Conceptual and Implementation Models for the Grid

The Grid is rapidly emerging as the dominant paradigm for wide area distributed application systems. As a result, there is a need for modeling and analyzing the characteristics and requirements of Grid systems and programming models. This work adopts the well-established body of models for distributed computing systems, which are based upon carefully stated assumptions or axioms, as a basis for defining and characterizing Grids and their programming models and systems. The requirements of programming Grid applications and the resulting requirements on the underlying virtual organizations and virtual machines are investigated. The assumptions underlying some of the programming models and systems currently used for Grid applications are identified and their validity in Grid environments is discussed. A more in-depth analysis of two programming systems, the Imperial College E-Science Networked Infrastructure (ICENI) and Accord, using the proposed definitions' structure is presented.     

Hybrid Runtime Management of Space-Time Heterogeneity for Parallel Structured Adaptive Applications

Structured adaptive mesh refinement (SAMR) techniques provide an effective means for dynamically concentrating computational effort and resources to appropriate regions in the application domain. However, due to their dynamism and space-time heterogeneity, scalable parallel implementation of SAMR applications remains a challenge. This paper investigates hybrid runtime management strategies and presents an adaptive hierarchical multipartitioner (AHMP) framework. AHMP dynamically applies multiple partitioners to different regions of the domain, in a hierarchical manner, to match the local requirements of the regions. Key components of the AHMP framework include a segmentation-based clustering algorithm (SBC) that can efficiently identify regions in the domain with relatively homogeneous partitioning requirements, mechanisms for characterizing the partitioning requirements of these regions, and a runtime system for selecting, configuring, and applying the most appropriate partitioner to each region. Further, to address dynamic resource situations for long-running applications, AHMP provides a hybrid partitioning strategy (HPS) that involves application-level pipelining, trading space for time when resources are sufficiently large and underutilized, and an application-level out-of-core strategy (ALOC), trading time for space when resources are scarce in order to enhance the survivability of applications. The AHMP framework has been implemented and experimentally evaluated on up to 1,280 processors of the IBM SP4 cluster at the San Diego Supercomputer Center.     

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.     

ISHair: Importance Sampling for Hair Scattering

We present an importance sampling method for the bidirectional scattering distribution function (bsdf) of hair. Our method is based on the multi‐lobe hair scattering model presented by Sadeghi et al. [ [SPJT10] ]. We reduce noise by drawing samples from a distribution that approximates the bsdf well. Our algorithm is efficient and easy to implement, since the sampling process requires only the evaluation of a few analytic functions, with no significant memory overhead or need for precomputation. We tested our method in a research raytracer and a production renderer based on micropolygon rasterization. We show significant improvements for rendering direct illumination using multiple importance sampling and for rendering indirect illumination using path tracing.     

Proactive thermal management in green datacenters

The increasing demand for faster computing and high storage capacity has resulted in an increase in energy consumption and heat generation in datacenters. Because of the increase in heat generation, cooling requirements have become a critical concern, both in terms of growing operating costs as well as their environmental and societal impacts. Presently, thermal management techniques make an effort to thermally profile and control datacenters’ cooling equipment to increase their efficiency. In conventional thermal management techniques, cooling systems are triggered by the temperature crossing predefined thresholds. Such reactive approaches result in delayed response as the temperature may already be too high, which can result in performance degradation of hardware.     

A Peer-to-Peer Approach to Web Service Discovery

Web Services are emerging as a dominant paradigm for constructing and composing distributed business applications and enabling enterprise-wide interoperability. A critical factor to the overall utility of Web Services is a scalable, flexible and robust discovery mechanism. This paper presents a Peer-to-Peer (P2P) indexing system and associated P2P storage that supports large-scale, decentralized, real-time search capabilities. The presented system supports complex queries containing partial keywords and wildcards. Furthermore, it guarantees that all existing data elements matching a query will be found with bounded costs in terms of number of messages and number of nodes involved. The key innovation is a dimension reducing indexing scheme that effectively maps the multidimensional information space to physical peers. The design and an experimental evaluation of the system are presented.     

