Accelerating binary biclustering on platforms with CUDA-enabled GPUs

© 2018 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Information Sciences. The Version of Record is available online at https://doi.org/10.1016/j.ins.2018.05.025This is a version of: J. González-Domínguez and R. R. Expósito, "Accelerating binary biclustering on platforms with CUDA-enabled GPUs", Information Sciences, Vol. 496, Sept. 2019, pp. 317-325, https://doi.org/10.1016/j.ins.2018.05.025[Abstract]: Data mining is nowadays essential in many scientific fields to extract valuable information from large input datasets and transform it into an understandable structure. For instance, biclustering techniques are very useful in identifying subsets of two-dimensional data where both rows and columns are correlated. However, some biclustering techniques have become extremely time-consuming when processing very large datasets, which nowadays prevents their use in many areas of research and industry (such as bioinformatics) that have experienced an explosive growth on the amount of available data. In this work we present CUBiBit, a tool that accelerates the search for relevant biclusters on binary data by exploiting the computational capabilities of CUDA-enabled GPUs as well as the several CPU cores available in most current systems. The experimental evaluation has shown that CUBiBit is up to 116 times faster than the fastest state-of-the-art tool, BiBit, in a system with two Intel Sandy Bridge processors (16 CPU cores) and three NVIDIA K20 GPUs. CUBiBit is publicly available to download from https://sourceforge.net/projects/cubibitThis work was supported by the Ministry of Economy, Industry and Competitiveness of Spain and FEDER funds of the European Union [grant TIN2016-75845-P (AEI/FEDER/UE)], as well as by Xunta de Galicia (Centro Singular de Investigacion de Galicia accreditation 2016-2019, ref. EDG431G/01).Xunta de Galicia; EDG431G/0

ParBiBit: Parallel tool for binary biclustering on modern distributed-memory systems

[Abstract]: Biclustering techniques are gaining attention in the analysis of large-scale datasets as they identify two-dimensional submatrices where both rows and columns are correlated. In this work we present ParBiBit, a parallel tool to accelerate the search of interesting biclusters on binary datasets, which are very popular on different fields such as genetics, marketing or text mining. It is based on the state-of-the-art sequential Java tool BiBit, which has been proved accurate by several studies, especially on scenarios that result on many large biclusters. ParBiBit uses the same methodology as BiBit (grouping the binary information into patterns) and provides the same results. Nevertheless, our tool significantly improves performance thanks to an efficient implementation based on C++11 that includes support for threads and MPI processes in order to exploit the compute capabilities of modern distributed-memory systems, which provide several multicore CPU nodes interconnected through a network. Our performance evaluation with 18 representative input datasets on two different eight-node systems shows that our tool is significantly faster than the original BiBit. Source code in C++ and MPI running on Linux systems as well as a reference manual are available at https://sourceforge.net/projects/parbibit/.This work was supported by the Ministry of Economy, Industry and Competitiveness of Spain and FEDER funds of the European Union [grant TIN2016-75845-P (AEI/FEDER/UE)], as well as by Xunta de Galicia (Centro Singular de Investigacion de Galicia accreditation 2016-2019, ref. EDG431G/01).Xunta de Galicia; EDG431G/0

BDWatchdog: real-time monitoring and profiling of Big Data applications and frameworks

This is a post-peer-review, pre-copyedit version of an article published in Future Generation Computer Systems. The final authenticated version is available online at: https://doi.org/10.1016/j.future.2017.12.068[Abstract] Current Big Data applications are characterized by a heavy use of system resources (e.g., CPU, disk) generally distributed across a cluster. To effectively improve their performance there is a critical need for an accurate analysis of both Big Data workloads and frameworks. This means to fully understand how the system resources are being used in order to identify potential bottlenecks, from resource to code bottlenecks. This paper presents BDWatchdog, a novel framework that allows real-time and scalable analysis of Big Data applications by combining time series for resource monitorization and flame graphs for code profiling, focusing on the processes that make up the workload rather than the underlying instances on which they are executed. This shift from the traditional system-based monitorization to a process-based analysis is interesting for new paradigms such as software containers or serverless computing, where the focus is put on applications and not on instances. BDWatchdog has been evaluated on a Big Data cloud-based service deployed at the CESGA supercomputing center. The experimental results show that a process-based analysis allows for a more effective visualization and overall improves the understanding of Big Data workloads. BDWatchdog is publicly available at http://bdwatchdog.dec.udc.es.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-PMinsiterio de Educación; FPU15/0338

Real-time resource scaling platform for Big Data workloads on serverless environments

