Name change: ARC-TS is now ARC

By | General Interest, News

ITS ARC logo

We are excited to announce that we will be changing our name from Advanced Research Computing – Technology Services (ARC-TS) to Advanced Research Computing (ARC).

Along with the entire ITS team, we are committed to enhancing our support of high performance computing infrastructure and delivering intuitive research computing solutions that are agile and researcher-centric—and we hope that this name change will give a grateful nod to the history and growth of HPC at Michigan.

We will update ARC-TS’s website to reflect the new name on Mar. 6. It will have a new URL, arc.umich.edu, and there will be automatic redirects for anyone using the previous URL. The Twitter handle has been renamed @umichARC, and customers can continue to reach the Help Desk at arcts-support@umich.edu.

This change does not affect the groups that were previously part of ARC (MICDE, MIDAS, and CSCAR), and those groups will remain as independent units within the Office of Research.

Director Brock Palen and team look forward to continuing working collaboratively with units in ITS and across the university to deliver world-class technology to researchers. 

DNA sequencing productivity increases with ARC-TS services

By | HPC, News, Research, Systems and Services
NovaSeq, the DNA sequencer that is about the size of large laser printer.

The Advanced Genomics Core’s Illumina NovaSeq 6000 sequencing platform. It’s about the size of large laser printer.

On the cutting-edge of research at U-M is the Advanced Genomics Core’s Illumina NovaSeq 6000 sequencing platform. The AGC is one of the first academic core facilities to optimize this exciting and powerful instrument, that is about the size of a large laser printer. 

The Advanced Genomics Core (AGC), part of the Biomedical Research Core Facilities within the Medical School Office of Research, provides high-quality, low-cost next generation sequencing analysis for research clients on a recharge basis. 

One NovaSeq run can generate as much as 4TB of raw data. So how is the AGC able to generate, process, analyze, and transfer so much data for researchers? They have partnered with Advanced Research Computing – Technology Services (ARC-TS) to leverage the speed and power of the Great Lakes High-Performance Computing Cluster

With Great Lakes, AGC can process the data, and then store the output on other ARC-TS services: Turbo Research Storage and Data Den Research Archive, and share with clients using Globus File Transfer. All three services work together. Turbo offers the capacity and speed to match the computational performance of Great Lakes, Data Den provides an archive of raw data in case of catastrophic failure, and Globus has the performance needed for the transfer of big data. 

“Thanks to Great Lakes, we were able to process dozens of large projects simultaneously, instead of being limited to just a couple at a time with our in-house system,” said Olivia Koues, Ph.D., AGC managing director. 

“In calendar year 2020, the AGC delivered nearly a half petabyte of data to our research community. We rely on the speed of Turbo for storage, the robustness of Data Den for archiving, and the ease of Globus for big data file transfers. Working with ARC-TS has enabled incredible research such as making patients resilient to COVID-19. We are proudly working together to help patients.”

“Our services process more than 180,000GB of raw data per year for the AGC. That’s the same as streaming the three original Star Wars movies and the three prequels more than 6,000 times,” said Brock Palen, ARC-TS director. “We enjoy working with AGC to assist them into the next step of their big data journey.”

ARC-TS is a division of Information and Technology Services (ITS). The Advanced Genomics Core (ACG) is part of the Biomedical Research Core Facilities (BRCF) within the Medical School Office of Research.

Using machine learning and the Great Lakes HPC Cluster for COVID-19 research

By | General Interest, Great Lakes, HPC, News, Research, Uncategorized

A researcher in the College of Literature, Science, and the Arts (LSA) is pioneering two separate, ongoing efforts for measuring and forecasting COVID-19: pandemic modeling and a risk tracking site

The projects are led by Sabrina Corsetti, a senior undergraduate student pursuing dual degrees in honors physics and mathematical sciences, and supervised by Thomas Schwarz, Ph.D., associate professor of physics. 

The modeling uses a machine learning algorithm that can forecast future COVID-19 cases and deaths. The weekly predictions are made using the ARC-TS Great Lakes High-Performance Computing Cluster, which provides the speed and dexterity to run the modeling algorithms and data analysis needed for data-informed decisions that affect public health. 

Each week, 51 processes (one for each state and one for the U.S.) are run in parallel (at the same time). “Running all 51 analyses on our own computers would take an extremely long time. The analysis places heavy demands on the hardware running the computations, which makes crashes somewhat likely on a typical laptop. We get all 51 done in the time it would take to do 1,” said Corsetti. “It is our goal to provide accurate data that helps our country.”

