Large-scale Computer Systems Research Articles

Privacy Enhanced Data Security Mechanism in a Large-Scale Distributed Computing System for HTC and MTC

We developed a pilot-job based large-scale distributed computing system to support HTC and MTC, called HTCaaS (High-Throughput Computing as a Service), which helps scientists solve large-scale scientific problems in areas such as pharmaceutical domains, high-energy physics, nuclear physics and bio science. Since most of these problems involve critical data that affect the national economy and activate basic industries, data privacy is a very important issue. In this paper, we implement a privacy enhanced data security mechanism to support HTC and MTC in a large-scale distributed computing system and show how this technique affects performance in our system. With this mechanism, users can securely store data in our system.

Guest Editors’ Introduction: Special Section on Emerging Memory Technologies in Very Large Scale Computing and Storage Systems

The eleven papers in this special section focus on both system- and circuit-level aspects of emerging memory and storage technologies. The overwhelmingly increasing demand for both storage and computation necessitates revisiting the traditional memory subsystems used in processors and storage systems to take advantage of emerging memory technologies. Dynamic Random Access Memory (DRAM) and Static Random Access Memory (SRAM) have been ubiquitously used for decades as main memory and as on-chip cache, respectively. Scaling and power issues of these traditional memory technologies have led to significant investment in emerging memory technologies. On the other hand, fundamental limitations of mechanical disk drives have brought great attention to further explore the design space of solid-state drives (SSDs). The promising features of emerging memory technologies such as low power consumption, increased performance, lower susceptibility to particle strikes, and higher bit density have prompted researchers to seek new organizations in the different memory-hierarchy levels, to propose new circuitry and algorithms to improve performance and reduce power consumption, and to develop new schemes to enhance system reliability.

Scalable Energy Efficiency with Resilience for High Performance Computing Systems

Ever-growing performance of supercomputers nowadays brings demanding requirements of energy efficiency and resilience, due to rapidly expanding size and duration in use of the large-scale computing systems. Many application/architecture-dependent parameters that determine energy efficiency and resilience individually have causal effects with each other, which directly affect the trade-offs among performance, energy efficiency and resilience at scale. To enable high-efficiency management for large-scale High-Performance Computing (HPC) systems nowadays, quantitatively understanding the entangled effects among performance, energy efficiency, and resilience is thus required. While previous work focuses on exploring energy-saving and resilience-enhancing opportunities separately, little has been done to theoretically and empirically investigate the interplay between energy efficiency and resilience at scale. In this article, by extending the Amdahl’s Law and the Karp-Flatt Metric, taking resilience into consideration, we quantitatively model the integrated energy efficiency in terms of performance per Watt and showcase the trade-offs among typical HPC parameters, such as number of cores, frequency/voltage, and failure rates. Experimental results for a wide spectrum of HPC benchmarks on two HPC systems show that the proposed models are accurate in extrapolating resilience-aware performance and energy efficiency, and capable of capturing the interplay among various energy-saving and resilience factors. Moreover, the models can help find the optimal HPC configuration for the highest integrated energy efficiency, in the presence of failures and applied resilience techniques.

Fernando Corbató: Time-Sharing Pioneer, Part 1

Fernando Corbato is best known his work developing time-sharing operating systems. Early in his career, Corbato had a key role in the development of both the Compatible Time-Sharing System (CTSS) and Multics at the Massachusetts Institute of Technology in the 1960s. In 1990, Corbato received the Turing Award for his pioneering work in organizing the concepts and leading the development of the general-purpose, large-scale, time-sharing and resource-sharing computer systems, and he was made a fellow of the Computer History Museum (CHM) in 2012. This interview is the first in a two-part Annals series based on an oral history conducted by Steven Webber the CHM in February 2006.

