A Study of Scalability and Cost-effectiveness of Large-scale Scientific Applications over Heterogeneous Computing Environment

Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data. In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures that these software tools need for good performance. 3) Transferring the big scientific dataset among clusters situated at geographically disparate locations. In the first part, I develop scalable algorithms to process huge amounts of scientific big data using the power of recent analytic tools such as, Hadoop, Giraph, NoSQL, etc. At a broader level, these algorithms take the advantage of locality-based computing that can scale with increasing amount of data. The thesis mainly addresses the challenges involved in large-scale genome analysis applications such as, genomic error correction and genome assembly which made their way to the forefront of big data challenges recently. In the second part of the thesis, I perform a systematic benchmark study using the above-mentioned algorithms on different distributed cyberinfrastructures to pinpoint the limitations in a traditional HPC cluster to process big data. Then I propose the solution to those limitations by balancing the I/O bandwidth of the solid state drive (SSD) with the computational speed of high-performance CPUs. A theoretical model has been also proposed to help the HPC system designers who are striving for system balance. In the third part of the thesis, I develop a high throughput architecture for transferring these big scientific datasets among geographically disparate clusters. The architecture leverages the power of Ethereum\u27s Blockchain technology and Swarm\u27s peer-to-peer (P2P) storage technology to transfer the data in secure, tamper-proof fashion. Instead of optimizing the computation in a single cluster, in this part, my major motivation is to foster translational research and data interoperability in collaboration with multiple institutions

Additivity property and emergence of power laws in nonequilibrium steady states

We show that an equilibriumlike additivity property can remarkably lead to power-law distributions observed frequently in a wide class of out-of-equilibrium systems. The additivity property can determine the full scaling form of the distribution functions and the associated exponents. The asymptotic behavior of these distributions is solely governed by branch-cut singularity in the variance of subsystem mass. To substantiate these claims, we explicitly calculate, using the additivity property, subsystem mass distributions in a wide class of previously studied mass aggregation models as well as in their variants. These results could help in the thermodynamic characterization of nonequilibrium critical phenomena.Comment: Revised longer version, 4 figure

Home Automation Using Arduino and ESP8266

With the advent of technology, life has become faster in pace and shorter in interactions, with others, as well as with the surroundings. In such a scenario, there is a need to have an endeavor to have everything at the push of a button away, and more importantly, automated. Home Automation is such an endeavor, in which, all the electrical appliances present at home are connected to each other, having interactions with sensors placed at strategic positions in a closed loop manner in order to perform meager tasks automatically, leaving less burden on the humans. With this project we are promoting the fact that Home Automation can greatly contribute to energy conservation too

Many faces of low mass neutralino dark matter in the unconstrained MSSM, LHC data and new signals

If all strongly interacting sparticles (the squarks and the gluinos) in an unconstrained minimal supersymmetric standard model (MSSM) are heavier than the corresponding mass lower limits in the minimal supergravity (mSUGRA) model, obtained by the current LHC experiments, then the existing data allow a variety of electroweak (EW) sectors with light sparticles yielding dark matter (DM) relic density allowed by the WMAP data. Some of the sparticles may lie just above the existing lower bounds from LEP and lead to many novel DM producing mechanisms not common in mSUGRA. This is illustrated by revisiting the above squark-gluino mass limits obtained by the ATLAS Collaboration, with an unconstrained EW sector with masses not correlated with the strong sector. Using their selection criteria and the corresponding cross section limits, we find at the generator level using Pythia, that the changes in the mass limits, if any, are by at most 10-12% in most scenarios. In some cases, however, the relaxation of the gluino mass limits are larger ($\approx 20$). If a subset of the strongly interacting sparticles in an unconstrained MSSM are within the reach of the LHC, then signals sensitive to the EW sector may be obtained. This is illustrated by simulating the $blj$\etslash, $l= e and \mu$, and $b\tau j$\etslash signals in i) the light stop scenario and ii) the light stop-gluino scenario with various light EW sectors allowed by the WMAP data. Some of the more general models may be realized with non-universal scalar and gaugino masses.Comment: 27 pages, 1 figure, references added, minor changes in text, to appear in JHE

A Novel Copper Chelate Modulates Tumor Associated Macrophages to Promote Anti-Tumor Response of T Cells

At the early stages of carcinogenesis, the induction of tumor specific T cell mediated immunity seems to block the tumor growth and give protective anti-tumor immune response. However, tumor associated macrophages (TAMs) might play an immunosuppressive role and subvert this anti tumor immunity leading to tumor progression and metastasis.The Cu (II) complex, (chelate), copper N-(2-hydroxy acetophenone) glycinate (CuNG), synthesized by us, has previously been shown to have a potential usefulness in immunotherapy of multiple drug resistant cancers. The current study demonstrates that CuNG treatment of TAMs modulates their status from immunosuppressive to proimmunogenic nature. Interestingly, these activated TAMs produced high levels of IL-12 along with low levels of IL-10 that not only allowed strong Th1 response marked by generation of high levels of IFN-gamma but also reduced activation induced T cell death. Similarly, CuNG treatment of peripheral blood monocytes from chemotherapy and/or radiotherapy refractory cancer patients also modulated their cytokine status. Most intriguingly, CuNG treated TAMs could influence reprogramming of TGF-beta producing CD4(+)CD25(+) T cells toward IFN-gamma producing T cells.Our results show the potential usefulness of CuNG in immunotherapy of drug-resistant cancers through reprogramming of TAMs that in turn reprogram the T cells and reeducate the T helper function to elicit proper anti-tumorogenic Th1 response leading to effective reduction in tumor growth