SCALABLE CAPABILITY-BASED AUTHORIZATION FOR HIGH-PERFORMANCE PARALLEL FILE SYSTEMS

As the size and scale of supercomputers continues to increase at an exponential rate the number of users on a given supercomputer will only grow larger. A larger number of users on a supercomputer places a greater importance on the strength of information security. Nowhere is this requirement for security more apparent than the file system, as users expect their data to be protected from accidental or deliberate modification. In spite of the ever-increasing demand for more secure file system access the majority of parallel file systems do not implement a robust security protocol for fear it will negatively impact the performance and scalability of the file system. We provide a capability-based security protocol for use in high-performance parallel file systems that is capable of meeting the performance and scalability requirements of current and future supercomputers. We develop a reference implementation for the Parallel Virtual File System and show its performance characteristics using several microbenchmarks. Our test results show that capability-based security is capable of protecting access to parallel file system objects, in some cases with little overhead

Two ways to Grid: the contribution of Open Grid Services Architecture (OGSA) mechanisms to service-centric and resource-centric lifecycles

Service Oriented Architectures (SOAs) support service lifecycle tasks, including Development, Deployment, Discovery and Use. We observe that there are two disparate ways to use Grid SOAs such as the Open Grid Services Architecture (OGSA) as exemplified in the Globus Toolkit (GT3/4). One is a traditional enterprise SOA use where end-user services are developed, deployed and resourced behind firewalls, for use by external consumers: a service-centric (or ‘first-order’) approach. The other supports end-user development, deployment, and resourcing of applications across organizations via the use of execution and resource management services: A Resource-centric (or ‘second-order’) approach. We analyze and compare the two approaches using a combination of empirical experiments and an architectural evaluation methodology (scenario, mechanism, and quality attributes) to reveal common and distinct strengths and weaknesses. The impact of potential improvements (which are likely to be manifested by GT4) is estimated, and opportunities for alternative architectures and technologies explored. We conclude by investigating if the two approaches can be converged or combined, and if they are compatible on shared resources

Understanding and Comparing Scalable Gaussian Process Regression for Big Data

As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the era of big data. Hence, various scalable GPs have been developed in the literature in order to improve the scalability while retaining desirable prediction accuracy. This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness. The numerical experiments on two toy examples and five real-world datasets with up to 250K points offer the following findings. In terms of scalability, most of the scalable GPs own a time complexity that is linear to the training size. In terms of capability, the sparse approximations capture the long-term spatial correlations, the local aggregations capture the local patterns but suffer from over-fitting in some scenarios. In terms of controllability, we could improve the performance of sparse approximations by simply increasing the inducing size. But this is not the case for local aggregations. In terms of robustness, local aggregations are robust to various initializations of hyperparameters due to the local attention mechanism. Finally, we highlight that the proper hybrid of global and local scalable GPs may be a promising way to improve both the model capability and scalability for big data.Comment: 25 pages, 15 figures, preprint submitted to KB

Scalability of broadcast performance in wireless network-on-chip

Networks-on-Chip (NoCs) are currently the paradigm of choice to interconnect the cores of a chip multiprocessor. However, conventional NoCs may not suffice to fulfill the on-chip communication requirements of processors with hundreds or thousands of cores. The main reason is that the performance of such networks drops as the number of cores grows, especially in the presence of multicast and broadcast traffic. This not only limits the scalability of current multiprocessor architectures, but also sets a performance wall that prevents the development of architectures that generate moderate-to-high levels of multicast. In this paper, a Wireless Network-on-Chip (WNoC) where all cores share a single broadband channel is presented. Such design is conceived to provide low latency and ordered delivery for multicast/broadcast traffic, in an attempt to complement a wireline NoC that will transport the rest of communication flows. To assess the feasibility of this approach, the network performance of WNoC is analyzed as a function of the system size and the channel capacity, and then compared to that of wireline NoCs with embedded multicast support. Based on this evaluation, preliminary results on the potential performance of the proposed hybrid scheme are provided, together with guidelines for the design of MAC protocols for WNoC.Peer ReviewedPostprint (published version

Mesmerizer: A Effective Tool for a Complete Peer-to-Peer Software Development Life-cycle

In this paper we present what are, in our experience, the best practices in Peer-To-Peer(P2P) application development and how we combined them in a middleware platform called Mesmerizer. We explain how simulation is an integral part of the development process and not just an assessment tool. We then present our component-based event-driven framework for P2P application development, which can be used to execute multiple instances of the same application in a strictly controlled manner over an emulated network layer for simulation/testing, or a single application in a concurrent environment for deployment purpose. We highlight modeling aspects that are of critical importance for designing and testing P2P applications, e.g. the emulation of Network Address Translation and bandwidth dynamics. We show how our simulator scales when emulating low-level bandwidth characteristics of thousands of concurrent peers while preserving a good degree of accuracy compared to a packet-level simulator

Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale

The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangian method, used in numerical simulations of fluids in astrophysics and computational fluid dynamics, among many other fields. SPH simulations with detailed physics represent computationally-demanding calculations. The parallelization of SPH codes is not trivial due to the absence of a structured grid. Additionally, the performance of the SPH codes can be, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. This work presents insights into the current performance and functionalities of three SPH codes: SPHYNX, ChaNGa, and SPH-flow. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. To gain such insights, a rotating square patch test was implemented as a common test simulation for the three SPH codes and analyzed on two modern HPC systems. Furthermore, to stress the differences with the codes stemming from the astrophysics community (SPHYNX and ChaNGa), an additional test case, the Evrard collapse, has also been carried out. This work extrapolates the common basic SPH features in the three codes for the purpose of consolidating them into a pure-SPH, Exascale-ready, optimized, mini-app. Moreover, the outcome of this serves as direct feedback to the parent codes, to improve their performance and overall scalability.Comment: 18 pages, 4 figures, 5 tables, 2018 IEEE International Conference on Cluster Computing proceedings for WRAp1