SUMMARYThe modeling of the electrical activity of the heart is of great medical and scientific interest, because it provides a way to get a better understanding of the related biophysical phenomena, allows the development of new techniques for diagnoses and serves as a platform for drug tests. The cardiac electrophysiology may be simulated by solving a partial differential equation coupled to a system of ordinary differential equations describing the electrical behavior of the cell membrane. The numerical solution is, however, computationally demanding because of the fine temporal and spatial sampling required. The demand for real-time high definition 3D graphics made the new graphic processing units (GPUs) a highly parallel, multithreaded, many-core processor with tremendous computational horsepower. It makes the use of GPUs a promising alternative to simulate the electrical activity in the heart. The aim of this work is to study the performance of GPUs for solving the equations underlying the electrical activity in a simple cardiac tissue. In tests on 2D cardiac tissues with different cell models it is shown that the GPU implementation runs 20 times faster than a parallel CPU implementation running with 4 threads on a quad-core machine, parts of the code are even accelerated by a factor of 180.

Abstract. This paper presents a tool for prototyping ODE (Ordinary Differential Equations) based systems in the area of computational modeling. The models, tailored during the project step of the system development, are recorded in MathML, a markup language built upon XML. This design choice improves interoperability with other tools used for mathematical modeling, mainly considering that it is based on Web architecture. The resulting work is a Web portal that transforms an ODE model documented in MathML to a C++ API that offers numerical solutions for that model.

The use of the GPU as a general purpose processor is becoming more popular and there are different approaches for this kind of programming. In this paper we present a comparison between different implementations of the OpenGL and CUDA approaches for solving our test case, a weighted Jacobi iteration with a structured matrix originating from a finite element discretization of the elliptic PDE part of the cardiac bidomain equations. The CUDA approach using textures showed to be the fastest with a speedup of 31 over a CPU implementation using one core and SSE. CUDA showed to be an efficient and easy way of programming GPU for general purpose problems, though it is also easier to write inefficient codes.

Figure 1: Interpreting a sketched geologic map to generate a 3D geologic model. From left to right, sketched geologic map, generated 3D model, and exploded view of the model. AbstractConstructing 3D geological models is a fundamental task in oil/gas exploration and production. A critical stage in the existing 3D geological modeling workflow is moving from a geological interpretation (usually 2D) to a 3D geological model. The construction of 3D geological models can be a cumbersome task mainly because of the models' complexity, and inconsistencies between the interpretation and modeling tasks. To narrow the gap between interpretation and modeling tasks, we propose a sketched based approach. Our main goal is to mimic how domain experts interpret geological structures and allow the creation of models directly from the interpretation task, therefore avoiding the drawbacks of a separate modeling stage. Our sketch-based modeler is based on standard annotations of 2D geological maps and on geologists' interpretation sketches. Specific geological rules and constraints are applied and evaluated during the sketch-based modeling process to guarantee the construction of a valid 3D geologic model.

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