Cone-beam computed tomography (CBCT) is a noninvasive x-ray imaging technique with multiple clinical applications (orthopedics, dentistry, radiation therapy, etc.). These systems can provide sub-millimeter resolution in images of high diagnostic quality with short scanning times. However, CBCT scans require subjecting the patient with a sufficient radiation dose to obtain a high-quality image. Although relatively low, some clinical applications such as image guidance in radiotherapy (IGRT) require obtaining CBCT daily. This repeated exposure to x-ray radiation outside the treatment volume can have detrimental effects and may increase the lifetime risk of a secondary malignancy in younger patients. Hence, there is a clinical need to reduce CBCT imaging dose. However, images produced with a reduced dose often have greater noise and artifacts which deteriorate CBCT image quality, reducing their clinical efficacy. Deep learning-based methods have become a popular choice showing impressive performance for noise reduction in low-dose CBCT. The success of these methods critically depends on the availability of paired images for training which is a major obstacle for most applications. Recently, the Noise2Inverse method was designed specifically for denoising computed tomography (CT) data without requiring paired noisy and clean images. While the Noise2Inverse method was shown to reduce noise in challenging real-world experimental datasets, it processes 3D-CT volumes as individual 2D images. In this work, we extend the Noise2Inverse method to work directly with 3D-CBCT volumes requiring the use of high-end HPC resources. We compare the results between a 2D and 3D UNet++ model utilizing the ICASSP-2024 3D Low Dose CBCT Grand Challenge dataset with CBCT data corresponding to two dose levels, clinical and low-dose. Experimental results show that both 2D and 3D models can significantly reduce the noise in CBCT increasing the SSIM up to 148.05% compared to the low-dose FDK reconstruction. Furthermore, the 3D model improves the SSIM by 0.26% and 1.46% for clinical- and low-dose, respectively, over the 2D model.

The use of data produced by scientific instruments, such as the Advanced Photon Source Upgrade (APS-U), to train and fine-tune machine learning models is becoming increasingly challenging due to high data production rates, large data volumes, and the growing complexity of machine learning models. To address these challenges, researchers have developed frameworks like fairDMS to efficiently organize vast amounts of data and models for rapid querying when model degradation is detected. However, the complexity of these frameworks and the physically distributed nature of experimental facilities complicate their deployment.Here we introduce a high-performance model and data management framework for machine learning, M2ML. In contrast to previous frameworks, M2ML abstracts the tasks into three key elements that can be easily called and accessed by users. M2ML is capable of utilizing a variety of computational resources, that are distributed across scientific facilities, to accelerate machine learning tasks. For example, it can automatically transfer data from an experimental facility (such as APS-U) to a high performance computing (HPC) facility (such as the Argonne Leadership Computing Facility (ALCF)), train machine learning models at the HPC facility, and deploy the trained models on edge computing devices back at the experimental facility for inferencing. M2ML provides a unified interface for (on-the-fly) model (re)training, storage, evaluation, fine-tuning, and inferencing using heterogeneous resources that can be geographically distributed. M2ML uses Globus services such as Globus Transfer and Globus Compute (formerly FuncX). We evaluate M2ML using a high energy diffraction microscopy (HEDM) workflow that employs BraggNN to predict the diffraction peak locations. Results show that, although the BraggNN model is small, M2ML can significantly accelerate the workflow through selective assignment of tasks to different computing resources.

The demands of increasingly large scientific application workflows lead to the need for more powerful supercomputers. As the scale of supercomputing systems have grown, the prediction of fault tolerance has become an increasingly critical area of study, since the prediction of system failures can improve performance by saving checkpoints in advance. We propose a real-time failure detection algorithm that adopts an event-based prediction model. The prediction model is a convolutional neural network that utilizes both traditional event attributes and additional spatio-temporal features. We present a case study using our proposed method with six years of reliability, availability, and serviceability event logs recorded by Mira, a Blue Gene/Q supercomputer at Argonne National Laboratory. In the case study, we have shown that our failure prediction model is not limited to predict the occurrence of failures in general. It is capable of accurately detecting specific types of critical failures such as coolant and power problems within reasonable lead time ranges. Our case study shows that the proposed method can achieve a F1score of 0.56 for general failures, 0.97 for coolant failures, and 0.86 for power failures.

The scale of scientific data generated by experimental facilities and simulations on high-performance computing facilities has been growing rapidly. In many cases, this data needs to be transferred rapidly and reliably to remote facilities for storage, analysis, sharing etc. At the same time, users want to verify the integrity of the data by doing a checksum after the data has been written to disk at the destination, to ensure the file has not been corrupted, for example due to network or storage data corruption, software bugs or human error. This end-to-end integrity verification creates additional overhead (extra disk I/O and more computation) and increases the overall data transfer time. In this paper, we evaluate strategies to maximize the overlap between data transfer and checksum computation. More specifically, we evaluate file-level and block-level (with various block sizes) pipelining to overlap data transfer and checksum computation. We evaluate these pipelining approaches in the context of GridFTP, a widely used protocol for science data transfers. We conducted both theoretical analysis and real experiments to evaluate our methods. The results show that block-level pipelining is an effective method in maximizing the overlap between data transfer and checksum computation and can improve the overall data transfer time with end-to-end integrity verification by up to 70% compared to the sequential execution of transfer and checksum, and by up to 60% compared to file-level pipelining.