Design of efficient Java communications for high performance computing

[Abstract] There is an increasing interest to adopt Java as the parallel programming language for the multi-core era. Although Java offers important advantages, such as built-in multithreading and networking support, productivity and portability, the lack of efficient communication middleware is an important drawback for its uptake in High Performance Computing (HPC). This PhD Thesis presents the design, implementation and evaluation of several solutions to improve this situation: (1) a high performance Java sockets implementation (JFS, Java Fast Sockets) on high-speed networks (e.g., Myrinet, InfiniBand) and shared memory (e.g., multi-core) machines; (2) a low-level messaging device, iodev, which efficiently overlaps communication and computation; and (3) a more scalable Java message-passing library, Fast MPJ (F-MPJ). Furthermore, new Java parallel benchmarks have been implemented and used for the performance evaluation of the developed middleware. The final and main conclusion is that the use of Java for HPC is feasible and even advisable when looking for productive development, provided that efficient communication middleware is made available, such as the projects presented in this Thesis.[Resumen] La tesis doctoral "Design of Efficient Java Communications for High Performance Computing" parte de la hipótesis inicial de que es posible desarrollar aplicaciones Java en computación de altas prestaciones, un ámbito en el que el rendimiento es crucial, siempre que esté disponible un middleware de comunicación eficiente. Así, se han diseñado, desarrollado y evaluado diferentes bibliotecas de comunicación en Java, desde el nivel de sockets al de paso de mensajes, obteniendo notables incrementos de eficiencia, confirmando que la hipótesis inicial es factible

F-MPJ: scalable Java message-passing communications on parallel systems

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-009-0270-0[Abstract] This paper presents F-MPJ (Fast MPJ), a scalable and efficient Message-Passing in Java (MPJ) communication middleware for parallel computing. The increasing interest in Java as the programming language of the multi-core era demands scalable performance on hybrid architectures (with both shared and distributed memory spaces). However, current Java communication middleware lacks efficient communication support. F-MPJ boosts this situation by: (1) providing efficient non-blocking communication, which allows communication overlapping and thus scalable performance; (2) taking advantage of shared memory systems and high-performance networks through the use of our high-performance Java sockets implementation (named JFS, Java Fast Sockets); (3) avoiding the use of communication buffers; and (4) optimizing MPJ collective primitives. Thus, F-MPJ significantly improves the scalability of current MPJ implementations. A performance evaluation on an InfiniBand multi-core cluster has shown that F-MPJ communication primitives outperform representative MPJ libraries up to 60 times. Furthermore, the use of F-MPJ in communication-intensive MPJ codes has increased their performance up to seven times.Ministerio de Educación y Ciencia; TIN2004-07797-C02Ministerio de Educación y Ciencia; TIN2007-67537-C03-2Xunta de Galicia; PGIDIT06PXIB105228P

High Performance Java Remote Method Invocation for Parallel Computing on Clusters

This is a post-peer-review, pre-copyedit version. The final authenticated version is available online at: http://dx.doi.org/10.1109/ISCC.2007.4381536[Abstract] This paper presents a more efficient Java remote method invocation (RMI) implementation for high-speed clusters. The use of Java for parallel programming on clusters is limited by the lack of efficient communication middleware and high-speed cluster interconnect support. This implementation overcomes these limitations through a more efficient Java RMI protocol based on several basic assumptions on clusters. Moreover, the use of a high performance sockets library provides with direct high-speed interconnect support. The performance evaluation of this middleware on a gigabit Ethernet (GbE) and a scalable coherent interface (SCI) cluster shows experimental evidence of throughput increase. Moreover, qualitative aspects of the solution such as transparency to the user, interoperability with other systems and no need of source code modification can augment the performance of existing parallel Java codes and boost the development of new high performance Java RMI applications.Ministerio de Education y Ciencia; TIN2004-07797-C02Xunta de Galicia; PGIDIT06PXIB105228PR

High-performance computing selection of models of DNA substitution for multicore clusters

