Lamia Youseff

Toward a Unified Ontology of Cloud Computing

Progress of research efforts in a novel technology is contingent on having a rigorous organization of its knowledge domain and a comprehensive understanding of all the relevant components of this technology and their relationships. Cloud computing is one contemporary technology in which the research community has recently embarked. Manifesting itself as the descendant of several other computing research areas such as service-oriented architecture, distributed and grid computing, and virtualization, cloud computing inherits their advancements and limitations. Towards the end-goal of a thorough comprehension of the field of cloud computing, and a more rapid adoption from the scientific community, we propose in this paper an ontology of this area which demonstrates a dissection of the cloud into five main layers, and illustrates their interrelations as well as their inter-dependency on preceding technologies. The contribution of this paper lies in being one of the first attempts to establish a detailed ontology of the cloud. Better comprehension of the technology would enable the community to design more efficient portals and gateways for the cloud, and facilitate the adoption of this novel computing approach in scientific environments. In turn, this will assist the scientific community to expedite its contributions and insights into this evolving computing field.

Paravirtualization for HPC systems

In this work, we investigate the efficacy of using paravirtualizing software for performance-critical HPC kernels and applications. We present a comprehensive performance evaluation of Xen, a low-overhead, Linux-based, virtual machine monitor, for paravirtualization of HPC cluster systems at LLNL. We investigate subsystem and overall performance using a wide range of benchmarks and applications. We employ statistically sound methods to compare the performance of a paravirtualized kernel against three Linux operating systems: RedHat Enterprise 4 for build versions 2.6.9 and 2.6.12 and the LLNL CHAOS kernel. Our results indicate that Xen is very efficient and practical for HPC systems.

Evaluating the Performance Impact of Xen on MPI and Process Execution For HPC Systems

Virtualization has become increasingly popular for enabling full system isolation, load balancing, and hardware multiplexing for high-end server systems. Virtualizing software has the potential to benefit HPC systems similarly by facilitating efficient cluster management, application isolation, full-system customization, and process migration. However, virtualizing software is not currently employed in HPC environments due to its perceived overhead. In this work, we investigate the overhead imposed by the popular, open-source, Xen virtualization system, on performance-critical HPC kernels and applications. We empirically evaluate the impact of Xen on both communication and computation and compare its use to that of a customized kernel using HPC cluster resources at Lawrence Livermore National Lab (LLNL). We also employ statistically sound methods to compare the performance of a para virtualized kernel against three popular Linux operating systems: RedHat Enterprise 4 (RHEL4) for build versions 2.6.9 and 2.6.12 and the LLNL CHAOS kernel, a specialized version of RHEL4. Our results indicate that Xen is very efficient and practical for HPC systems.

The impact of paravirtualized memory hierarchy on linear algebra computational kernels and software

Previous studies have revealed that paravirtualization imposes minimal performance overhead on High Performance Computing (HPC) workloads, while exposing numerous benefits for this field. In this study, we are investigating the memory hierarchy characteristics of paravirtualized systems and their impact on automatically-tuned software systems. We are presenting an accurate characterization of memory attributes using hardware counters and user-process accounting. For that, we examine the proficiency of ATLAS, a quintessential example of an autotuning software system, in tuning the BLAS library routines for paravirtualized systems. In addition, we examine the effects of paravirtualization on the performance boundary. Our results show that the combination of ATLAS and Xen paravirtualization delivers native execution performance and nearly identical memory hierarchy performance profiles. Our research thus exposes new benefits to memory-intensive applications arising from the ability to slim down the guest OS without influencing the system performance. In addition, our findings support a novel and very attractive deployment scenario for computational science and engineering codes on virtual clusters and computational clouds.

Discrete and continuous models of the dynamics of pelagic fish: Application to the capelin

In this paper, we study simulations of the schooling and swarming behavior of a mathematical model for the motion of pelagic fish. We use a derivative of a discrete model of interacting particles originated by Vicsek, Czir´ok et al. [6] [5] [23] [24]. Recently, a system of ODEs was derived from this model [2], and using these ODEs, we find transitory and long-term behavior of the discrete system. In particular, we numerically find stationary, migratory, and circling behavior in both the discrete and the ODE model and two types of swarming behavior in the discrete model. The migratory solutions are numerically stable and the circling solutions are metastable. We find a stable circulating ring solution of the discrete system where the fish travel in opposite directions within an annulus. We also find the origin of noise-driven swarming when repulsion and attraction are absent and the fish interact solely via orientation.

Parallel Modeling of Fish Interaction

This paper summarizes our work on a parallel algorithm for an interacting particle model, derived from the model by Czirok, Vicsek, et. al. [4, 5, 6, 15, 16]. Our model is particularly geared toward simulating the behavior of fish in large shoals. In this paper, the background and motivation for the problem are given, as well as an introduction to the mathematical model. A discussion of implementing this model in MATLAB and C++ follows. The parallel implementation is discussed with challenges particular to this mathematical model and how the authors addressed these challenges. Load balancing was performed and is discussed. Finally, a performance analysis follows, using a performance metric to compare the MATLAB , C+ + , and parallelized code.

Paravirtualization effect on single- and multi-threaded memory-intensive linear algebra software

Previous studies have revealed that paravirtualization imposes minimal performance overhead on High Performance Computing (HPC) workloads, while exposing numerous benefits for this field. In this study, we are investigating the impact of paravirtualization on the performance of automatically-tuned software systems. We compare peak performance, performance degradation in constrained memory situations, performance degradation in multi-threaded applications, and inter-VM shared memory performance. For comparison purposes, we examine the proficiency of ATLAS, a quintessential example of an autotuning software system, in tuning the BLAS library routines for paravirtualized systems. Our results show that the combination of ATLAS and Xen paravirtualization delivers native execution performance and nearly identical memory hierarchy performance profiles in both single and multi-threaded scenarios. Furthermore, we show that it is possible to achieve memory sharing among OS instances at native speeds. These results expose new benefits to memory-intensive applications arising from the ability to slim down the guest OS without influencing the system performance. In addition, our findings support a novel and very attractive deployment scenario for computational science and engineering codes on virtual clusters and computational clouds.

VIProf: Vertically Integrated Full-System Performance Profiler

In this paper, we present VIProf, a full-system, performance sampling system capable of extracting runtime behavior across an entire software stack. Our long-term goal is to employ VIProf profiles to guide online optimization of programs and their execution environments according to the dynamically changing execution behavior and resource availability. VIProf thus, must be transparent while producing accurate and useful performance profiles. We overview the design and implementation of VIProf and empirically evaluate the system using a popular software stack - one that includes a Linux operating system, a Java virtual machine, and a set of applications. This composition is commonly employed and important for high-end systems such as application and Web servers as well as computational grid services. We show that VIProf introduces little overhead and is able to capture accurate (function-level) full-system performance data that previously required multiple profiles and extensive, manual, and offline post-processing of profile data.