Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

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Non-volatile memory (NVM) provides a scalable and power-efficient solution to replace dynamic random access memory (DRAM) as main memory. However, because of the relatively high latency and low bandwidth of NVM, NVM is often paired with DRAM to build a heterogeneous memory system (HMS). As a result, data objects of the application must be carefully placed to NVM and DRAM for the best performance. In this paper, we introduce a lightweight runtime solution that automatically and transparently manages data placement on HMS without the requirement of hardware modifications and disruptive change to applications. Leveraging online profiling and performance models, the runtime solution characterizes memory access patterns associated with data objects, and minimizes unnecessary data movement. Our runtime solution effectively bridges the performance gap between NVM and DRAM. We demonstrate that using NVM to replace the majority of DRAM can be a feasible solution for future HPC systems with the assistance of a software-based data management.

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Algorithm-based fault tolerance (ABFT) is a highly efficient resilience solution for many widely-used scientific computing kernels. However, in the context of the resilience ecosystem, ABFT is completely opaque to any underlying hardware resilience mechanisms. As a result, some data structures are over-protected by ABFT and hardware, which leads to redundant costs in terms of performance and energy. In this paper, we rethink ABFT using an integrated view including both software and hardware with the goal of improving performance and energy efficiency of ABFT-enabled applications. In particular, we study how to coordinate ABFT and error-correcting code (ECC) for main memory, and investigate the impact of this coordination on performance, energy, and resilience for ABFT-enabled applications. Scaling tests and analysis indicate that our approach saves up to 25% for system energy (and up to 40% for dynamic memory energy) with up to 18% performance improvement over traditional approaches of ABFT with ECC.

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