Versión final aceptada de: https://doi.org/10.1016/j.future.2019.11.037This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-ncnd/ 4.0/. This version of the article: Enes, J., Expósito, R. R., & Touriño, J. (2020). 'Real-time resource scaling platform for Big Data workloads on serverless environments', has been accepted for publication in.: Future Generation Computer Systems, 105, 361–379. The Version of Record is available online at: https://doi.org/10.1016/j.future.2019.11.037.The serverless execution paradigm is becoming an increasingly popular option when workloads are to be deployed in an abstracted way, more specifically, without specifying any infrastructure requirements. Currently, such workloads are typically comprised of small programs or even a series of single functions used as event triggers or to process a data stream. Other applications that may also fit on a serverless scenario are stateless services that may need to seamlessly scale in terms of resources, such as a web server. Although several commercial serverless services are available (e.g., Amazon Lambda), their use cases are mostly limited to the execution of functions or scripts that can be adapted to predefined templates or specifications. However, current research efforts point out that it is interesting for the serverless paradigm to evolve from single functions and support more flexible infrastructure units such as operating-system-level virtualization in the form of containers. In this paper we present a novel platform to automatically scale container resources in real time, while they are running, and without any need for reboots. This platform is evaluated using Big Data workloads, both batch and streaming, as representative examples of applications that could be initially regarded as unsuitable for the serverless paradigm considering the currently available services. The results show how our serverless platform can improve the CPU utilization by up to 77% with an execution time overhead of only 6%, while remaining scalable when using a 32-container cluster.This work was supported by the Ministry of Economy, Industry and Competitiveness of Spain and FEDER funds of the European Union (project TIN2016-75845-P, AEI/FEDER/EU), the FPU Program of the Ministry of Education, Spain (grant FPU15/03381) and by Xunta de Galicia, Spain (Centro Singular de Investigación de Galicia accreditation 2016–2019, ref. ED431G/01). We also gratefully acknowledge CESGA for providing access to the Big Data infrastructure, and also sincerely thank Dr. Javier López Cacheiro for his technical support to perform some of the experiments. Other experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several universities as well as other organizations.Xunta de Galicia; ED431G/0

Design and Evaluation of Low-Latency Communication Middleware on High Performance Computing Systems

