Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions from tens of micrometers to a few nanometers. In order to resolve sample features at smaller length scales, however, a higher radiation dose is required. Therefore, the limitation on the achievable resolution is set primarily by noise at these length scales. We present TomoGAN, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases the angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose on the sample. Evaluation with both simulated and experimental datasets shows that our approach can significantly reduce noise in reconstructed images, improving the structural similarity score of simulation and experimental data from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with the simultaneous iterative reconstruction technique, showing the computational superiority of our approach.
Abstract-There is a clear trend towards using cloud resources in the scientific or the HPC community, with a key attraction of cloud being the elasticity it offers. In executing HPC applications on a cloud environment, it will clearly be desirable to exploit elasticity of cloud environments, and increase or decrease the number of instances an application is executed on during the execution of the application, to meet time and/or cost constraints. Unfortunately, HPC applications have almost always been designed to use a fixed number of resources.This paper describes our initial work towards the goal of making existing MPI applications elastic for a cloud framework. Considering the limitations of the MPI implementations currently available, we support adaptation by terminating one execution and restarting a new program on a different number of instances. The components of our envisioned system include a decision layer which considers time and cost constraints, a framework for modifying MPI programs, and a cloud-based runtime support that can enable redistributing of saved data, and support automated resource allocation and application restart on a different number of nodes.Using two MPI applications, we demonstrate the feasibility of our approach, and show that outputting, redistributing, and reading back data can be a reasonable approach for making existing MPI applications elastic.
Compute cycles in high performance systems are increasing at a much faster pace than both storage and widearea bandwidths. To continue improving the performance of large-scale data analytics applications, compression has therefore become promising approach. In this context, this paper makes the following contributions. First, we develop a new compression methodology, which exploits the similarities between spatial and/or temporal neighbors in a popular climate simulation dataset and enables high compression ratios and low decompression costs. Second, we develop a framework that can be used to incorporate a variety of compression and decompression algorithms. This framework also supports a simple API to allow integration with an existing application or data processing middleware. Once a compression algorithm is implemented, this framework automatically mechanizes multi-threaded retrieval, multi-threaded data decompression, and the use of informed prefetching and caching. By integrating this framework with a data-intensive middleware, we have applied our compression methodology and framework to three applications over two datasets, including the Global Cloud-Resolving Model (GCRM) climate dataset. We obtained an average compression ratio of 51.68%, and up to 53.27% improvement in execution time of data analysis applications by amortizing I/O time by moving compressed data.
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