We present Monarch,a framework-agnostic storage middleware that transparently employs storage tiering to accelerate Deep Learning (DL) training. It leverages existing storage tiers of modern supercomputers (i.e., compute node's local storage and shared parallel file system (PFS)), while considering the I/O patterns of DL frameworks to improve data placement across tiers. Monarchaims at accelerating DL training and decreasing the I/O pressure imposed over the PFS. We apply Monarchto TensorFlow and PyTorch, while validating its performance and applicability under different models and dataset sizes. Results show that, even when the training dataset can only be partially stored at local storage, Monarchreduces TensorFlow's and PyTorch's training time by up to 28% and 37% for I/O-intensive models, respectively. Furthermore, Monarchdecreases the number of I/O operations submitted to the PFS by up to 56%.

Recent advances in parallel computing have made it possible for scientists to perform atomistic simulations of materials involving billions of atoms. An immersive and interactive virtual environment such as ImmersaDesk is an ideal platform for exploring complex material processes in these simulations. However rendering such large datasets at an interactive speed is a major challenge. To solve this problem we have developed a visualization system by incorporating parallel and distributed computing paradigms. The system uses a parallelized fast visibility-culling algorithm based on the octree data structure to reduce the number of atoms sent to the graphics pipeline. An adaptive multiresolution algorithm based on atomic density is employed to further reduce the load on the graphics pipeline. The resulting system renders a billion-atom system at nearly interactive frame rates on a dual processor SGI Onyx2 with an InfiniteReality2 graphics pipeline connected to a 4-node PC cluster.