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3. A Survey of Potential MPI Complex Collectives: Large-Scale Mining and Analysis of HPC Applications

Author

Haghi, Pouya, Marshall, Ryan, Chen, Po Hao, Skjellum, Anthony, and Herbordt, Martin

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Offload of MPI collectives to network devices, e.g., NICs and switches, is being implemented as an effective mechanism to improve application performance by reducing inter- and intra-node communication and bypassing MPI software layers. Given the rich deployment of accelerators and programmable NICs/switches in data centers, we posit that there is an opportunity to further improve performance by extending this idea (of in-network collective processing) to a new class of more complex collectives. The most basic type of complex collective is the fusion of existing collectives. In previous work we have demonstrated the efficacy of this additional hardware and software support and shown that it can substantially improve the performance of certain applications. In this work we extend this approach. We seek to characterize a large number of MPI applications to determine overall applicability, both breadth and type, and so provide insight for hardware designers and MPI developers about future offload possibilities. Besides increasing the scope of prior surveys to include finding (potential) new MPI constructs, we also tap into new methods to extend the survey process. Prior surveys on MPI usage considered lists of applications constructed based on application developers' knowledge. The approach taken in this paper, however, is based on an automated mining of a large collection of code sources. More specifically, the mining is accomplished by GitHub REST APIs. We use a database management system to store the results and to answer queries. Another advantage is that this approach provides support for a more complex analysis of MPI usage, which is accomplished by user queries.

Published
2023

5. Multi-Institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB

Author

Brink, Laura, Romero, Ricardo Amaya, Coombs, Laura, Tilkin, Mike, Mazaheri, Sina, Gichoya, Judy, Zaiman, Zachary, Trivedi, Hari, Medina, Adam, Bizzo, Bernardo C., Chang, Ken, Kalpathy-Cramer, Jayashree, Kalra, Mannudeep K., Astuto, Bruno, Ramirez, Carolina, Majumdar, Sharmila, Lee, Amie Y., Lee, Christoph I., Cross, Nathan M., Chen, Po-Hao, Ciancibello, Michael, Chiunda, Allan, Nachand, Douglas, Shah, Chintan, and Wald, Christoph

Published
2025
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6. Big Memory Servers and Modern Approaches to Disk-Based Computation

Author

Chen, Po Hao and Keville, Kurt

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The Big Memory solution is a new computing paradigm facilitated by commodity server platforms that are available today. It exposes a large RAM subsystem to the Operating System and therefore affords application programmers a number of previously unavailable options for data management. Additionally, certain vendor-specific solutions offer additional memory management options that will result in better data reliability and access speeds., Comment: accepted to IEEE HPEC 2021 as an extended abstract

Published
2022

7. Artificial Intelligence–Based Feature Analysis of Pulmonary Vein Morphology on Computed Tomography Scans and Risk of Atrial Fibrillation Recurrence After Catheter Ablation: A Multi-Site Study

Author

Asaeikheybari, Golnoush, El-Harasis, Majd, Gupta, Amit, Shoemaker, M. Benjamin, Barnard, John, Hunter, Joshua, Passman, Rod S., Sun, Han, Kim, Hyun Su, Schilling, Taylor, Telfer, William, Eldridge, Britta, Chen, Po-Hao, Midya, Abhishek, Varghese, Bibin, Harwood, Samuel J., Jin, Alison, Wass, Sojin Y., Izda, Aleksandar, Park, Kevin, Abraham, Abel, Van Wagoner, David R., Tandon, Animesh, Chung, Mina K., and Madabhushi, Anant

Published
2024
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12. Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance

Author

Rudie, Jeffrey D, Duda, Jeffrey, Duong, Michael Tran, Chen, Po-Hao, Xie, Long, Kurtz, Robert, Ware, Jeffrey B, Choi, Joshua, Mattay, Raghav R, Botzolakis, Emmanuel J, Gee, James C, Bryan, R Nick, Cook, Tessa S, Mohan, Suyash, Nasrallah, Ilya M, and Rauschecker, Andreas M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Clinical Research, Neurosciences, Bayes Theorem, Brain, Deep Learning, Humans, Magnetic Resonance Imaging, Radiology, Deep learning, Convolutional neural networks, Neuroradiology, Brain MRI, U-Net, Artificial intelligence, Bayesian inference, Clinical decision support, Augmented performance, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P

Published
2021

16. Modification of gelatin and photocured 3D-printed resin to prepare biomimetic phantoms for ultrasound-guided minimally invasive surgeries

Author

Chen, Chien-Hua, Huang, Yi-Fan, Chen, Po-Hao, Hsu, Yu-Tung, Jaw, Fu-Shan, and Ho, Ming-Hua

Subjects
Crosslinked polymers, 3D printing, Biomimetics, Formaldehyde, Surgery, Engineering and manufacturing industries, Science and technology
Abstract

Abstract The main challenge of ultrasound-guided minimally invasive surgeries is to carry out treatments through small boreholes in the patient's body surface with sub-millimetric accuracy and facility offered by ultrasound [...]

