Motion analysis report

Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations

Characterizing Deep-Learning I/O Workloads in TensorFlow

The performance of Deep-Learning (DL) computing frameworks rely on the performance of data ingestion and checkpointing. In fact, during the training, a considerable high number of relatively small files are first loaded and pre-processed on CPUs and then moved to accelerator for computation. In addition, checkpointing and restart operations are carried out to allow DL computing frameworks to restart quickly from a checkpoint. Because of this, I/O affects the performance of DL applications. In this work, we characterize the I/O performance and scaling of TensorFlow, an open-source programming framework developed by Google and specifically designed for solving DL problems. To measure TensorFlow I/O performance, we first design a micro-benchmark to measure TensorFlow reads, and then use a TensorFlow mini-application based on AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow. To improve the checkpointing performance, we design and implement a burst buffer. We find that increasing the number of threads increases TensorFlow bandwidth by a maximum of 2.3x and 7.8x on our benchmark environments. The use of the tensorFlow prefetcher results in a complete overlap of computation on accelerator and input pipeline on CPU eliminating the effective cost of I/O on the overall performance. The use of a burst buffer to checkpoint to a fast small capacity storage and copy asynchronously the checkpoints to a slower large capacity storage resulted in a performance improvement of 2.6x with respect to checkpointing directly to slower storage on our benchmark environment.Comment: Accepted for publication at pdsw-DISCS 201