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

An Ultrahigh Vacuum Complementary Metal Oxide Silicon Compatible nonlithographic System to Fabricate nanoparticle-based Devices

Nanoparticles of metals and semiconductors are promising for the implementation of a variety of photonic and electronic devices with superior performances and new functionalities. However, their successful implementation has been limited due to the lack of appropriate fabrication processes that are suitable for volume manufacturing. The current techniques for the fabrication of nanoparticles either are solution based, thus requiring complex surface passivation, or have severe constraints over the choice of particle size and material. We have developed an ultrahigh vacuum system for the implementation of a complex nanosystem that is flexible and compatible with the silicon integrated circuit process, thus making it suitable for volume manufacturing. The system also allows the fabrication of Ohmic contacts and isolation dielectrics in an integrated manner, which is a requirement for most electronic and photonic devices. We have demonstrated the power and the flexibility of this new system for the manufacturing of nanoscale devices by implementing a variety of structures incorporating nanoparticles. Descriptions of this new fabrication system together with experimental results are presented in this article. The system explains the method of size-selected deposition of nanoparticles of any metallic, semiconducting, and (or) insulating materials on any substrate, which is very important in fabricating useful nanoparticle-baseddevices. It has also been shown that at elevated substrate temperature, a selective deposition of the nanoparticles is observed near the grain-boundary regions. However, in these natural systems, there will always be low and favorable energy states present away from the grain-boundary regions, leading to the undesirable deposition of nanoparticles in the far-grain-boundary regions, too

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