Nancy Hitschfeld - DCC

Nancy Hitschfeld
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Nancy Hitschfeld

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Computer Science - Distributed; Parallel; and Cluster Computing (4)
Physics - Medical Physics (2)
Computer Science - Architecture (1)

Publications Authored By Nancy Hitschfeld

A novel parallel simulation algorithm on the GPU, implemented in CUDA and C++, is presented for the simulation of Brownian particles that display excluded volume repulsion and interact with long and short range forces. When an explicit Euler-Maruyama integration step is performed to take into account the pairwise forces and Brownian motion, particle overlaps can appear. The excluded volume property brings up the need for correcting these overlaps as they happen, since predicting them is not feasible due to the random displacement of Brownian particles. Read More

There is a stage in the GPU computing pipeline where a grid of thread-blocks, in \textit{parallel space}, is mapped onto the problem domain, in \textit{data space}. Since the parallel space is restricted to a box type geometry, the mapping approach is typically a $k$-dimensional bounding box (BB) that covers a $p$-dimensional data space. Threads that fall inside the domain perform computations while threads that fall outside are discarded at runtime. Read More

The study of data-parallel domain re-organization and thread-mapping techniques are relevant topics as they can increase the efficiency of GPU computations when working on spatial discrete domains with non-box-shaped geometry. In this work we study the potential benefits of applying a succint data re-organization of a tetrahedral data-parallel domain of size $\mathcal{O}(n^3)$ combined with an efficient block-space GPU map of the form $g:\mathbb{N} \rightarrow \mathbb{N}^3$. Results from the analysis suggest that in theory the combination of these two optimizations produce significant performance improvement as block-based data re-organization allows a coalesced one-to-one correspondence at local thread-space while $g(\lambda)$ produces an efficient block-space spatial correspondence between groups of data and groups of threads, reducing the number of unnecessary threads from $O(n^3)$ to $O(n^2\rho^3)$ where $\rho$ is the linear block-size and typically $\rho^3 \ll n$. Read More

There is a stage in the GPU computing pipeline where a grid of thread-blocks is mapped to the problem domain. Normally, this grid is a k-dimensional bounding box that covers a k-dimensional problem no matter its shape. Threads that fall inside the problem domain perform computations, otherwise they are discarded at runtime. Read More

This chapter aims at introducing and discussing the techniques for the generation of 3D Finite Element Meshes of human organs. The field of computer assisted surgery is more specifically addressed. Read More


Neurosurgery interventions involve complex tracking systems because a tissue deformation takesplace. The neuronavigation system relies only on preoperative images. In order to overcome the soft tissue deformations and guarantee the accuracy of the navigation a biomechanical model can be used during surgery to simulate the deformation of the brain. Read More