PUMA publications for /tag/SimTech%20computingMon Mar 19 16:15:07 CET 2018Proceedings of the IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT'16)21-26{Applying Efficient Fault Tolerance to Enable the Preconditioned Conjugate Gradient Solver on Approximate Computing Hardware}2016AxC CCG PCG SimTech approximate computing conjugate error-correction error-detection fault-tolerance gradient linear myown preconditioned solver sparse systems A new technique is presented that allows to execute the preconditioned conjugate gradient (PCG) solver on approximate hardware while ensuring correct solver results. This technique expands the scope of approximate computing to scientific and engineering applications. The changing error resilience of PCG during the solving process is exploited by different levels of approximation which trade off numerical accuracy and hardware utilization. Such approximation levels are determined at runtime by periodically estimating the error resilience. An efficient fault tolerance technique allows reductions in hardware utilization by ensuring the continued exploitation of maximum allowed energy-accuracy trade-offs. Experimental results show that the hardware utilization is reduced on average by 14.5% and by up to 41.0% compared to executing PCG on accurate hardware.Mon 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 23rd IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS'17)237--239{Energy-efficient and Error-resilient Iterative Solvers for Approximate Computing}2017AxC SimTech approximate computing energy-efficiency fault monitoring myown quality tolerance Iterative solvers like the Preconditioned Conjugate Gradient (PCG) method are widely-used in compute-intensive domains including science and engineering that often impose tight accuracy demands on computational results. At the same time, the error resilience of such solvers may change in the course of the iterations, which requires careful adaption of the induced approximation errors to reduce the energy demand while avoiding unacceptable results. A novel adaptive method is presented that enables iterative Preconditioned Conjugate Gradient (PCG) solvers on Approximate Computing hardware with high energy efficiency while still providing correct results. The method controls the underlying precision at runtime using a highly efficient fault tolerance technique that monitors the induced error and the quality of intermediate computational results.Mon Mar 19 16:15:07 CET 2018Proceedings of the 22nd IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS'16)133--136{Pushing the Limits: How Fault Tolerance Extends the Scope of Approximate Computing}2016AxC SimTech approximate characterization computing fault metrics myown precision tolerance variable Approximate computing in hardware and software promises significantly improved computational performance combined with very low power and energy consumption. This goal is achieved by both relaxing strict requirements on accuracy and precision, and by allowing a deviating behavior from exact Boolean specifications to a certain extent. Today, approximate computing is often limited to applications with a certain degree of inherent error tolerance, where perfect computational results are not always required. However, in order to fully utilize its benefits, the scope of applications has to be significantly extended to other compute-intensive domains including science and engineering. To meet the often rather strict quality and reliability requirements for computational results in these domains, the use of appropriate characterization and fault tolerance measures is highly required. In this paper, we evaluate some of the available techniques and how they may extend the scope of application for approximate computing.Mon 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 computingCommunity for tag(s) SimTech computing