HPC as a Service: Lessons Learned

By Wolfgang Gentzsch and Burak Yenier

December 10, 2012

After a fast-paced three months, round 1 of the HPC Experiment (also known as the Uber-Cloud Experiment) concluded last month, with more than 160 participating organizations and individuals from 25 countries, working together in 25 international teams. In this article we present their main findings, challenges, and their lessons learned.

The aim of the Uber-Cloud Experiment is to explore the end-to-end process of accessing remote computing resources in HPC centers and in HPC clouds as well as to study and overcome the potential roadblocks.

The experiment kicked off in July 2012 and brought together four categories of participants: the industry end-users with their applications, the software providers, the computing and storage resource providers, and the experts. We set up an end-user project by first selecting an end-user and his software provider, assigning an HPC/CAE expert, and matching a suitable resource provider to complete the team. Each team’s goal was to complete the project, and to chart the way around the hurdles they identified.

End users can achieve many benefits by gaining access to additional compute resources beyond their current internal resources, such as workstations. Arguably the most important two are the benefits of agility gained by speeding up product design cycles through shorter simulation run times, and those gained by the superior quality achieved by simulating more sophisticated geometries or physics, or by running many more iterations to look for the best product design.

Tangible benefits like these make HPC and more specifically HPC-as-a-Service (HPCaaS) very attractive. But how far are we from an ideal HPCaaS or HPC in the cloud model?

Honestly, at this point, we don’t know. However, in the course of this experiment, following each team and monitoring its challenges and progress, we’ve collected some excellent insight into these roadblocks and how our 25 teams have tackled them.

The main approach for this experiment is to look at the end-users’ project and select the appropriate resources, software and expertise that match those requirements.

During the three months of the experiment, we were able to build 25 teams each with a project proposed by an end user. These teams were: Team Anchor Bolt, Team Resonance, Team Radiofrequency, Team Supersonic, Team Liquid-Gas, Team Wing-Flow, Team Ship-Hull, Team Cement-Flows, Team Sprinkler, Team Space Capsule, Team Car Acoustics, Team Dosimetry, Team Weathermen, Team Wind Turbine, Team Combustion, Team Blood Flow, Team Turbo-Machinery, Team Gas Bubbles, Team Side impact, Team ColombiaBio, and Team Cellphone.

The final report, available to all of our registered participants, contains the use cases of many of the teams offering valuable insight through their own words. We look forward to future rounds of the experiment where this accumulating knowledge will yield ever more successful projects.

We recognize that every end-user project requires a slightly different approach, a variety of software and compute resources, a certain expertise to lead the end-to-end process, and a tailored schedule. To be able to keep the entire experiment consistent we asked each team to follow a common roadmap. The expert assigned to each team is the guide in following this roadmap. It calls for communication with the organizers at certain points, although generally the teams are autonomous and make their own decisions.

Based on the roadmap we defined going into round 1 of the experiment, the teams followed six steps to reach their goal:

Step 1. Define the end-user project. The end-user together with the expert and software provider jointly defined the project. Based on this information, as organizers we assigned the appropriate resources to the project.

Step 2. Contact the resource provider and set up the project environment. The expert contacted the computing resource and performed activities such as assisting in software and license installations, creation of user accounts, and configuration of the project environment.

Step 3. Initiate the end-user project execution. The expert assisted the end-user with uploading the necessary data, code and configuration files to the remote resource(s). The expert then worked with the resource provider to queue the project up on the HPC system.

Step 4. Monitor the project. The expert remained engaged with the resource providers and at any time had up to date information about the status of the project.

Step 5. Review results with the end-user. The expert assisted the end-user in downloading the results from the resource provider’s environment and discussed the results with the end-user. If any rework or rerun was required it was completed by executing steps 2-5 again.

Step 6. Document findings. During the entire lifecycle of the project, there occurred hurdles, friction and failure points and the expert documented these findings.

Intentionally, we performed the first round of this experiment manually, that is, not via an automated service, because we believe the technology is not the challenge anymore; rather it’s the people and their processes, and that’s what we wanted to explore. We are continuously improving the roadmap to successful completion of our projects.

