogeneity in Computing Workshop (2014

H-RADIC: The Fault Tolerance Framework for Virtual Clusters on Multi-Cloud Environments

Even though the cloud platform promises to be reliable, several availability incidents prove that they are not. How can we be sure that a parallel application finishes the execution even if a site is affected by a failure? This paper presents H-RADIC, an approach based on RADIC architecture, that executes a parallel application in at least 3 different virtual clusters or sites. The execution state of each site is saved periodically in another site and it is recovered in case of failure. The paper details the configuration of the architecture and the experiments results using 3 virtual clusters running NAS parallel applications protected with DMTCP, a very well-known distributed multi-threaded checkpoint tool. Our experiments show that the execution time was increased between a 5% to 36% without failures and 27% to 66% in case of failures.Facultad de Informátic

H-RADIC: una solución de tolerancia a fallos para clústeres virtuales en ambientes multi-nube

Even though the cloud platform promises to be reliable, several availability incidents prove that it is not. How can we be sure that a parallel application finishes it´s execution even if a site is affected by a failure? This paper presents H-RADIC, an approach based on RADIC architecture, that executes parallel applications protected by RADIC in at least 3 different virtual clusters or sites. The execution state of each site is saved periodically in another site and it is recovered in case of failure. The paper details the configuration of the architecture and the experiment´s results using 3 clusters running NAS parallel applications protected with DMTCP, a very well-known distributed multi-threaded checkpoint tool. Our experiments show that by adding a cluster protector it will be possible to implement the next level in the hierarchy, where the first level in the RADIC hierarchy works as an observer at a site level. In adition, the experiments showed that the protection implementation is out of the critical path of the application and it depends on the utilized resources.Aunque las plataformas en la nube parecen ser muy confiables, varios incidentes de disponibilidad han podemos asegurarnos que una aplicación paralela termina su ejecución cuando el sitio en la nube ha sido afectado por una falla? Este articulo presenta HRADIC, un enfoque basado en la arquitectura RADIC, esta ejecuta aplicaciones paralelas en al menos 3 diferentes sitios o clústeres virtuales, todos protegidos por RADIC, donde el estado de la ejecución de cada sitio es guardado periódicamente en otro de los sitios y de ahí es recuperado en el caso de una falla. El articulo detalla la configuración de la arquitectura y los resultados de los experimentos usando 3 clústeres ejecutando aplicaciones NAS en paralelo, protegidas con DMTCP (una herramienta para realizar múltiples checkpoints). Nuestros experimentos muestran que al agregar un protector del clúster es posible implementar un nivel más en la jerarquía de RADIC, donde el primer nivel funciona como observador. Los experimentos muestran que la implementación de este protector esta fuera del camino critico de la ampliación y depende solamente de la utilización de recursos.Facultad de Informátic

H-RADIC: una solución de tolerancia a fallos para clústeres virtuales en ambientes multi-nube

Even though the cloud platform promises to be reliable, several availability incidents prove that it is not. How can we be sure that a parallel application finishes it´s execution even if a site is affected by a failure? This paper presents H-RADIC, an approach based on RADIC architecture, that executes parallel applications protected by RADIC in at least 3 different virtual clusters or sites. The execution state of each site is saved periodically in another site and it is recovered in case of failure. The paper details the configuration of the architecture and the experiment´s results using 3 clusters running NAS parallel applications protected with DMTCP, a very well-known distributed multi-threaded checkpoint tool. Our experiments show that by adding a cluster protector it will be possible to implement the next level in the hierarchy, where the first level in the RADIC hierarchy works as an observer at a site level. In adition, the experiments showed that the protection implementation is out of the critical path of the application and it depends on the utilized resources.Aunque las plataformas en la nube parecen ser muy confiables, varios incidentes de disponibilidad han podemos asegurarnos que una aplicación paralela termina su ejecución cuando el sitio en la nube ha sido afectado por una falla? Este articulo presenta HRADIC, un enfoque basado en la arquitectura RADIC, esta ejecuta aplicaciones paralelas en al menos 3 diferentes sitios o clústeres virtuales, todos protegidos por RADIC, donde el estado de la ejecución de cada sitio es guardado periódicamente en otro de los sitios y de ahí es recuperado en el caso de una falla. El articulo detalla la configuración de la arquitectura y los resultados de los experimentos usando 3 clústeres ejecutando aplicaciones NAS en paralelo, protegidas con DMTCP (una herramienta para realizar múltiples checkpoints). Nuestros experimentos muestran que al agregar un protector del clúster es posible implementar un nivel más en la jerarquía de RADIC, donde el primer nivel funciona como observador. Los experimentos muestran que la implementación de este protector esta fuera del camino critico de la ampliación y depende solamente de la utilización de recursos.Facultad de Informátic