Narayan Ganesan

Computational approaches to spatial orientation: from transfer functions to dynamic Bayesian inference.

Spatial orientation is the sense of body orientation and self-motion relative to the stationary environment, fundamental to normal waking behavior and control of everyday motor actions including eye movements, postural control, and locomotion. The brain achieves spatial orientation by integrating visual, vestibular, and somatosensory signals. Over the past years, considerable progress has been made toward understanding how these signals are processed by the brain using multiple computational approaches that include frequency domain analysis, the concept of internal models, observer theory, Bayesian theory, and Kalman filtering. Here we put these approaches in context by examining the specific questions that can be addressed by each technique and some of the scientific insights that have resulted. We conclude with a recent application of particle filtering, a probabilistic simulation technique that aims to generate the most likely state estimates by incorporating internal models of sensor dynamics and physical laws and noise associated with sensory processing as well as prior knowledge or experience. In this framework, priors for low angular velocity and linear acceleration can explain the phenomena of velocity storage and frequency segregation, both of which have been modeled previously using arbitrary low-pass filtering. How Kalman and particle filters may be implemented by the brain is an emerging field. Unlike past neurophysiological research that has aimed to characterize mean responses of single neurons, investigations of dynamic Bayesian inference should attempt to characterize population activities that constitute probabilistic representations of sensory and prior information.

Sorting on architecturally diverse computer systems

Sorting is an important problem that forms an essential component of many high-performance applications. Here, we explore the design space of sorting algorithms in recon-figurable hardware, looking to maximize the benefit associated with high-bandwidth, multiple-port access to memory. Rather than focus on an individual implementation, we investigate a family of approaches that exploit characteristics fairly unique to reconfigurable hardware.

Decoherence control in open quantum systems via classical feedback

In this work we propose a strategy using techniques from systems theory to completely eliminate decoherence and also provide conditions under which it can be done. A construction employing an auxiliary system, the bait, which is instrumental to decoupling the system from the environment is presented. Our approach to decoherence control in contrast to other approaches in the literature involves the bilinear input affine model of quantum control system which lends itself to various techniques from classical control theory, but with nontrivial modifications to the quantum regime. The elegance of this approach yields interesting results on open loop decouplability and decoherence free subspaces. Additionally, the feedback control of decoherence may be related to disturbance decoupling for classical input affine systems, which entails careful application of the methods by avoiding all the quantum mechanical pitfalls. In the process of calculating a suitable feedback the system must be restructured due to its tensorial nature of interaction with the environment, which is unique to quantum systems. In the subsequent section we discuss a general information extraction scheme to gain knowledge of the state and the amount of decoherence based on indirect continuous measurement. The analysis of continuous measurement on a decohering quantum system has not been extensively studied before. Finally, a methodology to synthesize feedback parameters itself is given, that technology permitting, could be implemented for practical 2-qubit systems to perform decoherence free quantum computing. The results obtained are qualitatively different and superior to the ones obtained via master equations.

Structural, dynamic, and electrostatic properties of fully hydrated DMPC bilayers from molecular dynamics simulations accelerated with graphical processing units (GPUs)

We present results of molecular dynamics simulations of fully hydrated DMPC bilayers performed on graphics processing units (GPUs) using current state‐of‐the‐art non‐polarizable force fields and a local GPU‐enabled molecular dynamics code named FEN ZI. We treat the conditionally convergent electrostatic interaction energy exactly using the particle mesh Ewald method (PME) for solution of Poissons Equation for the electrostatic potential under periodic boundary conditions. We discuss elements of our implementation of the PME algorithm on GPUs as well as pertinent performance issues. We proceed to show results of simulations of extended lipid bilayer systems using our program, FEN ZI. We performed simulations of DMPC bilayer systems consisting of 17,004, 68,484, and 273,936 atoms in explicit solvent. We present bilayer structural properties (atomic number densities, electron density profiles), deuterium order parameters (SCD), electrostatic properties (dipole potential, water dipole moments), and orientational properties of water. Predicted properties demonstrate excellent agreement with experiment and previous all‐atom molecular dynamics simulations. We observe no statistically significant differences in calculated structural or electrostatic properties for different system sizes, suggesting the small bilayer simulations (less than 100 lipid molecules) provide equivalent representation of structural and electrostatic properties associated with significantly larger systems (over 1000 lipid molecules). We stress that the three system size representations will have differences in other properties such as surface capillary wave dynamics or surface tension related effects that are not probed in the current study. The latter properties are inherently dependent on system size. This contribution suggests the suitability of applying emerging GPU technologies to studies of an important class of biological environments, that of lipid bilayers and their associated integral membrane proteins. We envision that this technology will push the boundaries of fully atomic‐resolution modeling of these biological systems, thus enabling unprecedented exploration of meso‐scale phenomena (mechanisms, kinetics, energetics) with atomic detail at commodity hardware prices.