[Abstract] This paper presents the high-performance computing (HPC) support of jModelTest2, the most popular bioinformatic tool for the statistical selection of models of DNA substitution. As this can demand vast computational resources, especially in terms of processing power, jModelTest2 implements three parallel algorithms for model selection: (1) a multithreaded implementation for shared memory architectures; (2) a message-passing implementation for distributed memory architectures, such as clusters; and (3) a hybrid shared/distributed memory implementation for clusters of multicore nodes, combining the workload distribution across cluster nodes with a multithreaded model optimization within each node. The main limitation of the shared and distributed versions is the workload imbalance that generally appears when using more than 32 cores, a direct consequence of the heterogeneity in the computational cost of the evaluated models. The hybrid shared/distributed memory version overcomes this issue reducing the workload imbalance through a thread-based decomposition of the most costly model optimization tasks. The performance evaluation of this HPC application on a 40-core shared memory system and on a 528-core cluster has shown high scalability, with speedups of the multithreaded version of up to 32, and up to 257 for the hybrid shared/distributed memory implementation. This can represent a reduction in the execution time of some analyses from 4 days down to barely 20 minutes. The implementation of the three parallel execution strategies of jModelTest2 presented in this paper are available under a GPL license at http://code.google.com/jmodeltest2.European Research Council; ERC-2007-Stg 203161-PHYGENOM to D.P.Ministerio de Ciencia y Educación; BFU2009-08611 to D.P.Ministerio de Ciencia y Educación; TIN2010-16735 to R.D

jmodeltest.org: selection of nucleotide substitution models on the cloud

This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Jose Manuel Santorum, Diego Darriba, Guillermo L. Taboada, David Posada; jmodeltest.org: selection of nucleotide substitution models on the cloud, Bioinformatics, Volume 30, Issue 9, 1 May 2014, Pages 1310–1311, https://doi.org/10.1093/bioinformatics/btu032[Abstract] The selection of models of nucleotide substitution is one of the major steps of modern phylogenetic analysis. Different tools exist to accomplish this task, among which jModelTest 2 (jMT2) is one of the most popular. Still, to deal with large DNA alignments with hundreds or thousands of loci, users of jMT2 need to have access to High Performance Computing clusters, including installation and configuration capabilities, conditions not always met. Here we present jmodeltest.org, a novel web server for the transparent execution of jMT2 across different platforms and for a wide range of users. Its main benefit is straightforward execution, avoiding any configuration/execution issues, and reducing significantly in most cases the time required to complete the analysis. Availability and implementation:jmodeltest.org is accessible using modern browsers, such as Firefox, Chrome, Opera, Safari and IE from http://jmodeltest.org. User registration is not mandatory, but users wanting to have additional functionalities, like access to previous analyses, have the possibility of opening a user account.European Research Council; 2007-Stg 203161- PHYGENOMMinisterio de Ciencia e Innovación; TIN2010-1673

Design and Implementation of MapReduce using the PGAS Programming Model with UPC

This is a post-peer-review, pre-copyedit version of an article published in International Conference on Parallel and Distributed Systems. Proceedings. The final authenticated version is available online at: http://dx.doi.org/10.1109/ICPADS.2011.162[Abstract] MapReduce is a powerful tool for processing large data sets used by many applications running in distributed environments. However, despite the increasing number of computationally intensive problems that require low-latency communications, the adoption of MapReduce in High Performance Computing (HPC) is still emerging. Here languages based on the Partitioned Global Address Space (PGAS) programming model have shown to be a good choice for implementing parallel applications, in order to take advantage of the increasing number of cores per node and the programmability benefits achieved by their global memory view, such as the transparent access to remote data. This paper presents the first PGAS-based MapReduce implementation that uses the Unified Parallel C (UPC) language, which (1) obtains programmability benefits in parallel programming, (2) offers advanced configuration options to define a customized load distribution for different codes, and (3) overcomes performance penalties and bottlenecks that have traditionally prevented the deployment of MapReduce applications in HPC. The performance evaluation of representative applications on shared and distributed memory environments assesses the scalability of the presented MapReduce framework, confirming its suitability.Ministerio de Ciencia e Innovación; TIN2010-1673

Parallel Brownian dynamics simulations with the message-passing and PGAS programming models

This is a post-peer-review, pre-copyedit version of an article published in Computer Physics Communications. The final authenticated version is available online at: https://doi.org/10.1016/j.cpc.2012.12.015[Abstract] The simulation of particle dynamics is among the most important mechanisms to study the behavior of molecules in a medium under specific conditions of temperature and density. Several models can be used to compute efficiently the forces that act on each particle, and also the interactions between them. This work presents the design and implementation of a parallel simulation code for the Brownian motion of particles in a fluid. Two different parallelization approaches have been followed: (1) using traditional distributed memory message-passing programming with MPI, and (2) using the Partitioned Global Address Space (PGAS) programming model, oriented towards hybrid shared/distributed memory systems, with the Unified Parallel C (UPC) language. Different techniques for domain decomposition and work distribution are analyzed in terms of efficiency and programmability, in order to select the most suitable strategy. Performance results on a supercomputer using up to 2048 cores are also presented for both MPI and UPC codes.Ministerio de Ciencia e Innovación ; TIN2010-16735Xunta de Galicia; ref. 2010/