The predictions for the U.S. at the national and state levels are fed into the COVID-19 Forecasting Hub, which is led by the UMass-Amherst Influenza Forecasting Center of Excellence based at the Reich Lab. The weekly predictions generated by the hub are then read out by the CDC for their weekly forecast updates Center for Disease Control (CDC) COVID-19 Forecasting Hub

The second project, a risk tracking site, involves COVID-19 data-acquisition from a Johns Hopkins University repository and the Michigan Safe Start Map. This is done on a daily basis, and the process runs quickly. It only takes about five minutes, but the impact is great. The data populates the COVID-19 risk tracking site for the State of Michigan that shows by county the total number of COVID-19 cases, the average number of new cases in the past week, and the risk level.

“Maintaining the risk tracking site requires us to reliably update its data every day. We have been working on implementing these daily updates using Great Lakes so that we can ensure that they happen at the same time each day. These updates consist of data pulls from the Michigan Safe Start Map (for risk assessments) and the Johns Hopkins COVID-19 data repository (for case counts),” remarked Corsetti.

“We are proud to support this type of impactful research during the global pandemic,” said Brock Palen, director of Advanced Research Computing – Technology Services. “Great Lakes provides quicker answers and optimized support for simulation, machine learning, and more. It is designed to meet the demands of the University of Michigan’s most intensive research.”

ARC-TS is a division of Information and Technology Services (ITS). 

Related information 

SEAS study wildlife refuge wetlands habitats using machine learning

By | General Interest, News, Research

This article was written by Taylor Gribble, the ARC-TS summer 2020 intern. 

A U-M School for Environment and Sustainability (SEAS) student team is working with the Shiawassee National Wildlife Refuge to study how fish move through different wetland habitats. Their work is primarily dependent on being in the field, but in March the pandemic delayed fieldwork. In June, the team of SEAS master students was allowed to begin socially distant field work. But the question was: How? 

With the help of the ARC-TS Scientific Computing and Research Consulting Services, the SEAS students were able to pivot their research methodology and develop advanced analysis approaches for hydroacoustic data using strategically placed cameras and machine learning.

The Shiawassee refuge is divided into separately managed wetland units. These wetland units can be connected or cut off from one another and the Shiawassee river. An Adaptive Resolution Imaging Sonar (ARIS) camera has been placed at the connection point between the refuge’s “control” wetland units and the river to track fish movements between these two ecosystems. They are created through human-made dikes and water control structures.

In order to find answers about fish movement, the SEAS team is divided into three separate parts: 

  1. In-the-field monitoring of fish, macroinvertebrates, water quality, and vegetation
  2. ARIS camera work: understanding how to use ARIS footage to answer ecological questions using machine learning facilitated by the ARC-TS Data Consultation Service
  3. Community education and outreach regarding restoration work at the refuge

Meghan Richey, machine learning specialist, and Armand Burks, research data scientist, are part of the ARC-TS Data Science Consultation team. Together they are working to see the project through by understanding the needs of the SEAS research team and providing the necessary coding expertise. In addition, they are working to provide the SEAS team with tools to become independent programmers so they can implement programming/coding into their future research endeavors.  

Richey works with the machine learning team. Machine learning is a tool for turning information into knowledge. It automatically finds patterns in complex data that are difficult for a human to find. While traditional problem solving uses data and rules to find an answer, machine learning uses data and answers to find the rules that apply to a problem. Together, they count the number of fish moving in front of the camera that was originally placed in mid-March but removed in mid-May due to flooding from the dam breaches in Midland, Mich. The camera has since been placed back into the “avenue” between one of the managed wetland pools and the river. With the help of a written machine algorithm, Richey and the SEAS team are able to count the number of fish they’re seeing in front of the camera feed. There is one camera placed in the water that is taking underwater images of the fish. The fish swim by the camera, and the team captures these frames.

Burks is responsible for the data conversion stages of the project. “They have a large amount of data that’s generated from the underwater camera. These aren’t the typical cameras as we would think of; they work with sonar which is based on sound. It is generating a lot of data in this sound-based sonar format that needs to be converted into something that is usable by the machine learning model.”

In order for the program to run smoothly and be able to count the fish, Burks and SEAS team had to develop a tool that allows them to turn the raw data into an actual video feed. Once this is completed the SEAS research team watch a series of pre-recorded videos that are saved to files. In order to receive the raw data, a large data conversion must happen to transform raw sonar data into videos. From there, the machine learning algorithms can be built and analyzed.

The ARC-TS team plans to continue working with the students and the team at the refuge to refine their methods and test with recently collected footage.

Bring the power of the HPC clusters to your laptop 

By | Great Lakes, HPC, News

Open OnDemand (OOD) is a tool that brings to researchers and students the power of Great Lakes, the university’s flagship open-science, high-performance, computing cluster. 