Описание тестирования памяти однокристальных систем на основе ARM

Мощность вычислений традиционно находится в фокусе при разработке крупномасштабных вычислительных систем, в большинстве случаев такие проекты остаются плохо оборудованными и не могут эффективно справляться с ориентированными на высокую производительность рабочими нагрузками. Кроме того, стоимость и вопросы энергопотребления для крупномасштабных вычислительных систем всё ещё остаются источником беспокойства. Потенциальное решение включает в себя использование низко затратных процессоров ARM с маленькой мощностью в больших массивах в манере, которая обеспечивает массивное распараллеливание и высокую пропускную способность, производительность (относительно существующих крупномасштабных вычислительных проектов). Предоставление большего приоритета производительности и стоимости повышает значимость производительности оперативной памяти и оптимизации проекта до высокой производительности всей системы. Используя несколько эталонных тестов производительности оперативной памяти для оценки различных аспектов производительности RAM и кэш-памяти, мы даем описание производительности четырех различных моделей однокристальной системы на основе ARM, а именно Cortex-A9, Cortex-A7, Cortex-A15 r3p2 и Cortex-A15 r3p3. Затем мы обсуждаем значимость этих результатов для вычислений большого объема и потенциала для ARM- процессоров.

Implementation of a solution Cloud Computing with MapReduce model

In recent years, large scale computer systems have emerged to meet the demands of high storage, supercomputing, and applications using very large data sets. The emergence of Cloud Computing offers the potentiel for analysis and processing of large data sets.Mapreduce is the most popular programming model which is used to support the development of such applications. It was initially designed by Google for building large datacenters on a large scale, to provide Web search services with rapid response and high availability.In this paper we will test the clustering algorithm K-means Clustering in a Cloud Computing. This algorithm is implemented on MapReduce. It has been chosen for its characteristics that are representative of many iterative data analysis algorithms. Then, we modify the framework CloudSim to simulate the MapReduce execution of K-means Clustering on different Cloud Computing, depending on their size and characteristics of target platforms.The experiment show that the implementation of K-means Clustering gives good results especially for large data set and the Cloud infrastructure has an influence on these results.

Improved parallel performance of the CICE model in CESM1

The Los Alamos sea ice model, CICE, is a sophisticated finite difference grid point model. It has been a part of the National Science Foundation and Department of Energy community climate models (Community Climate System Model (CCSM) and Community Earth System Model (CESM)) for over a decade. It includes various physical and dynamical processes and is parallelized to run on large-scale computer systems. The CICE model was assessed in the CESM at different resolutions and target processor counts to better understand and optimize the performance. Several new decompositions and a new feature to reduce the halo cost were added to the model. The new decompositions better leverage land block elimination and take advantage of scaling opportunities in different computational kernels. As a result of these new features, the CICE model performance has been improved by up to 45% and has more flexibility to be run efficiently at arbitrary Message Passing Interface (MPI) task counts.

Stochastic modeling and optimization of garbage collection algorithms in solid-state drive systems

Markov chains and mean-field analysis are powerful tools and widely used for performance analysis in large-scale computer and communication systems. In this paper, we consider the application of Markov modeling and mean-field analysis to solid-state drives (SSDs). SSDs are now widely deployed in mobiles, desktops, and data centers due to their high I/O performance and low energy consumption. In particular, we focus on characterizing the performance---durability tradeoff of garbage collection (GC) algorithms in SSDs. Specifically, we first develop a stochastic Markov chain model to capture the I/O dynamics of large-scale SSDs, then adapt mean-field analysis to derive the asymptotic steady state, based on which we are able to easily analyze the performance---durability tradeoff of a large family of GC algorithms. We further prove the model convergence and generalize the model for all types of workload. Inspired by this model, we also propose a randomized greedy algorithm (RGA) which has a single tunable parameter to trade between performance and durability. Using trace-driven simulation on DiskSim with SSD add-ons, we demonstrate how RGA can be parameterized to realize the performance---durability tradeoff.

A survey on techniques for improving the energy efficiency of large-scale distributed systems

The great amounts of energy consumed by large-scale computing and network systems, such as data centers and supercomputers, have been a major source of concern in a society increasingly reliant on information technology. Trying to tackle this issue, the research community and industry have proposed myriad techniques to curb the energy consumed by IT systems. This article surveys techniques and solutions that aim to improve the energy efficiency of computing and network resources. It discusses methods to evaluate and model the energy consumed by these resources, and describes techniques that operate at a distributed system level, trying to improve aspects such as resource allocation, scheduling, and network traffic management. This work aims to review the state of the art on energy efficiency and to foster research on schemes to make network and computing resources more efficient.

