Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Big Data Analytics on Traditional HPC Infrastructure Using Two-Level Storage

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.Comment: Submitted to SC15, 8 pages, 7 figures, 3 table

A methodology for Spark parameter tuning

Spark has been established as an attractive platform for big data analysis, since it manages to hide most of the complexities related to parallelism, fault tolerance and cluster setting from developers. However, this comes at the expense of having over 150 configurable parameters, the impact of which cannot be exhaustively examined due to the exponential amount of their combinations. The default values allow developers to quickly deploy their applications but leave the question as to whether performance can be improved open. In this work, we investigate the impact of the most important tunable Spark parameters with regards to shuffling, compression and serialization on the application performance through extensive experimentation using the Spark-enabled Marenostrum III (MN3) computing infrastructure of the Barcelona Supercomputing Center. The overarching aim is to guide developers on how to proceed to changes to the default values. We build upon our previous work, where we mapped our experience to a trial-and-error iterative improvement methodology for tuning parameters in arbitrary applications based on evidence from a very small number of experimental runs. The main contribution of this work is that we propose an alternative systematic methodology for parameter tuning, which can be easily applied onto any computing infrastructure and is shown to yield comparable if not better results than the initial one when applied to MN3; observed speedups in our validating test case studies start from 20%. In addition, the new methodology can rely on runs using samples instead of runs on the complete datasets, which render it significantly more practical.Peer ReviewedPostprint (author's final draft

Distributed training of deep neural networks with spark: The MareNostrum experience

Deployment of a distributed deep learning technology stack on a large parallel system is a very complex process, involving the integration and configuration of several layers of both, general-purpose and custom software. The details of such kind of deployments are rarely described in the literature. This paper presents the experiences observed during the deployment of a technology stack to enable deep learning workloads on MareNostrum, a petascale supercomputer. The components of a layered architecture, based on the usage of Apache Spark, are described and the performance and scalability of the resulting system is evaluated. This is followed by a discussion about the impact of different configurations including parallelism, storage and networking alternatives, and other aspects related to the execution of deep learning workloads on a traditional HPC setup. The derived conclusions should be useful to guide similarly complex deployments in the future.Peer ReviewedPostprint (author's final draft

Optimizing Data-Intensive Computing with Efficient Configuration Tuning

As the complexity of distributed analytics systems evolves over time, more configuration parameters get exposed for tuning. While these numerous parameters allow users more control over how their workloads are executed, this flexibility comes at a cost, since finding the right configurations for such systems in a cost-effective way becomes challenging. In practice, several factors contribute to the complexity of tuning the configuration of those systems: the large configuration space, the diversity of the served workloads (each workload possibly requiring a different resource allocation strategy to run optimally), and the dynamic characteristics of these systems’ environment (e.g., increase in input data size, changes in the allocation of resources). Paradoxically, existing solutions for workload tuning either assume static tuning environment or workloads that are inexpensive to run (i.e. requiring hundreds of execution samples). Recently, Bayesian Optimisation (BO) strategies have been applied as a solution to enable efficient autotuning. They build a probabilistic model incrementally to predict the impact of the parameters on performance using a small number of execution samples. The incrementally constructed BO model is used to guide the tuning process and accelerate convergence to a near-optimal configuration. Unfortunately, for distributed analytics systems, the configuration space is too large to construct a good model using traditional BO, which fails to provide quick convergence in high dimensional configuration space. I argue that cost-effective tuning strategies can only be developed when taking into account: the frequent changes that can happen in the analytics workload/environment, the amortization of tuning costs and how this influences tuning profitability, the high dimensionality of configuration space and the need to cater for diverse workloads. To tackle these challenges, I propose Tuneful, an efficient configuration tuning framework for such expensive to tune systems. It works efficiently both initially (when little data is available) as well as later (as more tuning knowledge is acquired). It starts with learning workload-specific influential parameters incrementally and tunes those only, then when more tuning knowledge becomes available, it detects similarity across workloads and utilizes multitask BO to share the tuning knowledge across similar workloads. I show how augmenting the BO approach with parameters’ significance and workload similarity characteristics enables an efficient configuration tuning in high dimensional configuration space. Over diverse analytics workloads, this significantly accelerates both configuration tuning and cost amortization, saving search time by 2.7-3.7X at median compared to the-state-of-the-art approaches

Applying big data paradigms to a large scale scientific workflow: lessons learned and future directions

The increasing amounts of data related to the execution of scientific workflows has raised awareness of their shift towards parallel data-intensive problems. In this paper, we deliver our experience combining the traditional high-performance computing and grid-based approaches with Big Data analytics paradigms, in the context of scientific ensemble workflows. Our goal was to assess and discuss the suitability of such data-oriented mechanisms for production-ready workflows, especially in terms of scalability. We focused on two key elements in the Big Data ecosystem: the data-centric programming model, and the underlying infrastructure that integrates storage and computation in each node. We experimented with a representative MPI-based iterative workflow from the hydrology domain, EnKF-HGS, which we re-implemented using the Spark data analysis framework. We conducted experiments on a local cluster, a private cloud running OpenNebula, and the Amazon Elastic Compute Cloud (AmazonEC2). The results we obtained were analysed to synthesize the lessons we learned from this experience, while discussing promising directions for further research.This work was supported by the Spanish Ministry of Economics and Competitiveness grant TIN-2013-41350-P, the IC1305 COST Action “Network for Sustainable Ultrascale Computing Platforms” (NESUS), and the FPU Training Program for Academic and Teaching Staff Grant FPU15/00422 by the Spanish Ministry of Education