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In this paper, we present the design and implementation of Neptune, a simple, domain-specific language based on the Ruby programming language. Neptune automates the configuration and deployment of scientific software frameworks over disparate cloud computing systems. Neptune integrates support for MPI, MapReduce, UPC, X10, StochKit, and others. We implement Neptune as a software overlay for the AppScale cloud platform and extend AppScale with support for elasticity and hybrid execution for scientific computing applications. Neptune imposes no overhead on application execution, yet significantly simplifies the application deployment process, enables portability across cloud systems, and promotes lock-in avoidance by specific cloud vendors.

NASA's vision is to “reach for new heights and reveal the unknown so that what we do and learn will benefit all humankind.” While our missions provide large volumes of unique and invaluable data to the scientific community, they also serve to inspire and educate the next generation of engineers and scientists. One critical aspect of “benefiting all humankind” is to make our missions as visible and accessible as possible to facilitate the transfer of scientific knowledge to the public. The recent successful landing of the Curiosity rover on Mars exemplified this vision: we shared the landing event via live video streaming and web experiences with millions of people around the world. The video stream on Curiosity's website was delivered by a highly scalable stack of computing resources in the cloud to cache and distribute the video stream to our viewers. While this work was done in the context of public outreach, it has extensive implications for the development of mission critical, highly available, and elastic applications in the cloud for a diverse set of use cases across NASA.

Scientists, educators, decision makers, students, and many others utilize scientific data produced by science instruments. They study our universe, make new discoveries in areas such as weather forecasting and cancer research, and shape policy decisions that impact nations fiscally, socially, economically, and in many other ways. Over the past 20 years or so, the data produced by these scientific instruments have increased in volume, complexity, and resolution, causing traditional computing infrastructures to have difficulties in scaling up to deal with them. This reality has led us, and others, to investigate the applicability of cloud computing to address the scalability challenges. NASA’s Jet Propulsion Laboratory (JPL) is at the forefront of transitioning its science applications to the cloud environment. Through the Apache Object Oriented Data Technology (OODT) framework, for NASA’s first software released at the open-source Apache Software Foundation (ASF), engineers at JPL have been able to scale the storage and computational aspects of their scientific data systems to the cloud – thus achieving reduced costs and improved performance. In this chapter, we report on the use of Apache OODT for cloud computing, citing several examples in a number of scientific domains. Experience, specific performance, and numbers are also reported. Directions for future work in the area are also suggested.

The Ensemble project has consistently identified common patterns pertaining to activity planning across missions, analogs, and field tests. In this process, we have developed a unified planning repository that serves Mars Science Laboratory, International Space Station, Analog Field tests like Pavilion Lake Research Project, and many others. This repository encapsulates complexities associated with persistence, indexing, access and synchronization of data required for distributed planning. By providing a uniform Restful interface to the clients, the plan repository enables access from a variety of clients spanning multiple NASA centers. These clients range from shell scripts and browsers to mobile apps and complex desktop applications. It supports multiple representations, or formats, of plans to support multiple clients. With a focus on interoperability, end-users from disparate clients are able to interact with each other seamlessly. With support for rich querying capabilities through an inverted index of all of the activities and plans stored in the repository, we allow operators and scientists to search for the plans based on tactically relevant criteria with structured and unstructured (full-text) queries. Lastly, in order to support distributed planning, the repository incorporates a messaging layer to synchronize plans across multiple geographically dispersed clients in real-time. In this paper, we describe common patterns in tactical planning and the feature sets we have built to support them. Furthermore, we share key insights and lessons learned from deploying plan repository on a variety of missions and field tests. We provide an overview of the novel integration of technologies that support planning repository, and we discuss the role of cloud computing in our deployment strategy. We conclude with an overview of future research and upcoming features in the plan repository.

Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 μs inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.

Cloud Computing holds tremendous potential for missions across the National Aeronautics and Space Administration. Several flight missions are already benefiting from an investment in cloud computing for mission critical pipelines and services through faster processing time, higher availability, and drastically lower costs available on cloud systems. However, these processes do not currently extend to general scientific algorithms relevant to earth science missions. The members of the Airborne Cloud Computing Environment task at the Jet Propulsion Laboratory have worked closely with the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) mission to integrate cloud computing into their science data processing pipeline. This paper details the efforts involved in deploying a science data system for the CARVE mission, evaluating and integrating cloud computing solutions with the system and porting their science algorithms for execution in a cloud environment.