Open OnDemand is a way for researchers and students to use a web interface to access the Advanced Research Computing – Technology Services (ARC-TS) Great Lakes and Lighthouse High-Performance Computing resources. Because users do not need to have any technical training, it’s as simple as going to a browser and logging in. Users can start working immediately. 

“It’s your laptop, but 1,000 times bigger,” said Brock Palen, director, ARC-TS. “Open OnDemand offers our customers the speed and capacity of the HPC clusters without investing hours in training.”

The benefits of OOD are many, including providing easy file management, command-line shell access to the HPC clusters, job management and monitoring, and graphical desktop environments and desktop interactive applications such as RStudio, MATLAB, and Jupyter Notebook.

“This system works well for a range of fields from engineering to the physical and social sciences. Open OnDemand has lowered the barrier to access powerful HPC clusters so that students and researchers can do incredibly innovative work,” said Matt Britt, ARC-TS HPC manager. 

Additional resources:

ARC-TS is a division of Information and Technology Services (ITS).

3-2-1…blast off! COE students use ARC-TS HPC clusters for rocket design

By | Educational, General Interest, Great Lakes, Happenings, HPC, News
MASA team photo

The MASA team has been working with the ARC-TS and the Great Lakes High-Performance Computing Clusters to rapidly iterate simulations. What previously took six hours on another cluster, takes 15 minutes on Great Lakes. (Image courtesy of MASA)

This article was written by Taylor Gribble, the ARC-TS summer 2020 intern. 

The Michigan Aeronautical Science Association (MASA) is a student-run engineering team at U-M that has been designing, building, and launching rockets since its inception in 2003. Since late 2017, MASA has focused on developing liquid-bipropellant rockets—which are rockets that react to a liquid fuel with a liquid oxidizer to produce thrust—in an effort to remain at the forefront of collegiate rocketry. The team is made up of roughly 70 active members including both undergraduate and graduate students who participate year-round.

Since 2018, MASA has been working on the Tangerine Space Machine (TSM) rocket which aims to be the first student-built liquid-bipropellant rocket to ever be launched to space. When completed, the rocket’s all-metal airframe will stand over 25 feet tall. The TSM will reach an altitude of 400,000 feet and will fly to space at over five times the speed of sound.

MASA is building this rocket as part of the Base 11 Space Challenge which was organized by the Base 11 Organization to encourage high school and college students to get involved in STEM fields. The competition has a prize of $1 million, to be awarded to the first team to successfully reach space. MASA is currently leading the competition, having won Phase 1 of the challenge in 2019 with the most promising preliminary rocket design.

Since the start of the TSM project, MASA has made great strides towards achieving its goals. The team has built and tested many parts of the complete system, including custom tanks, electronics, and ground support equipment. In 2020, the experimental rocket engine designed by MASA for the rocket broke the student thrust record when it was tested, validating the work that the team had put into the test.

The team’s rapid progress was made possible in-part by the extensive and lightning-quick simulations using the ARC-TS Great Lakes High-Performance Computing Cluster.

The student engineers are Edward Tang, Tommy Woodbury, and Theo Rulko, and they have been part of MASA for over two years.

Tang is MASA’s aerodynamics and recovery lead and a junior studying aerospace engineering with a minor in computer science. His team is working to develop advanced in-house flight simulation software to predict how the rocket will behave during its trip to space.

“Working on the Great Lakes HPC Cluster allows us to do simulations that we can’t do anywhere else. The simulations are complicated and can be difficult to run. We have to check it, and do it again; over and over and over,” said Tang. “The previous computer we used would take as long as six hours to render simulations. It took 15 minutes on Great Lakes.”

A computer simulation of Liquid Oxygen Dome Coupled Thermal-Structural

This image shows a Liquid Oxygen Dome Coupled Thermal-Structural simulation that was created on the ARC-TS Great Lakes HPC Cluster. (Image courtesy of MASA)

Rulko, the team’s president, is a junior studying aerospace engineering with a minor in materials science and engineering.

Just like Tang, Rulko has experience using the Great Lakes cluster. “Almost every MASA subteam has benefited from access to Great Lakes. For example, the Structures team has used it for Finite Element Analysis simulations of complicated assemblies to make them as lightweight and strong as possible, and the Propulsion team has used it for Computational Fluid Dynamics simulations to optimize the flow of propellants through the engine injector. These are both key parts of what it takes to design a rocket to go to space which we just wouldn’t be able to realistically do without access to the tools provided by ARC-TS.”

