[Show abstract][Hide abstract] ABSTRACT: The popularity of Big Data computing models like MapReduce has caused the emergence of many frameworks oriented to High Performance Computing (HPC) systems. The suitability of each one to a particular use case depends on its design and implementation, the underlying system resources and the type of application to be run. Therefore, the appropriate selection of one of these frameworks generally involves the execution of multiple experiments in order to assess their performance, scalability and resource efficiency. This work studies the main issues of this evaluation, proposing a new MapReduce Evaluator (MREv) tool which unifies the configuration of the frameworks, eases the task of collecting results and generates resource utilization statistics. Moreover, a practical use case is described, including examples of the experimental results provided by this tool. MREv is available to download at http://mrev.des.udc.es.
Full-text · Article · Dec 2015 · Procedia Computer Science
[Show abstract][Hide abstract] 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.
No preview · Article · Dec 2015 · Computers & Electrical Engineering
[Show abstract][Hide abstract] ABSTRACT: Development of new methods to detect pairwise epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important task in bioinformatics as they can help to explain genetic influences on diseases. As these studies are time consuming operations, some tools exploit the characteristics of different hardware accelerators (such as GPUs and Xeon Phi coprocessors) to reduce the runtime. Nevertheless, all these approaches are not able to efficiently exploit the whole computational capacity of modern clusters that contain both GPUs and Xeon Phi coprocessors. In this paper we investigate approaches to map pairwise epistasic detection on heterogeneous clusters using both types of accelerators. The runtimes to analyze the well-known WTCCC dataset consisting of about 500K SNPs and 5K samples on one and two NVIDIA K20m are reduced by 27% thanks to the use of a hybrid approach with one additional single Xeon Phi coprocessor.
Full-text · Article · Jul 2015 · IEEE Transactions on Parallel and Distributed Systems
[Show abstract][Hide abstract] ABSTRACT: On-chip power consumption is one of the fundamental challenges of current technology scaling. Cache memories consume a sizable part of this power, particularly due to leakage energy. STT-RAM is one of several new memory technologies that have been proposed in order to improve power while preserving performance. It features high density and low leakage, but at the expense of write energy and performance. This article explores the use of STT-RAM-based scratchpad memories that trade nonvolatility in exchange for faster and less energetically expensive accesses, making them feasible for on-chip implementation in embedded systems. A novel multiretention scratchpad partitioning is proposed, featuring multiple storage spaces with different retention, energy, and performance characteristics. A customized compiler-based allocation algorithm suitable for use with such a scratchpad organization is described. Our experiments indicate that a multiretention STT-RAM scratchpad can provide energy savings of 53% with respect to an iso-area, hardware-managed SRAM cache.
No preview · Article · Dec 2014 · ACM Transactions on Architecture and Code Optimization
[Show abstract][Hide abstract] ABSTRACT: The advent of cloud computing technologies, which dynamically provide on-demand access to computational resources over the
Internet, is offering new possibilities to many scientists and researchers. Nowadays, Infrastructure as a Service (IaaS) cloud
providers can offset the increasing processing requirements of data-intensive computing applications, becoming an emerging
alternative to traditional servers and clusters. In this paper, a comprehensive study of the leading public IaaS cloud platform,
Amazon EC2, has been conducted in order to assess its suitability for data-intensive computing. One of the key contributions
of this work is the analysis of the storage-optimized family of EC2 instances. Furthermore, this study presents a detailed
analysis of both performance and cost metrics. More specifically, multiple experiments have been carried out to analyze the
full I/O software stack, ranging from the low-level storage devices and cluster file systems up to real-world applications
using representative data-intensive parallel codes and MapReduce-based workloads. The analysis of the experimental results
has shown that data-intensive applications can benefit from tailored EC2-based virtual clusters, enabling users to obtain
the highest performance and cost-effectiveness in the cloud.