Software defined infrastructures

A fundamental component of any large-scale computer system is infrastructure. Cloud computing has completely changed the way infrastructure is viewed, offering more simplicity, flexibility, and monetary benefits compared to a traditional view of infrastructure. At the core of this transformation is the notion of virtualization of infrastructure as a whole, with providers offering infrastructure-as-a-service (IaaS) to consumers. However, just offering IaaS alone is insufficient for software defined environments (SDEs). This paper examines infrastructure in the context of SDE and discusses what we believe are some of the fundamental characteristics required of such infrastructure—called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">software defined infrastructure</i> (SDI)—and how it fits into the larger landscape of cloud computing environments and SDEs. Various components of SDI are discussed, including core intelligence, monitoring pieces, and management, in addition to a brief discussion on silos such as compute, network and storage. Consumer and provider points of view are also presented along with infrastructure-level service-level agreements (SLAs). Also presented are the design principles and high-level architectural design of the infrastructure intelligence controller, which constantly transforms infrastructure to honor consumer requirements (SLAs) amidst provider constraints (costs). We believe that the insights presented in this paper can be used for better design of SDE architectures and of data-center systems software in general.

A study on combinational effects of job and resource characteristics on energy consumption

By becoming more popular and complex, HPC systems like computational grids, clusters, clouds and the supporting data centers are now changed to remarkable energy consumers. A wide variety of researches, ranging from power-aware hardware design to developing optimized programs and to power-aware job scheduling, have been done hitherto in order to reduce their energy consumption. However, the success of these approaches highly depends to having a precise knowledge about power consumption behavior of the target system. In this paper, some neglected facts are shown about combinational effects of jobs' and resources' characteristics on energy consumption rate and define corresponding parameters which make these facts practically utilizable by formulating them as functions of job-machine characteristics. These facts are supported by the experimental analyses on real machines and analytical studies. The outcome of this paper can be exploited to have more energy efficient task mapping process in large-scale heterogeneous computational systems.

Benchmarking cloud performance for service level agreement parameters

Infrastructure as a Service (IaaS) Clouds offer capabilities for the high-availability of a wide range of systems, from individual virtual machines to large-scale high performance computing (HPC) systems. But it is argued that the widespread uptake for such systems will only happen if Cloud providers, or brokers, are able to offer bilateral service level agreements (SLAs). In this paper, we discuss how to measure and use quality of service (QoS) information to be able to predict availability, quantify risk, and consider liability in case of failure. We demonstrate through this work that there is a pressing need for such an understanding and explore a set of benchmarks that offers an interesting characterisation of resource performance variability which can be quite significant. We subsequently identify how such information might be used both directly by a user and indirectly via a Cloud Broker in the automatic construction and management of SLAs which reference certain kinds of financial portfolios.

Investigation on the Multi-Objective Optimization of Supercritical Airfoil Based on Nondominated Sorting Genetic Algorithm

This paper aimed to investigate the multi-objective optimization method of supercritical airfoil. To achieve the optimal design of supercritical airfoil Rae2822, an improved NSGA-2 (Nondominated Sorting Genetic Algorithm) method was utilized, while the cross-operator and adaptive-variation operator were introduced to improve the convergence speed of the algorithm. During the optimization, the airfoil parametric modeling was achieved based on the Bezier-Bernstein method, and the objective function was obtained through solving the N-S equations. Considering the parallel computation characteristics of the algorithm, the computation was conducted in large-scale Linux computer system to reduce the solving time. Optimization results showed that the undominate solution with high quality obtained through the NSGA-2 method distributed evenly, which provided the designer a wider choosing space. It was also showed that the multi-objective optimization method presented in this paper was feasible and reliable.

