Michael Albrecht

In recent years, there has been a renewed interest in languages and systems for large scale distributed computing. Unfortunately, most systems available to the end user use a custom description language tightly coupled to a specific runtime implementation, making it difficult to transfer applications between systems. To address this problem we introduce Makeflow, a simple system for expressing and running a data-intensive workflow across multiple execution engines without requiring changes to the application or workflow description. Makeflow allows any user familiar with basic Unix Make syntax to generate a workflow and run it on one of many supported execution systems. Furthermore, in order to assess the performance characteristics of the various execution engines available to users and assist them in selecting one for use we introduce Workbench, a suite of benchmarks designed for analyzing common workflow patterns. We evaluate Workbench on two physical architectures -- the first a storage cluster with local disks and a slower network and the second a high performance computing cluster with a central parallel filesystem and fast network -- using a variety of execution engines. We conclude by demonstrating three applications that use Makeflow to execute data intensive applications consisting of thousands of jobs.

Abstract Objective This study evaluates the impact of an ambient artificial intelligence (AI) documentation platform on clinicians’ perceptions of documentation workflow. Materials and Methods An anonymous pre- and non-anonymous post-implementation survey evaluated ambulatory clinician perceptions on impact of Abridge, an ambient AI documentation platform. Outcomes included clinical documentation burden, work after-hours, clinician burnout, and work satisfaction. Data were analyzed using descriptive statistics and proportional odds logistic regression to compare changes for concordant questions across pre- and post-surveys. Covariate analysis examined effect of specialty type and duration of AI tool usage. Results Survey response rates were 51.9% (93/181) pre-implementation and 74.4% (99/133) post-implementation. Clinician perception of ease of documentation workflow (OR = 6.91, 95% CI: 3.90-12.56, P <.001) and in completing notes associated with usage of the AI tool (OR = 4.95, 95% CI: 2.87-8.69, P <.001) was significantly improved. Most respondents agreed that the AI tool decreased documentation burden, decreased the time spent documenting outside clinical hours, reduced burnout risk, and increased job satisfaction, with 48% agreeing that an additional patient could be seen if needed. Clinician specialty type and number of days using the AI tool did not significantly affect survey responses. Discussion Clinician experience and efficiency was improved with use of Abridge across a breadth of specialties. Conclusion An ambient AI documentation platform had tremendous impact on improving clinician experience within a short time frame. Future studies should utilize validated instruments for clinician efficiency and burnout and compare impact across AI platforms.

Researchers with large-scale data-intensive applications often wish to scale up applications to run on multiple clusters, employing a middleware layer for resource management across clusters. However, at the very largest scales, such middleware is often “unfriendly” to individual clusters, which are usually designed to support communication within the cluster, not outside of it. To address this problem we have modified the Work Queue master-worker application framework to support a hierarchical configuration that more closely matches the physical architecture of existing clusters. Using a synthetic application we explore the properties of the system and evaluate its performance under multiple configurations, with varying worker reliability, network capabilities, and data requirements. We show that by matching the software and hardware architectures more closely we can gain both a modest improvement in runtime and a dramatic reduction in network footprint at the master. We then run a scalable molecular dynamics application (AWE) to examine the impact of hierarchy on performance, cost and efficiency for real scientific applications and see a 96% reduction in network footprint, making it much more palatable to system operators and opening the possibility of increasing the application scale by another order of magnitude or more.