Hierarchical Partitioning Techniques for Structured Adaptive Mesh Refinement Applications

This paper presents the design and preliminary evaluation of hierarchical partitioning and load-balancing techniques for distributed structured adaptive mesh refinement (SAMR) applications. The overall goal of these techniques is to enable the load distribution to reflect the state of the adaptive grid hierarchy and exploit it to reduce synchronization requirements, improve load-balance, and enable concurrent communications and incremental redistribution. The hierarchical partitioning algorithm (HPA) partitions the computational domain into subdomains and assigns them to hierarchical processor groups. Two variants of HPA are presented in this paper. The static hierarchical partitioning algorithm (SHPA) assigns portions of overall load to processor groups. In SHPA, the group size and the number of processors in each group is setup during initialization and remains unchanged during application execution. It is experimentally shown that SHPA reduces communication costs as compared to the Non-HPA scheme, and reduces overall application execution time by up to 59%. The adaptive hierarchical partitioning algorithm (AHPA) dynamically partitions the processor pool into hierarchical groups that match the structure of the adaptive grid hierarchy. Initial evaluations of AHPA show that it can reduce communication costs by up to 70%.     

Electrical behaviour of discontinuous silver films deposited on softened polystyrene and poly (4-vinylpyridine) blends

Results of the studies carried out on the electrical behaviour of silver island films deposited on the blends of polystyrene (PS) and poly (4-vinylpyridine) (P4VP) are presented here. The substrates were held at 457 K, much above the glass transition temperature of both the polymers to ensure sufficient polymer fluidity during deposition, to obtain a sub-surface particulate film. A constant deposition rate of 0.4 nm/s was used throughout the study. Films on softened PS gives rise to a very high room temperature resistance approaching that of the substrate resistance due to the formation of a highly agglomerated structure. On the other hand, films on softened P4VP gives rise to a room temperature resistance in the range of a few tens to a few hundred MΩ/, which is desirable for device applications. The blends of PS and P4VP show room temperature resistances in the desirable range even at a PS/P4VP ratio of 75:25. The films show an increase in resistance when they are exposed to atmosphere. This is attributed to the oxidation of silver islands. The film resistances in the desired range could be obtained even after exposure to atmosphere up to a PS concentration of 50%.     

Addressing spatiotemporal and computational heterogeneity in structured adaptive mesh refinement

Structured adaptive mesh refinement (SAMR) techniques can provide accurate and cost- effective solutions to realistic scientific and engineering simulations modeling complex physical phenomena. However, the adaptive nature and inherent space–time heterogeneity of SAMR applications result in significant runtime management challenges. Moreover, certain SAMR applications involving reactive flows exhibit pointwise varying workloads and cannot be addressed by traditional parallelization approaches, which assume homogeneous loads. This paper presents hierarchical partitioning, bin-packing based load balancing, and Dispatch structured partitioning strategies to manage the spatiotemporal and computational heterogeneity in SAMR applications. Experimental evaluation of these schemes using 3-D Richtmyer–Meshkov compressible turbulence and 2-D reactive-diffusion kernels demonstrates the improvement in overall performance.     

LF55GN Photosensitive Flexopolymer: A New Material for Ultrathick and High-Aspect-Ratio MEMS Fabrication

A growing interest in the development of thick functional structures with high aspect ratio for microelectromechanical system (MEMS) applications has triggered the investigation of several polymer materials. This paper presents LF55GN flexopolymer material as a new negative-tone photoresist to fabricate ultrathick MEMS microstructures. Up to 4-mm-thick layers are obtained using a casting method in a single photolithography step. Standard UV illumination is used to polymerize such thick microstructures in less than 1 min and with an aspect ratio up to 27. We have fabricated microstructures on rigid, flexible, and stepped substrates. Using oblique UV exposure, tilted pillars are achieved with an angle of 25deg to the substrate normal. Due to the elastomeric nature of the LF55GN flexopolymer, the microstructures can be easily deformed without causing any stress-related problems.