Investigation on the Multi-Objective Optimization of Supercritical Airfoil Based on Nondominated Sorting Genetic Algorithm

This paper aimed to investigate the multi-objective optimization method of supercritical airfoil. To achieve the optimal design of supercritical airfoil Rae2822, an improved NSGA-2 (Nondominated Sorting Genetic Algorithm) method was utilized, while the cross-operator and adaptive-variation operator were introduced to improve the convergence speed of the algorithm. During the optimization, the airfoil parametric modeling was achieved based on the Bezier-Bernstein method, and the objective function was obtained through solving the N-S equations. Considering the parallel computation characteristics of the algorithm, the computation was conducted in large-scale Linux computer system to reduce the solving time. Optimization results showed that the undominate solution with high quality obtained through the NSGA-2 method distributed evenly, which provided the designer a wider choosing space. It was also showed that the multi-objective optimization method presented in this paper was feasible and reliable.

Cascaded Mach-Zehnder wavelength filters in silicon photonics for low loss and flat pass-band WDM (de-)multiplexing

We present 1-to-8 wavelength (de-)multiplexer devices based on a binary tree of cascaded Mach-Zehnder-like lattice filters, and manufactured using a 90 nm CMOS-integrated silicon photonics technology. We demonstrate that these devices combine a flat pass-band over more than 50% of the channel spacing with low insertion loss of less than 1.6 dB, and have a small device size of approximately 500 × 400 µm. This makes this type of filters well suited for application as WDM (de-)multiplexer in silicon photonics transceivers for optical data communication in large scale computer systems.

On the trade‐off of mixing scientific applications on capacity high‐performance computing systems

Network contention is seen as a major hurdle to achieve higher throughput in today's large-scale high-performance computing systems. Even more so with the current trend of employing blocking networks driven by the need of reducing cost. Additionally, the effect is aggravated by current system schedulers that allocate jobs as soon as nodes become available, thus producing job fragmentation, that is, the tasks of one job might be spread throughout the system instead of being allocated contiguously. This fragmentation increases the probability of sharing network resources with other applications, which produces higher inter-application network contention. In this study, the authors perform a broad analysis of diverse applications' performance variability because of the topology connectivity and fragmentation and a classification of applications based on their sensitiveness to these two factors. Once they understood the inherent characteristics of applications, the authors analysed the applications performance in a shared environment, that is, when mixing with other applications. They show that inter-application contention might be a significant factor of degradation even in the networks with high connectivity. Their results suggest different strategies on task allocation policies: grouping sensitive and insensitive applications, reducing the number of applications sharing the first level switch or isolation of sensitive applications.

A new parallel recomputing code design methodology for fast failure recovery

As the size of large-scale computer systems increases, their mean-time-between-failures are becoming significantly shorter than the execution time of many current scientific applications. Fault-tolerant parallel algorithm (FTPA) is an application-level fault-tolerant approach that can achieve fast self-recovery by parallel recomputing. The method of parallelizing the loops has been used to design the parallel recomputing code for FTPA in our prior work.In the present paper, we first propose a new parallel recomputing code design methodology. Second, the parallel recomputing code design methodology is automated by exploring the use of compiler technology. Finally, we evaluate the performance of our approach with five programs on Tianhe-1A. The experimental results show that the parallel recomputing code generated by the new method has a higher efficiency of parallel recomputing.

Scalable optimization in grid, cloud, and intelligent network computing – foreword

Scalable optimization in grid, cloud, and intelligent network computing – foreword

Silicon Photonic Interconnects for Large-Scale Computer Systems

Optical interconnects play an integral role in large-scale digital computing, switching, and routing systems. The authors describe a path toward future many-chip modules based on silicon photonic interposers that stitch together tens of chips in a dense and efficient communication infrastructure. They review the guiding design principles for this macrochip and describe its canonical energy, loss, and area budgets.

Modeling and Simulation of Smart and Green Computing Systems

Advanced modeling and simulation methodologies are an essential aspect of the comprehensive performance evaluation that precedes the costly prototyping activities required for complex, large-scale computing, control, and communication systems.