Rulko’s goals for the team include focusing on developing as much hardware/software as possible in-house so that members can control and understand the entire process. He believes MASA is about more than just building rockets; his goal for the team is to teach members about custom design and fabrication and to make sure that they learn the problem-solving skills they need to tackle real-world engineering challenges. “We want to achieve what no other student team has.”

MASA has recently faced unforeseen challenges due to the COVID-19 pandemic that threaten to hurt not only the team’s timeline but also to derail the team’s cohesiveness. “Beaucase of the pandemic, the team is dispersed literally all over the world. Working with ARC-TS has benefitted the entire team. The system has helped us streamline and optimize our workflow, and has made it easy to connect to Great Lakes, which allows us to rapidly develop and iterate our simulations while working remotely from anywhere,” said Tang. “The platform has been key to allowing us to continue to make progress during these difficult times.”

Tommy Woodbury is a senior studying aerospace engineering. Throughout his time on MASA he has been able to develop many skills. “MASA is what has made my time here at Michigan a really positive experience. Having a group of highly-motivated and supportive individuals has undoubtedly been one of the biggest factors in my success transferring to Michigan.

This image depicts the Liquid Rocket Engine Injector simulation.

This image depicts the Liquid Rocket Engine Injector simulation. (Image courtesy of MASA)

ARC-TS is a division of Information and Technology Services. Great Lakes is available without charge for student teams and organizations who need HPC resources. This program aims to enable students access to high-performance computing to enhance their team’s mission.

Back-to-school research technology you can use

By | General Interest, News

Advanced Research Computing — Technology Services, a division of ITS,  provides access to and support for advanced computing resources. ARC-TS facilitates new and more powerful approaches to research challenges in fields ranging from physics to linguistics, and from engineering to medicine.

Here are the top 5 research technologies you need for a successful academic year:

  1. Get connected with the ITS Remote Resource Guide for specific information for researchers. 
  2. High-performance computing (HPC) including the Great Lakes HPC Cluster that is suitable for most use cases, and the Armis2 HPC Cluster that is appropriate for sensitive data including PHI/HIPAA.
  3. ARC-TS and ITS have long- and short-term storage options to support researchers. The ITS Data Storage Finder will help you find storage solutions to meet your needs.
  4. U-M Dropbox is available with 5TB of storage for individual accounts for all active faculty, staff, students, emeritus faculty, and sponsored affiliates at the Ann Arbor, Dearborn, Flint, and Michigan Medicine campuses.
  5. eResearch is the University of Michigan’s site for electronic research administration, including Regulatory Management for IRB and IBC review and approval, eRAM for animal research, Proposal Management for grant proposal organization, and M-Inform for disclosing and managing outside conflicts of interest. 

ARC-TS partners with Consulting for Statistics, Computing and Analytics Research (CSCAR) for training. CSCAR provides individualized support and training to researchers in a variety of areas relating to the management, collection, and analysis of data. CSCAR also supports the use of technical software and advanced computing in research. 

Find us on Twitter for updates. 

How can we help you? 

Contact the ARC-TS Help Desk, arcts-support@umich.edu

Beta tool helps researchers manage IT services

By | General Interest, News, Research, Uncategorized

Since August 2019, ARC-TS has been developing a tool that would give researchers and their delegates the ability to directly manage the IT research services they consume from ARC-TS, such as user access and usage stats.

The ARC-TS Resource Management Portal (RMP) beta tool is now available for U-M researchers.

The RMP is a self-service-only user portal with tools and APIs for research managers, unit support staff, and delegates to manage their ARC-TS IT resources. Common activities such as managing user access (adding and removing users), viewing historical usage to make informed decisions about lab resource needs, and determining volume capacity at a glance are just some of the functionality the ARC-TS RMP provides.

The portal currently provides tools for use with Turbo Research Storage, a high-capacity, reliable, secure, and fast storage solution. Longer-term, RMP will scale to include the other storage and computing services offered by ARC-TS. It is currently read-view only.

To get started or find help, contact arcts-support@umich.edu.

Open OnDemand Update on Great Lakes and Lighthouse May 21, 2020

By | Great Lakes, HPC, News

We are migrating Open OnDemand from version 1.4 to 1.6 to fix a security issue on May 21, 2020. Users will not be able to use the service during the upgrade process but running jobs should continue to run, based on our testing. If you need access during this period of time, we recommend ending your existing job and resubmitting when the service is restored.

Lighthouse will be upgraded from 9 a.m. to 12:00 p.m.  (ITS Status Page Link)

Great Lakes will be upgraded from 1 p.m. to 5:00 p.m.  (ITS Status Page Link)