Flame-MR: An event-driven architecture for MapReduce applications

[Abstract] Nowadays, many organizations analyze their data with the MapReduce paradigm, most of them using the popular Apache Hadoop framework. As the data size managed by MapReduce applications is steadily increasing, the need for improving the Hadoop performance also grows. Existing modifications of Hadoop (e.g., Mellanox Unstructured Data Accelerator) attempt to improve performance by changing some of its underlying subsystems. However, they are not always capable to cope with all its performance bottlenecks or they hinder its portability. Furthermore, new frameworks like Apache Spark or DataMPI can achieve good performance improvements, but they do not keep compatibility with existing MapReduce applications. This paper proposes Flame-MR, a new event-driven MapReduce architecture that increases Hadoop performance by avoiding memory copies and pipelining data movements, without modifying the source code of the applications. The performance evaluation on two representative systems (an HPC cluster and a public cloud platform) has shown experimental evidence of significant performance increases, reducing the execution time by up to 54% on the Amazon EC2 cloud.Ministerio de Economía y Competititvidad; TIN2013-42148-PMinisterio de Educación; FPU14/0280

Design of efficient Java message-passing collectives on multi-core clusters

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-010-0464-5[Abstract] This paper presents a scalable and efficient Message-Passing in Java (MPJ) collective communication library for parallel computing on multi-core architectures. The continuous increase in the number of cores per processor underscores the need for scalable parallel solutions. Moreover, current system deployments are usually multi-core clusters, a hybrid shared/distributed memory architecture which increases the complexity of communication protocols. Here, Java represents an attractive choice for the development of communication middleware for these systems, as it provides built-in networking and multithreading support. As the gap between Java and compiled languages performance has been narrowing for the last years, Java is an emerging option for High Performance Computing (HPC). Our MPJ collective communication library increases Java HPC applications performance on multi-core clusters: (1) providing multi-core aware collective primitives; (2) implementing several algorithms (up to six) per collective operation, whereas publicly available MPJ libraries are usually restricted to one algorithm; (3) analyzing the efficiency of thread-based collective operations; (4) selecting at runtime the most efficient algorithm depending on the specific multi-core system architecture, and the number of cores and message length involved in the collective operation; (5) supporting the automatic performance tuning of the collectives depending on the system and communication parameters; and (6) allowing its integration in any MPJ implementation as it is based on MPJ point-to-point primitives. A performance evaluation on an InfiniBand and Gigabit Ethernet multi-core cluster has shown that the implemented collectives significantly outperform the original ones, as well as higher speedups when analyzing the impact of their use on collective communications intensive Java HPC applications. Finally, the presented library has been successfully integrated in MPJ Express (http://mpj-express.org), and will be distributed with the next release.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación; FPU; AP2009-2112Xunta de Galicia; PGIDIT06PXIB105228P

Analysis and evaluation of MapReduce solutions on an HPC cluster

This is a post-peer-review, pre-copyedit version of an article published in Computers & Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1016/j.compeleceng.2015.11.021[Abstract] The ever growing needs of Big Data applications are demanding challenging capabilities which cannot be handled easily by traditional systems, and thus more and more organizations are adopting High Performance Computing (HPC) to improve scalability and efficiency. Moreover, Big Data frameworks like Hadoop need to be adapted to leverage the available resources in HPC environments. This situation has caused the emergence of several HPC-oriented MapReduce frameworks, which benefit from different technologies traditionally oriented to supercomputing, such as high-performance interconnects or the message-passing interface. This work aims to establish a taxonomy of these frameworks together with a thorough evaluation, which has been carried out in terms of performance and energy efficiency metrics. Furthermore, the adaptability to emerging disks technologies, such as solid state drives, has been assessed. The results have shown that new frameworks like DataMPI can outperform Hadoop, although using IP over InfiniBand also provides significant benefits without code modifications.Ministerio de Economía y Competitividad; TIN2013-42148-