[Resumen]El interés en Java para computación paralela está motivado por sus interesantes características, tales como su soporte multithread, portabilidad, facilidad de aprendizaje,alta productividad y el aumento significativo en su rendimiento omputacional. No obstante, las aplicaciones paralelas en Java carecen generalmente de mecanismos de comunicación eficientes, los cuales utilizan a menudo protocolos basados en sockets incapaces de obtener el máximo provecho de las redes de baja latencia, obstaculizando la adopción de Java en computación de altas prestaciones (High Per- formance Computing, HPC). Esta Tesis Doctoral presenta el diseño, implementación y evaluación de soluciones de comunicación en Java que superan esta limitación. En consecuencia, se desarrollaron múltiples dispositivos de comunicación a bajo nivel para paso de mensajes en Java (Message-Passing in Java, MPJ) que aprovechan al máximo el hardware de red subyacente mediante operaciones de acceso directo a memoria remota que proporcionan comunicaciones de baja latencia. También se incluye una biblioteca de paso de mensajes en Java totalmente funcional, FastMPJ, en la cual se integraron los dispositivos de comunicación. La evaluación experimental ha mostrado que las primitivas de comunicación de FastMPJ son competitivas en comparación con bibliotecas nativas, aumentando significativamente la escalabilidad de aplicaciones MPJ. Por otro lado, esta Tesis analiza el potencial de la computación en la nube (cloud computing) para HPC, donde el modelo de distribución de infraestructura como servicio (Infrastructure as a Service, IaaS) emerge como una alternativa viable a los sistemas HPC tradicionales. La evaluación del rendimiento de recursos cloud específicos para HPC del proveedor líder, Amazon EC2, ha puesto de manifiesto el impacto significativo que la virtualización impone en la red, impidiendo mover las aplicaciones intensivas en comunicaciones a la nube. La clave reside en un soporte de virtualización apropiado, como el acceso directo al hardware de red, junto con las directrices para la optimización del rendimiento sugeridas en esta Tesis.[Resumo]O interese en Java para computación paralela está motivado polas súas interesantes características, tales como o seu apoio multithread, portabilidade, facilidade de aprendizaxe, alta produtividade e o aumento signi cativo no seu rendemento computacional. No entanto, as aplicacións paralelas en Java carecen xeralmente de mecanismos de comunicación e cientes, os cales adoitan usar protocolos baseados en sockets que son incapaces de obter o máximo proveito das redes de baixa latencia, obstaculizando a adopción de Java na computación de altas prestacións (High Performance Computing, HPC). Esta Tese de Doutoramento presenta o deseño, implementaci ón e avaliación de solucións de comunicación en Java que superan esta limitación. En consecuencia, desenvolvéronse múltiples dispositivos de comunicación a baixo nivel para paso de mensaxes en Java (Message-Passing in Java, MPJ) que aproveitan ao máaximo o hardware de rede subxacente mediante operacións de acceso directo a memoria remota que proporcionan comunicacións de baixa latencia. Tamén se inclúe unha biblioteca de paso de mensaxes en Java totalmente funcional, FastMPJ, na cal foron integrados os dispositivos de comunicación. A avaliación experimental amosou que as primitivas de comunicación de FastMPJ son competitivas en comparación con bibliotecas nativas, aumentando signi cativamente a escalabilidade de aplicacións MPJ. Por outra banda, esta Tese analiza o potencial da computación na nube (cloud computing) para HPC, onde o modelo de distribución de infraestrutura como servizo (Infrastructure as a Service, IaaS) xorde como unha alternativa viable aos sistemas HPC tradicionais. A ampla avaliación do rendemento de recursos cloud específi cos para HPC do proveedor líder, Amazon EC2, puxo de manifesto o impacto signi ficativo que a virtualización impón na rede, impedindo mover as aplicacións intensivas en comunicacións á nube. A clave atópase no soporte de virtualización apropiado, como o acceso directo ao hardware de rede, xunto coas directrices para a optimización do rendemento suxeridas nesta Tese.[Abstract]The use of Java for parallel computing is becoming more promising owing to its appealing features, particularly its multithreading support, portability, easy-tolearn properties, high programming productivity and the noticeable improvement in its computational performance. However, parallel Java applications generally su er from inefficient communication middleware, most of which use socket-based protocols that are unable to take full advantage of high-speed networks, hindering the adoption of Java in the High Performance Computing (HPC) area. This PhD Thesis presents the design, development and evaluation of scalable Java communication solutions that overcome these constraints. Hence, we have implemented several lowlevel message-passing devices that fully exploit the underlying network hardware while taking advantage of Remote Direct Memory Access (RDMA) operations to provide low-latency communications. Moreover, we have developed a productionquality Java message-passing middleware, FastMPJ, in which the devices have been integrated seamlessly, thus allowing the productive development of Message-Passing in Java (MPJ) applications. The performance evaluation has shown that FastMPJ communication primitives are competitive with native message-passing libraries, improving signi cantly the scalability of MPJ applications. Furthermore, this Thesis has analyzed the potential of cloud computing towards spreading the outreach of HPC, where Infrastructure as a Service (IaaS) o erings have emerged as a feasible alternative to traditional HPC systems. Several cloud resources from the leading IaaS provider, Amazon EC2, which speci cally target HPC workloads, have been thoroughly assessed. The experimental results have shown the signi cant impact that virtualized environments still have on network performance, which hampers porting communication-intensive codes to the cloud. The key is the availability of the proper virtualization support, such as the direct access to the network hardware, along with the guidelines for performance optimization suggested in this Thesis

SMusket: Spark-based DNA error correction on distributed-memory systems

©2020 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Future Generation Computer Systems. The Version of Record is available online at https://doi.org/10.1016/j.future.2019.10.038This is the accepted version of: R. R. Expósito, J. González-Domínguez, and J. Touriño, "SMusket: Sparkbased DNA error correction on distributed-memory systems", Future Generation Computer Systems, vol. 111, pp. 698-713, 2020, https://doi.org/10.1016/j.future.2019.10.038[Abstract]: Next-Generation Sequencing (NGS) technologies have revolutionized genomics research over the last decade, bringing new opportunities for scientists to perform groundbreaking biological studies. Error correction in NGS datasets is considered an important preprocessing step in many workflows as sequencing errors can severely affect the quality of downstream analysis. Although current error correction approaches provide reasonably high accuracies, their computational cost can be still unacceptable when processing large datasets. In this paper we propose SparkMusket (SMusket), a Big Data tool built upon the open-source Apache Spark cluster computing framework to boost the performance of Musket, one of the most widely adopted and top-performing multithreaded correctors. Our tool efficiently exploits Spark features to implement a scalable error correction algorithm intended for distributed-memory systems built using commodity hardware. The experimental evaluation on a 16-node cluster using four publicly available datasets has shown that SMusket is up to 15.3 times faster than previous state-of-the-art MPI-based tools, also providing a maximum speedup of 29.8 over its multithreaded counterpart. SMusket is publicly available under an open-source license at https://github.com/rreye/smusketThis work was supported by the Ministry of Economy, Industry and Competitiveness of Spain and FEDER, Spain funds of the European Union (project TIN2016-75845-P, AEI/FEDER/EU); and by Xunta de Galicia, Spain (projects ED431G/01 and ED431C 2017/04).Xunta de galicia; ED431G/01Xunta de Galicia; ED431C 2017/0