In this paper, we present the design and implementation of Neptune, a domain specific language (DSL) that automates configuration and deployment of existing HPC software via cloud computing platforms. We integrate Neptune into a popular, open-source cloud platform, and extend the platform with support for user-level and automated placement of cloud services and HPC components. Such platform integration of Neptune facilitates hybrid-cloud application execution as well as portability across disparate cloud fabrics. Supporting additional cloud fabrics through a single interface enables high throughput computing (HTC) to be achieved by users who do not necessarily own grid-level resources but do have access to otherwise independent cloud technologies.We evaluate Neptune using different applications that employ a wide range of popular HPC packages for their implementation including MPI, X10, MapReduce, DFSP, and dwSSA. In addition, we show how Neptune can be extended to support other HPC software and application domains, and thus be used as a mechanism for many task computing (MTC).

The twin Mars Exploration Rovers, Spirit and Opportunity, have both been in operation on Mars for over 6 years. Hundreds of thousands of images and other pieces of scientific data has been generated by or for each rover during that time. The Mars Science Laboratory (Curiosity) is expected to produce even more data then its predecessors; future missions will dwarf Curiosity and so on. It is necessary to build tools that will scale with the increased data associated with a mission and allow the human operator to make informed decisions based on all available data. Companies such as Amazon and Google thrive on making large amounts of data accessible to their customers. This paper discusses how we have borrowed the paradigms of such web search and e-commerce sites and applied them to interactions with mission data.

The Mars Exploration Rover Mission (MER) includes the twin rovers, Spirit and Opportunity, which have been performing geological research and surface exploration since early 2004. The rovers' durability well beyond their original prime mission (90 sols or Martian days) has allowed them to be a valuable platform for scientific research for well over 2000 sols, but as a by-product it has produced new challenges in providing efficient and cost-effective tactical operational planning. ***

We present MSLICE Sequencing (MSEQ), an Integrated Development Environment (IDE) for robot command sequences, which will be used to construct and integrate sequences for the Mars Science Laboratory, scheduled to launch in November of 2011. MSEQ is an experiment in combining the paradigms of software development and robotic operations, under the thesis that the latter can benefit greatly from reusing the technologies developed to assist in the former. 12

Developers across the Jet Propulsion Laboratory are leveraging cloud computing on a variety of problems on multiple missions. In this paper, we explore cloud computing and its potential to revolutionize spacecraft operations. We describe concrete applications that have been developed and integrated with ground data systems. We will evaluate the role cloud computing is playing in the development of MSLICE (Mars Science Laboratory Interface), the primary software tool to control the (MSL) Mars Science Laboratory Rover.

The Mars Reconnaissance Orbiter's HiRISE (High Resolution Imaging Science Experiment) camera takes the largest images of the Martian surface. The image size is typically around 2.52 gigapixels. There is only a handful of software capable of doing a task as simple as reducing the size of the image by half and saving the result as a new image. The Scalable Image Processing Framework (SIPF) overcomes these issues by creating a generalized tile-based processing pipeline that loads only a small portion of the image into memory. This allows for the data in memory at any given time to become manageable. Image tiles are an intrinsic property that provides scalability and efficiency while processing images. Distributed computing technologies such as cloud computing can be applied naturally. A mathematical framework for scalable image operations is defined that provides insight into the scalable considerations needed with each class of operations. We also formalize the deferred execution design pattern and show how it is used as a basis for our implementation. The SIPF has the ability to perform a variety of Scalable Image Operations such as Cropping, Rotation, Scaling (Bilinear and Nearest Neighbor Interpolation), Edge Detection, Sharpening, Convolution (Filters), Brightness, Contrast, and Gaussian Blurring. The Scalable Image Processing Framework will be used to process incoming images from the Mars Exploration Rovers and eventually the Mars Science Laboratory. It will be integrated with the Maestro software (science visualization and planning tool). Maestro is used for the Mars Exploration Rover Mission and other celestial body exploratory missions. 1 2

Cloud Computing has delivered unprecedented compute capacity to NASA missions at affordable rates. Missions like the Mars Exploration Rovers (MER) and Mars Science Lab (MSL) are enjoying the elasticity that enables them to leverage hundreds, if not thousands, or machines for short durations without making any hardware procurements. In this paper, we describe Polyphony, a resilient, scalable, and modular framework that efficiently leverages a large set of computing resources to perform parallel computations. Polyphony can employ resources on the cloud, excess capacity on local machines, as well as spare resources on the supercomputing center, and it enables these resources to work in concert to accomplish a common goal. Polyphony is resilient to node failures, even if they occur in the middle of a transaction. We will conclude with an evaluation of a production-ready application built on top of Polyphony to perform image-processing operations of images from around the solar system, including Mars, Saturn, and Titan.

