Speeding Up a Large-Scale Filter

Speeding up GW Calculations to Meet the Challenge of Large Scale Quasiparticle Predictions

Although the GW approximation is recognized as one of the most accurate theories for predicting materials excited states properties, scaling up conventional GW calculations for large systems remains a major challenge. We present a powerful and simple-to-implement method that can drastically accelerate fully converged GW calculations for large systems, enabling fast and accurate quasiparticle ca...

Speeding Up Neural Networks for Large Scale Classification using WTA Hashing

In this paper we propose to use the Winner Takes All hashing technique to speed up forward propagation and backward propagation in fully connected layers in convolutional neural networks. The proposed technique reduces significantly the computational complexity, which in turn, allows us to train layers with a large number of kernels with out the associated time penalty. As a consequence we are ...

Speeding Up Finite Element Wave Propagation for Large-Scale Earthquake Simulations

This paper describes the implementation and performance of a new approach to finite element earthquake simulations that represents a speedup factor of 3x in the total solving time employed by Hercules—the octree-based earthquake simulator developed by the Quake Group at Carnegie Mellon University. This gain derives from applying an efficient method for computing the stiffness contribution at th...

Speeding up maximal fully-correlated itemsets search in large databases

Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. Our previous work on the maximal fully-correlated itemset (MFCI) framework can rule out the itemsets with irrelevant items and its downward-closed property helps to achieve good computational performance. However, to calculate the desired MFCIs in large databases,...

Production Scale Up of Nanofibers: A Review