No preview · Article · Oct 2014 · The Computer Journal
[Show abstract][Hide abstract] ABSTRACT: This paper examines four different strategies, each one with its own data distribution, for im-plementing the parallel Conjugate Gradient (CG) method and how they impact communication and overall performance. Firstly, typical 1D and 2D distributions of the matrix involved in CG computations are considered. Then, a new 2D version of the CG method with asymmetric work-load, based on leaving some threads idle during part of the computation to reduce communication, is proposed. The four strategies are independent of sparse storage schemes and are implemented using Unified Parallel C (UPC), a Partitioned Global Address Space (PGAS) language. The strategies are evaluated on two different platforms through a set of matrices that exhibit distinct sparse patterns, demonstrating that our asymmetric proposal outperforms the others except for one matrix on one platform.
Full-text · Article · Sep 2014 · The Journal of Supercomputing
[Show abstract][Hide abstract] ABSTRACT: The performance and scalability of communications are key for HPC applications in current multi-core era. Despite the significant benefits (e.g., produc-tivity, multithreading) of Java for parallel program-ming, its poor communications support has hindered its adoption in HPC. This paper presents FastMPJ (http://fastmpj.com), an efficient Message-Passing in Java (MPJ) library, boosting Java for HPC by: (1) providing high performance shared memory commu-nications using threads; (2) taking full advantage of high-speed networks to provide low-latency and high bandwidth communications; (3) including a scalable collective library with topology aware primitives, au-tomatically selected at runtime; (4) avoiding Java data buffering overheads through zero-copy protocols; and (5) implementing the most widely extended MPI-like Java binding for a highly productive development. The performance evaluation on representative testbeds (InfiniBand, 10 Gigabit Ethernet, Myrinet, and shared memory systems) has shown that FastMPJ rivals MPI libraries, significantly improving the efficiency and scalability of communication-intensive Java HPC ap-plications.
[Show abstract][Hide abstract] ABSTRACT: The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing systems. Unfortunately, this heterogeneity is also exposed to the programmer complicating its exploitation. This paper presents a new technique to automat-ically rewrite sequential programs into a parallel counterpart targeting GPU-based heterogeneous systems. The original source code is analyzed through domain-independent computational kernels, which hide the complexity of the implementation details by presenting a non-statement-based, high-level, hierarchical representation of the application. Next, a locality-aware technique based on standard compiler transformations is applied to the original code through OpenHMPP directives. Two representative case studies from scientific applications have been selected: the three-dimensional discrete convolution and the simple-precision general matrix multiplication. The effectiveness of our technique is corroborated by a performance evaluation on NVIDIA GPUs.
[Show abstract][Hide abstract] ABSTRACT: This manuscript summarizes the main ideas introduced in . We propose a compiler that automatically transforms a sequential application into a parallel counterpart for multicore processors. It is based on an intermediate representation, named KIR, which exposes multiple levels of parallelism and hides the complexity of the implementation details thanks to the domain-independent kernels (e.g., assignment, reduction). The effectiveness and performance of our approach, built on top of GCC, has been tested with a large variety of codes.
[Show abstract][Hide abstract] ABSTRACT: With the evolution of high-performance computing, parallel applications have developed an increasing necessity for fault tolerance, most commonly provided by checkpoint and restart techniques. Checkpointing tools are typically implemented at one of two different abstraction levels: at the system level or at the application level. The latter has become an interesting alternative due to its flexibility and the possibility of operating in different environments. However, application-level checkpointing tools often require the user to manually insert checkpoints in order to ensure that certain requirements are met (e.g. forcing checkpoints to be taken at the user code and not inside kernel routines). This paper examines the transformations required to enable automatic checkpointing of parallel applications in the CPPC application-level checkpointing framework. These transformations have been implemented on two very different compiler infrastructures: Cetus and LLVM. Cetus is a Java-based compiler infrastructure aiming to provide an easy to use and clean IR and API for program transformation. LLVM is a low-level, SSA-based toolchain. The fundamental differences of both approaches are analyzed from the structural, behavioral and performance perspectives.
Full-text · Article · Dec 2013 · International Journal of Parallel Programming
[Show abstract][Hide abstract] ABSTRACT: Land abandonment and stagnation of rural markets in the last few years have become one of the main concerns of rural administrations. The use of Web and GIS (Geographic Information System) technologies can help to mitigate the effects of these problems. This paper pro-poses a novel Web-GIS tool with spatial capabilities for the dynamization of rural land markets by encouraging the transfer of land from owners to farmers through the leasing of plots. The system, based on open source software, offers information about the properties, their environment and their owners. It uses standards for handling the geographic information and for communicating with external data sources. This system was used as the basis for the development of SITEGAL, the tool for the management of the Land Bank of Galicia (www.bantegal.com/sitegal). SITEGAL has been operational since 2007 obtaining benefits for both administration and users (farmers and land owners), and promoting the e-Government.