The rich variety of unmanned robotics at NASA's Jet Propulsion Laboratory sets the bar for innovations in activity planning. The Operations Planning Team at JPL, co-winner of the NASA Software of the Year award in 2004, is continuously working to enhance the capabilities and user experience for long term and real-time planning operations. Spatial Querying improves the efficiency of planetary surface operations. It can empower scientists by giving them the ability to search for all imagery containing a particular point of interest. Furthermore, these queries can help maximizing the amount of science by reducing the amount of redundant imagery: before planning to acquire new images of a location, planners can check if they already have sufficient data for it. In this paper, we describe our use of R-Trees, along with novel 3D to 2D projections, to delivery spatial querying capabilities to the operators and scientists of MER and MSL mission. Aside from covering the implementation details, we discuss a key aspect of our software: the user interface. To make the interface effective, we stressed low latency and fast computations on the server side to emphasize instant gratification from the user's perspective. Furthermore, we leveraged the interfaces that our customers are already familiar with to make the capability intuitive to utilize.

Mars rover images provide essential context for planning science activities. This work describes a method for delivering Mars rover images to operations planners that is highly efficient and scalable. Experimental results of various image compression strategies applied to rover images are given. Next, an adaptive level-of-detail tile-based delivery methodology for images is presented. With a tile-aware image browsing application, images of virtually limitless size may be distributed to participating scientists with great efficiency and thus provide a common collaborative context. This work also describes advances in mosaicking rover images in support of operations planning.

A centralized mission activity planning system for any long-term mission, such as the Mars Exploration Rover Mission (MER), is completely infeasible due to budget and geographic constraints. A distributed operations system is key to addressing these constraints; therefore, future system and software engineers must focus on the problem of how to provide a secure, reliable, and distributed mission activity planning system. We will explain how Maestro, the next generation mission activity planning system, with its heavy emphasis on portability and distributed operations has been able to meet these design challenges. MER has been an excellent proving ground for Maestro's new approach to distributed operations. The backend that has been developed for Maestro could benefit many future missions by reducing the cost of centralized operations system architecture.

Supplemental Digital Content is available in the text., Background: Coronavirus disease 2019 (COVID-19) has led to over 1 million deaths worldwide and has been associated with cardiac complications including cardiac arrhythmias. The incidence and pathophysiology of these manifestations remain elusive. In this worldwide survey of patients hospitalized with COVID-19 who developed cardiac arrhythmias, we describe clinical characteristics associated with various arrhythmias, as well as global differences in modulations of routine electrophysiology practice during the pandemic. Methods: We conducted a retrospective analysis of patients hospitalized with COVID-19 infection worldwide with and without incident cardiac arrhythmias. Patients with documented atrial fibrillation, atrial flutter, supraventricular tachycardia, nonsustained or sustained ventricular tachycardia, ventricular fibrillation, atrioventricular block, or marked sinus bradycardia (heart rate

Background RENASCENT is a prospective, multi-center first-in-human clinical study to evaluate the clinical performance of the novel sirolimus-eluting 150-μm strut thickness FORTITUDE® BRS for percutaneous coronary intervention of single de novo coronary lesions. Methods FORTITUDE® BRS was tested in a prospective study in Italy and Colombia. Study objectives were in-scaffold angiographic late lumen loss (LLL) measured by quantitative coronary angiography and target vessel failure (TVF) defined as the composite rate of cardiac death, target vessel myocardial infarction or ischemia driven target lesion revascularization (TLR) at 9- and 24-months with clinical results up to 36-months. Results A total of 63 patients were enrolled. All patients underwent lesion pre-dilatation and 22 patients (34.9%) underwent post-dilatation. Clinical device and procedural success was 98.4% (62/63 patients) and 96.8% (61/63 patients) respectively. At 9-months, TVF occurred in 3/61 (4.9%) of the patients including 2 peri-procedural MI and one ischemia-driven TLR. Between 9- to 24-months, ischemia-driven TLR occurred in 3 additional patients (4.9%) including 1 patient who presented with very late ST after stopping all medications. There were no further TVF between 24- and 36-months. Conclusions In this multi-center prospective study, the FORTITUDE® BRS was shown to be safe and effective in the treatment of single coronary lesions with low levels of TVF and LLL at 9- and 24-months. It was shown to be clinically safe upto 36-months follow-up.

An 87-year-old man with a history of transcatheter aortic valve replacement with a 6-year-old 27-mm Direct Flow bioprosthesis (Direct Flow Medical, Santa Rosa, California), presented with heart failure. Echocardiography showed normal transcatheter heart valve (THV) leaflets with no obstruction (mean