Advances in computational science, combined with the increasingly interdisciplinary and geographically distributed research teams, have led to a need to support multi-tiered, data- and meta-data-rich collaboration infrastructures. Our research addresses the interactive, remote tasks undertaken in such collaborations, which require a flexible software infrastructure able to dynamically deploy services where and when needed, and to provide data to clients in the forms in which they require it with suitable levels of end-to-end performance. The concept of service augmentation advanced in this paper seeks to continuously adjust the differences or degrees of incompatibility between the data received and the data displayed or stored by clients. Difference adjustments occur anywhere on the paths between data providers and clients, and compatibility computations leverage all of the resources that may be brought to bear, including CPUs and GPUs on servers and additional data manipulations on server, overlay, and client nodes. A formal structure and experimental evaluations of this concept are performed with the SmartPointer scientific visualization and annotation framework, for which we show that data-driven SLAs provide improved client flexibility and the ability to maintain application-specific notions of quality of service

Fast growing large-scale systems enable scientific applications to run at a much larger scale and accordingly produce gigantic volumes of simulation output. Such data imposes a grand challenge to post-processing tasks such as visualization and data analysis, because these tasks are often performed at a host machine that is remotely located and equipped with much less memory and storage resources. During the simulation runs, it is also desirable for scientists to be able to interactively monitor and steer the progress of simulation. This requires scientific data to be represented in an efficient form for initial exploration and computation steering. In this paper, we propose DynaM a software framework that can represent scientific data in a multiresolution form, and dynamically organize data blocks into an optimized layout for efficient scientific analysis. DynaM supports a convolution-based multiresolution data representation for abstracting scientific data for visualization at a wide spectrum of resolution. To support the efficient generation and retrieval of different data granularities from such representation, a dynamic data organization in DynaM is enabled to cater distinct peculiarities of different size data blocks for efficient and balanced I/O performance. Our experimental results demonstrate that DynaM can efficiently represent large scientific dataset and speed up the visualization of multidimensional scientific data. An up to 29 times speedup is achieved on Jaguar supercomputer at Oak Ridge National Laboratory.

Today many young members of the Sindhi community are migrating from villages to cities in Pakistan, where the national language, Urdu and the co-official language, English, dominates. This study investigates the daily language patterns of transplanted Sindhi speakers and the impact of such patterns on their mother tongue. Furthermore, as these speakers frequently switch from one language to another due to exposure to multilingualism in urban settings, this study determines the dominant language being used by young Sindhis in their mixed discourse, compared to the discourse of older Sindhis. Reasons for adopting other languages in academic institutions and home-settings were also investigated. A qualitative case study was conducted to obtain detailed responses about the linguistic choices of twenty young Sindhi speakers. Two Sindhi families were also observed to study the language choices across generations in home-settings, and semi-structured interviews were conducted to determine reasons for the mixed languages which emerged. The data were analysed using frequency analysis for linguistic choices and thematic analysis of the daily life discourse. Results show that the young Sindhis in the city of Karachi are using dominant languages Urdu and English in their daily life and have higher proficiency in Urdu and English as compared to their mother tongue Sindhi. Moreover, there are several social, economic, and cultural reasons for switching from one language to another among the younger Sindhi generation, in contrast to the older Sindhi generation.

Pakistan is a diverse multiethnic country and has different cultures and languages. There are 74 languages spoken in the country (Siddiqui, 2019), year). However, different communities tend to migrate from their hometowns or rural areas to cities due to geographical, social, and economic mobility. In cities, they tend to adopt a new identity, culture, and language for sustainability. Karachi is the provincial capital and hub of various speech communities and faces these issues. This study focuses on an inter-provincial migrated Khowar community and their language use at home, neighborhood, educational institutions, and workplace domains in Karachi. It investigates the language use patterns with a focus on male and female Khowar speakers who have different roles and exposures in society. A qualitative case study has been undertaken with purposive and snowball sampling. Data for linguistic choices were taken from 20 young male and female Khowar speakers enrolled in a public sector university through semi-structured interviews. The interviews were transcribed and underwent thematic analysis. The findings of the study revealed differences in language use patterns across gender; female Khowar speakers are preserving the language in the home, neighborhood, and educational sector while the male speakers are shifting to Urdu and English in educational and social domains. However, the language practices for both males and females are changing within the workplace domains as Urdu is used as a common Lingua Franca for wider communication. Although the language use patterns have begun to change, the male and female Khowar speakers maintain a strong ethnolinguistic identity and affiliation with their community. Government, community members, and policymakers must be made aware of shifting language use among Khowar and other ethnic communities who have migrated to multilingual cities for a better understanding of the multilingual scenario in cities and drafting and designing the official language policy and planning at the provincial and federal level.

We present a collection of techniques for exploiting latent I/O asynchrony which can substantially improve performance in data-intensive parallel applications. Latent asynchrony refers to an application's tolerance for decoupling ancillary operations from its core computation, and is a property of HPC codes not fully explored by current HPC I/O systems. Decoupling operations such as buffering and staging, reorganization, and format conversion in space and in time from core codes can shorten I/O phases, preserving valuable MPP compute cycles. We describe in this paper DataTaps, IOgraphs, and Metabots, three tools which allow HPC developers to implement decoupled I/O operations. Using these tools, asynchrony can be exploited by data generators which overlap computation with communication, and by data consumers that perform data conversion and reorganization out-of-band and on-demand. In the context of a data-intensive fusion simulation, we show that exploiting latent asynchrony through decoupling of operations can provide significant performance benefits.