A 2-dimensional computational model to analyze the effects of cellular heterogeinity on cardiac pacemaking

The mechanical action of the heart is made possible in response to electrical events that involve the cardiac cells, a property that classifies the heart tissue between the excitable tissues. At the cellular level, the electrical event is the signal that triggers the mechanical contraction, inducing a transient increase in intracellular calcium which, in turn, carries the message of contraction to the contractile proteins of the cell. The primary goal of my project was to implement in CUDA (Compute Unified Device Architecture, an hardware architecture for parallel processing created by NVIDIA) a tissue model of the rabbit sinoatrial node to evaluate the heterogeneity of its structure and how that variability influences the behavior of the cells. In particular, each cell has an intrinsic discharge frequency, thus different from that of every other cell of the tissue and it is interesting to study the process of synchronization of the cells and look at the value of the last discharge frequency if they synchronized.

In-situ detection of defect formation in organic flexible electronics by Kelvin Probe Force Microscopy

Organic semiconductor technology has attracted considerable research interest in view of its great promise for large area, lightweight, and flexible electronics applications. Owing to their advantages in processing and unique physical properties, organic semiconductors can bring exciting new opportunities for broad-impact applications requiring large area coverage, mechanical flexibility, low-temperature processing, and low cost. In order to achieve highly flexible device architecture it is crucial to understand on a microscopic scale how mechanical deformation affects the electrical performance of organic thin film devices. Towards this aim, I established in this thesis the experimental technique of Kelvin Probe Force Microscopy (KPFM) as a tool to investigate the morphology and the surface potential of organic semiconducting thin films under mechanical strain. KPFM has been employed to investigate the strain response of two different Organic Thin Film Transistor with active layer made by 6,13-bis(triisopropylsilylethynyl)-pentacene (TIPS-Pentacene), and Poly(3-hexylthiophene-2,5-diyl) (P3HT). The results show that this technique allows to investigate on a microscopic scale failure of flexible TFT with this kind of materials during bending. I find that the abrupt reduction of TIPS-pentacene device performance at critical bending radii is related to the formation of nano-cracks in the microcrystal morphology, easily identified due to the abrupt variation in surface potential caused by local increase in resistance. Numerical simulation of the bending mechanics of the transistor structure further identifies the mechanical strain exerted on the TIPS-pentacene micro-crystals as the fundamental origin of fracture. Instead for P3HT based transistors no significant reduction in electrical performance is observed during bending. This finding is attributed to the amorphous nature of the polymer giving rise to an elastic response without the occurrence of crack formation.

Gpu-based many-core architecture emulation: A double level approach

The efficient emulation of a many-core architecture is a challenging task, each core could be emulated through a dedicated thread and such threads would be interleaved on an either single-core or a multi-core processor. The high number of context switches will results in an unacceptable performance. To support this kind of application, the GPU computational power is exploited in order to schedule the emulation threads on the GPU cores. This presents a non trivial divergence issue, since GPU computational power is offered through SIMD processing elements, that are forced to synchronously execute the same instruction on different memory portions. Thus, a new emulation technique is introduced in order to overcome this limitation: instead of providing a routine for each ISA opcode, the emulator mimics the behavior of the Micro Architecture level, here instructions are date that a unique routine takes as input. Our new technique has been implemented and compared with the classic emulation approach, in order to investigate the chance of a hybrid solution.

Parallelization of the algorithm WHAM with NVIDIA CUDA.

The aim of my thesis is to parallelize the Weighting Histogram Analysis Method (WHAM), which is a popular algorithm used to calculate the Free Energy of a molucular system in Molecular Dynamics simulations. WHAM works in post processing in cooperation with another algorithm called Umbrella Sampling. Umbrella Sampling has the purpose to add a biasing in the potential energy of the system in order to force the system to sample a specific region in the configurational space. Several N independent simulations are performed in order to sample all the region of interest. Subsequently, the WHAM algorithm is used to estimate the original system energy starting from the N atomic trajectories. The parallelization of WHAM has been performed through CUDA, a language that allows to work in GPUs of NVIDIA graphic cards, which have a parallel achitecture. The parallel implementation may sensibly speed up the WHAM execution compared to previous serial CPU imlementations. However, the WHAM CPU code presents some temporal criticalities to very high numbers of interactions. The algorithm has been written in C++ and executed in UNIX systems provided with NVIDIA graphic cards. The results were satisfying obtaining an increase of performances when the model was executed on graphics cards with compute capability greater. Nonetheless, the GPUs used to test the algorithm is quite old and not designated for scientific calculations. It is likely that a further performance increase will be obtained if the algorithm would be executed in clusters of GPU at high level of computational efficiency. The thesis is organized in the following way: I will first describe the mathematical formulation of Umbrella Sampling and WHAM algorithm with their apllications in the study of ionic channels and in Molecular Docking (Chapter 1); then, I will present the CUDA architectures used to implement the model (Chapter 2); and finally, the results obtained on model systems will be presented (Chapter 3).