An Application-Based Performance Evaluation of NASAs Nebula Cloud Computing Platform

The high performance computing (HPC) community has shown tremendous interest in exploring cloud computing as it promises high potential. In this paper, we examine the feasibility, performance, and scalability of production quality scientific and engineering applications of interest to NASA on NASA's cloud computing platform, called Nebula, hosted at Ames Research Center. This work represents the comprehensive evaluation of Nebula using NUTTCP, HPCC, NPB, I/O, and MPI function benchmarks as well as four applications representative of the NASA HPC workload. Specifically, we compare Nebula performance on some of these benchmarks and applications to that of NASA s Pleiades supercomputer, a traditional HPC system. We also investigate the impact of virtIO and jumbo frames on interconnect performance. Overall results indicate that on Nebula (i) virtIO and jumbo frames improve network bandwidth by a factor of 5x, (ii) there is a significant virtualization layer overhead of about 10% to 25%, (iii) write performance is lower by a factor of 25x, (iv) latency for short MPI messages is very high, and (v) overall performance is 15% to 48% lower than that on Pleiades for NASA HPC applications. We also comment on the usability of the cloud platform

Electra: A Modular-Based Expansion of NASA's Supercomputing Capability

NASA has increasingly relied on high-performance computing (HPC) re- sources for computational modeling, simulation, and data analysis to meet the science and engineering goals of its missions in space exploration, aeronautics, and Earth and space science. The NASA Advanced Supercomputing (NAS) Division at Ames Research Center in Silicon Valley, Calif., hosts NASAs premier supercomputing resources, integral to achieving and enhancing the success of the agencys missions. NAS provides a balanced environment, funded under the High-End Computing Capability (HECC) project, comprised of world-class supercomputers, including its flagship distributed-memory cluster, Pleiades; high-speed networking; and massive data storage facilities, along with multi-disciplinary support teams for user support, code porting and optimization, and large-scale data analysis and scientific visualization. However, as scientists have increased the fidelity of their simulations and engineers are conducting larger parameter-space studies, the requirements for supercomputing resources have been growing by leaps and bounds. With the facility housing the HECC systems reaching its power and cooling capacity, NAS undertook a prototype project to investigate an alternative approach for housing supercomputers. Modular supercomputing, or container-based computing, is an innovative concept for expanding NASAs HPC capabilities. With modular supercomputing, additional containerssimilar to portable storage podscan be connected together as needed to accommodate the agencys ever-increasing demand for computing resources. In addition, taking advantage of the local weather permits the use of cooling technologies that would additionally save energy and reduce annual water usage. The first stage of NASAs Modular Supercomputing Facility (MSF) prototype, which resulted in a 1,000 square-foot module on a concrete pad with room for 16 compute racks, was completed in Fall 2016 and an SGI (now HPE) computer system, named Electra, was deployed there in early 2017. Cooling is performed via an evaporative system built into the module, and preliminary experience shows a Power Usage Effectiveness (PUE) measurement of 1.03. Electra achieved over a petaflop on the LINPACK benchmark, sufficient to rank number 96 on the November 2016 TOP500 list [14]. The system consists of 1,152 InfiniBand-connected Intel Xeon Broadwell-based nodes. Its users access their files on a facility-wide file system shared by all HECC compute assets via Mellanox MetroX InfiniBand extenders, which connect the Electra fabric to Lustre routers in the primary facility over fiber-optic links about 900 feet long. The MSF prototype has exceeded expectations and is serving as a blueprint for future expansions. In the remainder of this chapter, we detail how modular data center technology can be used to expand an existing compute resource. We begin by describing NASAs requirements for supercomputing and how resources were provided prior to the integration of the Electra module-based system

Evaluating the Suitability of Commercial Clouds for NASA's High Performance Computing Applications: A Trade Study