Accelerating HMMER on GPUs by implementing hybrid data and task parallelism

Many biologically motivated problems are expressed as dynamic programming recurrences and are difficult to parallelize due to the intrinsic data dependencies in their algorithms. Therefore their solutions have been sped up using task level parallelism only. Emerging platforms such as GPUs are appealing parallel architectures for high-performance; at the same time they are a motivation to rethink the algorithms associated with these problems, to extract finer-grained parallelism such as data parallelism. In this paper, we consider the hmmersearch program as a representative of these problems and we re-design its computational algorithm to extract data parallelism for a more efficient execution on emerging platforms, despite the fact that hmmersearch has data dependencies. Our approach outperforms other existing methods when searching a very large database of unsorted sequences on GPUs.

FENZI: GPU-Enabled Molecular Dynamics Simulations of Large Membrane Regions Based on the CHARMM Force Field and PME

When studying membrane-bound protein receptors, it is necessary to move beyond the current state-of-the-art simulations that only consider small membrane patches and implicit solvent. Limits of traditional computer platforms negatively impact the models level of realism and the computational scales achievable. On the other hand, multi-core platforms such as GPUs offer the possibility to span length scales in membrane simulations much larger and with higher resolutions than before. To this end, this paper presents the design and implementation of an advanced GPU algorithm for Molecular Dynamics (MD) simulations of large membrane regions in the NVT, NVE, and NPT ensembles using explicit solvent and Particle Mesh Ewald (PME) method for treating the conditionally-convergent electrostatic component of the classical force field. A key component of our algorithm is the redesign of the traditional PME method to better fit on the multithreading GPU architecture. This has been considered a fundamentally hard problem in the molecular dynamics community working on massively multithreaded architecture. Our algorithm is integrated in the code FENZI (textit{yun dong de FEN ZI} in Mandarin or textit{moving molecules} in English). The paper analyzes both the performance and accuracy of large-scale GPU-enabled simulations of membranes using FENZI, showing how our code can enable multi-nanosecond MD simulations per day, even when using PME.

GPU-Enabled Macromolecular Simulation: Challenges and Opportunities

GPU-enabled simulation of fully atomistic macromolecular systems is rapidly gaining momentum, enabled by massive parallelism and the parallelizability of various components of the underlying algorithms and methodologies. Here, we consider key aspects required for obtaining realistic macromolecular systems specifically adapted to GPUs; these aspects include realistic mathematical models and valid simulations.

Simulation and study of large-scale bacteria-materials interactions via BioScape enabled by GPUs

Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biological systems is a computationally intensive challenge. We present a GPU based simulation framework in a reactive environment in 3D space, along with the modeling language, BioScape, in order to describe various biological processes. We also present an efficient computational framework to study the interactions enabled by the massively parallel processing capability of the GPUs. Our driving example is a bio-triggered drug delivery system for infection-resistant medical implants. The modeling and simulation framework presented here will help in identifying biological targets and materials to treat biomaterials associated bacterial infections. The computational framework will offer a deeper insight into various biological processes compared to traditional modeling via implicit differential equations, and help us observe the key events as they unfold.

Quantum internal model principle and enhanced disturbance decoupling

In this article we present the Quantum Internal Model Principle which is a consequence of theory of Disturbance Decoupling for quantum systems, the applications of which extends to control of decoherence in open quantum systems. In the process of formulating a disturbance rejection scheme for quantum systems we arrive at the Internal Model Principle for quantum control systems which is first of its kind in the literature. The internal model principle relates the disturbance rejecting control to the model of the environmental interaction and provides conditions under which a quantum system could be successfully and completely decoupled from the environmental interactions. Although the theory of quantum disturbance decoupling is inspired from classical decoupling, the vagaries of quantum mechanics entails non-trivial modifications and redesign of the construction of decoupling control. Such a modification leads to an enhanced disturbance rejection scheme for the classical systems as well.In this article, we study the problem of designing a Decohere nce Control for quantum systems with the help of a scalable ancillary quantum control and techniq ues from geometric control theory, in order to successfully andcompletelydecouple an open quantum system from its environment. We reformulate the problem of decoherence control as a disturbance rejecti on scheme which also leads us to the idea of Internal Model Principlefor quantum control systems which is first of its kind in the li t rature. It is shown that decoupling a quantum disturbance from an ope quantum system, is possible only with the help of a quantum controller which takes into account the model of the environmental interaction. This is demonstrated for a simple 2-qubit system wherein the eff cts of decoherence are completely eliminated. The theory provides conditions to be imposed on the controller to ensure perfect decoupling. Hence the problem of decoherence control naturally gives ri se to the quantum internal model principle which relates the disturbance rejecting control to the mode l f the environmental interaction. Classical internal model principle and disturbance decoup ling focus on different aspects viz. perfect output tracking and complete decoupling of output from exte rnal disturbances respectively. However for quantum systems, the two problems come together and merge in order to produce an effective platform for decoherence control. In this article we introduce a semi nal connection between disturbance decoupling and the corresponding analog for internal model principle f or quantum systems.

Control of decoherence in open quantum systems using feedback

Quantum feedback is assuming increasingly im- portant role in quantum control and quantum information processing. In this work we analyze the application of such feedback techniques in eliminating decoherence in open quan- tum systems. In order to apply such system theoretic methods we first analyze the invariance properties of quadratic forms which corresponds to expected value of a measurement and present conditions for decouplability of measurement outputs of such time-varying open quantum systems from environmental effects.