Performance Optimization of a Parallel Error Correction Tool

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] Due to the continuous development in the field of Next Generation Sequencing (NGS) technologies that have allowed researchers to take advantage of greater genetic samples in less time, it is a matter of relevance to improve the existing algorithms aimed at the enhancement of the quality of those generated reads. In this work, we present a Big Data tool implemented upon the open-source Apache Spark framework that is able to execute validated error-correction algorithms at an improved performance. The experimental evaluation conducted on a multi-core cluster has shown significant improvements in execution times, providing a maximum speedup of 9.5 over existing error correction tools when processing an NGS dataset with 25 million reads.This research was funded by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00/AEI/10.13039/501100011033), and by Xunta de Galicia and FEDER funds of the European Union (Centro de Investigación de Galicia accreditation 2019-2022, ref. ED431G2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30)Xunta de Galicia; ED431G2019/01Xunta de Galicia; ED431C 2021/3

RGen: Data Generator for Benchmarking Big Data Workloads

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] This paper presents RGen, a parallel data generator for benchmarking Big Data workloads, which integrates existing features and new functionalities in a standalone tool. The main functionalities developed in this work were the generation of text and graphs that meet the characteristics defined by the 4 Vs of Big Data. On the one hand, the LDA model has been used for text generation, which extracts topics or themes covered in a series of documents. On the other hand, graph generation is based on the Kronecker model. The experimental evaluation carried out on a 16-node cluster has shown that RGen provides very good weak and strong scalability results. RGen is publicly available to download at https://github.com/rubenperez98/RGen, accessed on 30 September 2021.CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades (Grant ED431G 2019/01). This project was also supported by the “Consellería de Cultura, Educación e Ordenación Universitaria” via the Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49 and 2021/30).Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431C 2021/3

SparkEC: speeding up alignment-based DNA error correction tools

[Abstract]: In recent years, huge improvements have been made in the context of sequencing genomic data under what is called Next Generation Sequencing (NGS). However, the DNA reads generated by current NGS platforms are not free of errors, which can affect the quality of downstream analysis. Although error correction can be performed as a preprocessing step to overcome this issue, it usually requires long computational times to analyze those large datasets generated nowadays through NGS. Therefore, new software capable of scaling out on a cluster of nodes with high performance is of great importance. In this paper, we present SparkEC, a parallel tool capable of fixing those errors produced during the sequencing process. For this purpose, the algorithms proposed by the CloudEC tool, which is already proved to perform accurate corrections, have been analyzed and optimized to improve their performance by relying on the Apache Spark framework together with the introduction of other enhancements such as the usage of memory-efficient data structures and the avoidance of any input preprocessing. The experimental results have shown significant improvements in the computational times of SparkEC when compared to CloudEC for all the representative datasets and scenarios under evaluation, providing an average and maximum speedups of 4.9× and 11.9×, respectively, over its counterpart. As error correction can take excessive computational time, SparkEC provides a scalable solution for correcting large datasets. Due to its distributed implementation, SparkEC speed can increase with respect to the number of nodes in a cluster. Furthermore, the software is freely available under GPLv3 license and is compatible with different operating systems (Linux, Windows and macOS).Ministerio de Ciencia e Innovación; PID2019-104184RB-I00 / AEI / 10.13039 / 501100011033Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2021/30FEDER; ED431G 2019/01FEDER; ED431C 2021/3

Optimization of Real-World MapReduce Applications With Flame-MR: Practical Use Cases

[Abstract] Apache Hadoop is a widely used MapReduce framework for storing and processing large amounts of data. However, it presents some performance issues that hinder its utilization in many practical use cases. Although existing alternatives like Spark or Hama can outperform Hadoop, they require to rewrite the source code of the applications due to API incompatibilities. This paper studies the use of Flame-MR, an in-memory processing architecture for MapReduce applications, to improve the performance of real-world use cases in a transparent way while keeping application compatibility. Flame-MR adapts to the characteristics of the workloads, managing efficiently the use of custom data formats and iterative computations, while also reducing workload imbalance. The experimental evaluation, conducted in high performance clusters and the Microsoft Azure cloud, shows a clear outperformance of Flame-MR over Hadoop. In most cases, Flame-MR reduces the execution times by more than a half