NASAs High-End Computing Capability (HECC) Project is periodically asked if it could be more cost effective through the use of commercial cloud resources. To answer the question, HECCs Application Performance and Productivity (APP) team undertook a performance and cost evaluation comparing three domains: two commercial cloud providers, Amazon and Penguin, and HECCs in-house resourcesthe Pleiades and Electra systems. In the study, the APP team used a combination of the NAS Parallel Benchmarks (NPB) and six full applications from NASAs workload on Pleiades and Electra to compare performance of nodes based on three different generations of Intel Xeon processorsHaswell, Broadwell, and Skylake. Because of export control limitations, the most heavily used applications on Pleiades and Electra could not be used in the cloud; therefore, only one of the applications, OpenFOAM, represents work from the Aeronautics Research Mission Directorate and the Human and Exploration Mission Directorate. The other five applications are from the Science Mission Directorate

Performance Projections of HPC Applications on Chip Multiprocessor (CMP) Based Systems

Performance projections of High Performance Computing (HPC) applications onto various hardware platforms are important for hardware vendors and HPC users. The projections aid hardware vendors in the design of future systems and help HPC users with system procurement and application refinements. In this dissertation, we present an efficient method to project the performance of HPC applications onto Chip Multiprocessor (CMP) based systems using widely available standard benchmark data. The main advantage of this method is the use of published data about the target machine; the target machine need not be available. With the current trend in HPC platforms shifting towards cluster systems with chip multiprocessors (CMPs), efficient and accurate performance projection becomes a challenging task. Typically, CMP-based systems are configured hierarchically, which significantly impacts the performance of HPC applications. The goal of this research is to develop an efficient method to project the performance of HPC applications onto systems that utilize CMPs. To provide for efficiency, our projection methodology is automated (projections are done using a tool) and fast (with small overhead). Our method, called the surrogate-based workload application projection method, utilizes surrogate benchmarks to project an HPC application performance on target systems where computation component of an HPC application is projected separately from the communication component. Our methodology was validated on a variety of systems utilizing different processor and interconnect architectures with high accuracy and efficiency. The average projection error on three target systems was 11.22 percent with standard deviation of 1.18 percent for twelve HPC workloads

The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States

Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures. Indeed, the human brain includes about 10^15 synapses and 10^11 neurons activated at a mean rate of several Hz. Full brain simulation poses Exascale challenges even if simulated at the highest abstraction level. The WaveScalES experiment in the Human Brain Project (HBP) has the goal of matching experimental measures and simulations of slow waves during deep-sleep and anesthesia and the transition to other brain states. The focus is the development of dedicated large-scale parallel/distributed simulation technologies. The ExaNeSt project designs an ARM-based, low-power HPC architecture scalable to million of cores, developing a dedicated scalable interconnect system, and SWA/AW simulations are included among the driving benchmarks. At the joint between both projects is the INFN proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine. DPSNN can be configured to stress either the networking or the computation features available on the execution platforms. The simulation stresses the networking component when the neural net - composed by a relatively low number of neurons, each one projecting thousands of synapses - is distributed over a large number of hardware cores. When growing the number of neurons per core, the computation starts to be the dominating component for short range connections. This paper reports about preliminary performance results obtained on an ARM-based HPC prototype developed in the framework of the ExaNeSt project. Furthermore, a comparison is given of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server platforms

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

Deploying and optimizing performance of a 3D hydrodynamic model on cloud

This papers presents details on deploying the Environmental Fluid Dynamics Code (EFDC) on a container-based cloud environment. Results are compared to a bare metal deployment. Application-specific benchmarking tests are complemented by detailed network tests that evaluate isolated MPI communication protocols both at intra-node and inter-node level with varying degrees of self-contention. Cloud-based simulations report significant performance loss in mean run-times. A containerised environment increases simulation time by up to 50%. More detailed analysis demonstrates that much of this performance penalty is a result of large variance in MPI communciation times. This manifests as simulation runtime variance on container cloud that hinders both simulation run-time and collection of well-defined quality-of-service metrics

Analysis of I/O Performance on an Amazon EC2 Cluster Compute and High I/O Platform

“This is a post-peer-review, pre-copyedit version of an article published in Journal of Grid Computing. The final authenticated version is available online at: https://doi.org/10.1007/s10723-013-9250-y[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.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación; AP2010-4348Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ref. 2010/