Published
2023
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21. Artificial intelligence for precision education in radiology.

Author

Duong, Michael, Rauschecker, Andreas, Rudie, Jeffrey, Chen, Po-Hao, Cook, Tessa, Bryan, R, and Mohan, Suyash

Subjects
Artificial Intelligence, Bayes Theorem, Clinical Competence, Education, Medical, Graduate, Humans, Radiology, Simulation Training, Teaching, Teaching Materials
Abstract

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institutions practice. The coming age of AI-augmented radiology may enable not only precision medicine but also what we describe as precision medical education, where instruction is tailored to individual trainees based on their learning styles and needs.

Published
2019

27. Toward Data-Driven Radiology Education-Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA).

Author

Chen, Po-Hao, Loehfelm, Thomas W, Kamer, Aaron P, Lemmon, Andrew B, Cook, Tessa S, and Kohli, Marc D

Subjects
Humans, Program Evaluation, Radiology, Internship and Residency, Databases, Factual, Radiology Information Systems, Accreditation, United States, ACGME, Analytics, Big data, Case log, Database, Education, Radiology training, Residency, Networking and Information Technology R&D (NITRD), Biomedical Imaging, Clinical Sciences, Nuclear Medicine & Medical Imaging
Abstract

The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a resident-specific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers.

Published
2016

31. Crayfish: Navigating the Labyrinth of Machine Learning Inference in Stream Processing Systems

Author

Horchidan, Sonia-Florina, Chen, Po Hao, Kritharakis, Emmanouil, Carbone, Paris, Kalavri, Vasiliki, Horchidan, Sonia-Florina, Chen, Po Hao, Kritharakis, Emmanouil, Carbone, Paris, and Kalavri, Vasiliki

Abstract

As Machine Learning predictions are increasingly being used in business analytics pipelines, integrating stream processing with model serving has become a common data engineering task. Despite their synergies, separate software stacks typically handle streaming analytics and model serving. Systems for data stream management do not support ML inference out-of-the-box, while model-serving frameworks have limited functionality for continuous data transformations, windowing, and other streaming tasks. As a result, developers are left with a design space dilemma whose trade-offs are not well understood. This paper presents Crayfish, an extensible benchmarking framework that facilitates designing and executing comprehensive evaluation studies of streaming inference pipelines. We demonstrate the capabilities of Crayfish by studying four data processing systems, three embedded libraries, three external serving frameworks, and two pre-trained models. Our results prove the necessity of a standardized benchmarking framework and show that (1) even for serving tools in the same category, the performance can vary greatly and, sometimes, defy intuition, (2) GPU accelerators can show compelling improvements for the serving task, but the improvement varies across tools, and (3) serving alternatives can achieve significantly different performance, depending on the stream processors they are integrated with., QC 20240507Part of ISBN:978-389318091-2, 978-389318094-3, 978-389318095-0

Published
2024
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39. Forest Survival

Author

Chen, Po-Hao

Subjects
Survival, Twine
Abstract

In Forest Survival, you are traveling to a huge forest but lost in it. Your action will lead you to different consequences.

Published
2021

40. Sarcopenia evaluation on cardiac magnetic resonance imaging in older adults for outcomes prediction following surgical aortic valve replacement

Author

Mirzai, Saeid, primary, Aleixo, Gabriel F.P., additional, Mazumder, Samia, additional, Berglund, Felix, additional, Patil, Meghana, additional, Layoun, Habib, additional, Martens, Pieter, additional, Wang, Tom Kai Ming, additional, Chen, Po-Hao, additional, Svensson, Lars, additional, Tang, W.H. Wilson, additional, and Kwon, Deborah, additional

Published
2023
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47. The Association of Body Composition and Outcomes Following Autologous Hematopoietic Stem Cell Transplantation in Patients with Non-Hodgkin Lymphoma

Author

Aleixo, Gabriel, primary, Wei, Wei, additional, Chen, Po Hao, additional, Gandhi, Namita, additional, Anwer, Faiz, additional, Dean, Robert, additional, Hamilton, Betty K., additional, Hill, Brian, additional, Jagadeesh, Deepa, additional, Khouri, Jack, additional, Pohlman, Brad, additional, Sobecks, Ronald, additional, Winter, Allison, additional, Caimi, Paolo, additional, and Majhail, Navneet, additional

Published
2023
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