PUMA publications for /tag/SimTech%20computing%20simulationMon Mar 19 16:15:07 CET 2018Proceedings of the IEEE International On-Line Testing Symposium (IOLTS'13)240--243{Efficacy and Efficiency of Algorithm-Based Fault Tolerance on GPUs}2013ABFT GPGPU SimTech algorithm-based computing errors fault fault-tolerance myown scientific simulation Computer simulations drive innovations in science and industry, and they are gaining more and more importance. However, their high computational demand generates extraordinary challenges for computing systems. Typical highperformance computing systems, which provide sufficient performance and high reliability, are extremly expensive. Modern GPUs offer high performance at very low costs, and they enable simulation applications on the desktop. However, they are increasingly prone to transient effects and other reliability threats. To fulfill the strict reliability requirements in scientific computing and simulation technology, appropriate fault tolerance measures have to be integrated into simulation applications for GPUs. Algorithm-Based Fault Tolerance on GPUs has the potential to meet these requirements. In this work we investigate the efficiency and the efficacy of ABFT for matrix operations on GPUs. We compare ABFT against fault tolerance schemes that are based on redundant computations and we evaluate its error detection capabilitiesMon Mar 19 16:15:07 CET 2018Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM'14)424--431{Adaptive Parallel Simulation of a Two-Timescale-Model for Apoptotic Receptor-Clustering on GPUs}2014Euler-Maruyama GPU SimTech adaptive aggregation approximation computing heterogeneous ligand-receptor-model multi-timescale myown parallel particle simulation Computational biology contributes important solutions for major biological challenges. Unfortunately, most applications in computational biology are highly computeintensive and associated with extensive computing times. Biological problems of interest are often not treatable with traditional simulation models on conventional multi-core CPU systems. This interdisciplinary work introduces a new multi-timescale simulation model for apoptotic receptor-clustering and a new parallel evaluation algorithm that exploits the computational performance of heterogeneous CPU-GPU computing systems. For this purpose, the different dynamics involved in receptor-clustering are separated and simulated on two timescales. Additionally, the time step sizes are adaptively refined on each timescale independently. This new approach improves the simulation performance significantly and reduces computing times from months to hours for observation times of several seconds.SimTech computing simulationCommunity for tag(s) SimTech computing simulation