No preview · Article · Dec 2013 · Earth Science Informatics
[Show abstract][Hide abstract] ABSTRACT: Cloud computing is posing several challenges, such as security, fault tolerance, access interface singularity, and network constraints, both in terms of latency and bandwidth. In this scenario, the performance of communications depends both on the network fabric and its efficient support in virtualized environments, which ultimately determines the overall system performance. To solve the current network constraints in cloud services, their providers are deploying high-speed networks, such as 10 Gigabit Ethernet. This paper presents an evaluation of high-performance computing message-passing middleware on a cloud computing infrastructure, Amazon EC2 cluster compute instances, equipped with 10 Gigabit Ethernet. The analysis of the experimental results, confronted with a similar testbed, has shown the significant impact that virtualized environments still have on communication performance, which demands more efficient communication middleware support to get over the current cloud network limitations.
Full-text · Article · Dec 2013 · Personal and Ubiquitous Computing
[Show abstract][Hide abstract] ABSTRACT: Cloud computing is currently being explored by the scientific community to assess its suitability for High Performance Computing (HPC) environments. In this novel paradigm, compute and storage resources, as well as applications, can be dynamically provisioned on a pay-per-use basis. This paper presents a thorough evaluation of the I/O storage subsystem using the Amazon EC2 Cluster Compute platform and the recent High I/O instance type, to determine its suitability for I/O-intensive applications. The evaluation has been carried out at different layers using representative benchmarks in order to evaluate the low-level cloud storage devices available in Amazon EC2, ephemeral disks and Elastic Block Store (EBS) volumes, both on local and distributed file systems. In addition, several I/O interfaces (POSIX, MPI-IO and HDF5) commonly used by scientific workloads have also been assessed. Furthermore, the scalability of a representative parallel I/O code has also been analyzed at the application level, taking into account both performance and cost metrics. The analysis of the experimental results has shown that available cloud storage devices can have different performance characteristics and usage constraints. Our comprehensive evaluation can help scientists to increase significantly (up to several times) the performance of I/O-intensive applications in Amazon EC2 cloud. An example of optimal configuration that can maximize I/O performance in this cloud is the use of a RAID 0 of 2 ephemeral disks, TCP with 9,000 bytes MTU, NFS async and MPI-IO on the High I/O instance type, which provides ephemeral disks backed by Solid State Drive (SSD) technology.
Full-text · Article · Dec 2013 · Journal of Grid Computing
[Show abstract][Hide abstract] ABSTRACT: Servet is a suite of benchmarks focused on extracting a set of parameters with high influence on the overall performance of multicore clusters. These parameters can be used to optimize the performance of parallel applications by adapting part of their behavior to the characteristics of the machine. Up to now the tool considered network bandwidth as constant and independent of the communication pattern. Nevertheless, the inter-node communication bandwidth decreases on modern large supercomputers depending on the number of cores per node that simultaneously access the network and on the distance between the communicating nodes. This paper describes two new benchmarks that improve Servet by characterizing the network performance degradation depending on these factors. This work also shows the experimental results of these benchmarks on a Cray XE6 supercomputer and some examples of how real parallel codes can be optimized by using the information about network degradation.
[Show abstract][Hide abstract] ABSTRACT: The simulation of particle dynamics is an essential method to analyze and predict the behavior of molecules in a given medium. This work presents the design and implementation of a parallel simulation of Brownian dynamics with hydrodynamic interactions for shared memory systems using two approaches: (1) OpenMP directives and (2) the Partitioned Global Address Space (PGAS) paradigm with the Unified Parallel C (UPC) language. The structure of the code is described, and different techniques for work distribution are analyzed in terms of efficiency, in order to select the most suitable strategy for each part of the simulation. Additionally, performance results have been collected from two representative NUMA systems, and they are studied and compared against the original sequential code.
No preview · Article · Sep 2013 · The Journal of Supercomputing