Department of Computational and Data Sciences (CDS): Recent submissions
Now showing items 81-100 of 111
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Towards Designing PCM-Conscious Database Systems
Phase Change Memory (PCM) is a recently developed non-volatile memory technology that is expected to provide an attractive combination of the best features of conventional disks (persistence, capacity) and of DRAM (access ... -
Improving Data Center Utilisation by Reducing Fragmentation
Virtualization enables better server consolidation and utilisation compared to stand-alone servers running a single workload. This enabled wide-spread cloud adoption among many organizations. Data center utilisation is ... -
A Divide and Conquer Framework For Graph Processing in Distributed Heterogeneous Systems
In many fields of science and engineering graph data structures are used to represent real-world information. As these graphs scale in size, it becomes very inefficient to process these graphs on a single core CPU using ... -
EMF: System Design and Challenges for Disaggregated GPUs in datacenters for Efficiency, Modularity and Flexibility
With Dennard Scaling phasing out in the mid-2000s, architectural scaling and hardware specialization take centre stage to provide performance bene fits with already stalling Moore's law. An outcome from this hardware ... -
Coarse-grained dynamics derived structural ensemble for prediction of metal binding sites of protein and phenotypic effects of variants
Structures of proteins play a key role in determining their functions. Knowledge of structure, especially the details of specific sites of a protein can help us understand their contribution to the overall activity. ... -
A Numerical Method for Modeling Light Scattering from Spherical Particles with a Probability Density in the Parametric Space
With modern computational tools, modeling light scattering from a particle with known physical parameters has become relatively trivial. Evaluation using analytical solutions is highly eficient compared to a full ... -
Vector Extrapolation and Guided Filtering Methods for Improving Photoacoustic and Microscopic Images
Photoacoustic imaging is a noninvasive imaging modality which combines the bene ts of optical contrast and ultrasonic resolution. It is applied widely for monitoring tissue health conditions in the elds of cardiology, ... -
Exploring the Inherent Saliency in Visual Data through Convolutional Neural Networks
Saliency plays a key role in various computer vision tasks. Extracting salient regions from images and videos has been a well-established problem in computer vision. Determining salient regions in an image or video has ... -
Numerical Analysis of Some Preconditioners and Associated Error Estimators for Solving Linear Systems
Convergence of iterative algorithms in solving large linear systems is largely affected by the condition number of the matrix. Preconditioners reduce the condition number of the system matrix, thereby letting the linear ... -
Learning Across Domains: Applications to Text-based Person Search and Multi-Source Domain Adaptation
With rapid development in technology and ubiquitous presence of diverse types of sensors, a large amount of data from different modalities (e.g., text, audio, images etc.) describing the same person/ object/event has ... -
Scalability Bottleneck Analysis of High Performance Applications
Obtaining high performance and scalability for high performance applications are challenging. There are various bottlenecks including, higher rate of memory access, complex algorithm, high rate of communication, big ... -
Towards Learning Adversarially Robust Deep Learning Models
Deep learning models have shown impressive performance across a wide spectrum of computer vision applications, including medical diagnosis and autonomous driving. One of the major concerns that these models face is their ... -
Stability Preserving Bisection Algorithms in Reaction-Diffusion Complex Networks
Reaction-Diffusion complex networks are ubiquitous in many pragmatic models of network of interacting nodes with individual dynamics, such as social interactions, neuronal functions, transportation models, ecological ... -
Parallel Smoothers for Multigrid Method in Heterogeneous CPU-GPU Environment
Real-world applications require the solution of large a sparse system of algebraic equations that arise from the discretization of partial di erential equations with the help of supercomputers. Modern supercomputers are ... -
A Hierarchical Control Plane Framework for Integrated SDN-SFC Management in Multi-tenant Cloud Datacenters
Cloud data centers represent one of the most complex and dynamic environments in terms of network management. The multitude of hosted applications in such centers share the same fabric and yet demand easy and fast service ... -
Improving photoacoustic imaging with model compensating and deep learning methods
Photoacoustic imaging is a hybrid biomedical imaging technique combining optical ab- sorption contrast with ultrasonic resolution. It is a non-invasive technique that is scalable to reveal structural, functional, and ... -
Global control of mechanics on Riemannian manifolds, and applications to under-actuated aerial vehicles
We consider the problem of designing trajectory tracking feedback control laws for La- grangian mechanical systems in a Riemannian geometric framework. Classical nonlinear control techniques that rely on Euclidean ... -
An Accelerator for Machine Learning Based Classifiers
Artificial Neural Networks (ANNs) are algorithmic techniques that simulate biological neural systems. Typical realization of ANNs are software solutions using High Level Languages (HLLs) such as C, C++, etc. Such solutions ... -
Feasible Path Prescription for Engineering Systems in a High-Index Constrained Dynamics Framework
Constrained dynamic performance and control models of complex engineering systems can be represented in the form of a Differential Algebraic Equation (DAE) system. The high-index of this DAE system poses computational ... -
Benchmarking and Scheduling Strategies for Distributed Stream Processing
(2018-08-20)The velocity dimension of Big Data refers to the need to rapidly process data that arrives continuously as streams of messages or events. Distributed Stream Processing Systems (DSPS) refer to distributed programming and ...