The teams reported the following main roadblocks and provided information on how they resolved them (or not):

  • Security and privacy, guarding the raw data, processing models and the results
  • Unpredictable costs can be a major problem in securing a budget for a given project
  • Lack of easy, intuitive self-service registration and administration
  • Incompatible software licensing models hinder adoption of Computing-as-a-Service
  • High expectations can lead to disappointing results
  • Lack of reliability and availability of resources can lead to long delays

Just like all other participants, we as the organizers, treated the experiment as a learning opportunity. In our report we have also summarized what we’ve found to be shortcomings of the experiment as we put it together in round 1. Learning from these shortcomings we have improved the experiment for round 2. To be specific, we discussed and provided solutions for the following shortcomings:

All participants are professionals with busy schedules and the experiment is not their primary job, so they could only dedicate a few hours per week to the experiment

  • Vacations delayed most of the teams’ progress, especially in the beginning (August) of the Experiment
  • Some resource providers ran into resource crunches which delayed team projects
  • Some of our projects ran into long delays since the project and the resource provider weren’t the best match possible
  • Some resource providers struggled with the installation of an application
  • Other resource providers had difficulties with providing network access through complex network connections
  • Resource providers differ in their service philosophies
  • Simply getting started was a challenge
  • A few teams struggled with figuring out which team member needs to do what and when
  • Team forming was one of the steps, which took the longest amount of time, each team member needed to exchange significant amounts of information about their background, capabilities, expectations, availability, and commitment levels with one another before the project could even kick off
  • Finally, manual processes are just slow; they consumed days, sometimes weeks especially because the various technology and people resources were inherently remote, each with different priorities

We hope that our participants will extract value out of the experiment and the final report. They certainly deserve to do so in return for their generous contributions, support and participation. We now look forward to round 2 of the experiment with its already over 250 participants and the learning that it will result in.

If you are interested in participating in round 2 or just want to monitor its progress, you can register at http://hpcexperiment.com.  You can also go there to get the final report for round 1, which details the results and recommendations.

About the Authors

Wolfgang Gentzsch and Burak Yenier are the creators and facilitators of the Uber-Cloud Experiment. Wolfgang is an HPC veteran. Having worked in leading positions in research, academia and industry for some 30 years, Wolfgang is now an HPC consultant and the chairman of the ISC Cloud conference series for HPC and Big Data in the Cloud. Burak is the vice president of operations at CashEdge, a software-as-a-service company in Silicon Valley, which provides innovative payments and aggregation solutions to financial institutions.


Related Articles

Half-Time in the Uber-Cloud

The Uber-Cloud Experiment

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

New Exascale System for Earth Simulation Introduced

April 23, 2018

After four years of development, the Energy Exascale Earth System Model (E3SM) will be unveiled today and released to the broader scientific community this month. The E3SM project is supported by the Department of Energy Read more…

By Staff

RSC Reports 500Tflops, Hot Water Cooled System Deployed at JINR

April 18, 2018

RSC, developer of supercomputers and advanced HPC systems based in Russia, today reported deployment of “the world's first 100% ‘hot water’ liquid cooled supercomputer” at Joint Institute for Nuclear Research (JI Read more…

By Staff

New Device Spots Quantum Particle ‘Fingerprint’

April 18, 2018

Majorana particles have been observed by university researchers employing a device consisting of layers of magnetic insulators on a superconducting material. The advance opens the door to controlling the elusive particle Read more…

By George Leopold

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’s introduction of an ARM-based system (XC-50) last November. Read more…

By John Russell

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

IBM Expands Quantum Computing Network

April 5, 2018

IBM is positioning itself as a first mover in establishing the era of commercial quantum computing. The company believes in order for quantum to work, taming qu Read more…

By Tiffany Trader

FY18 Budget & CORAL-2 – Exascale USA Continues to Move Ahead

April 2, 2018

It was not pretty. However, despite some twists and turns, the federal government’s Fiscal Year 2018 (FY18) budget is complete and ended with some very positi Read more…

By Alex R. Larzelere

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Leading Solution Providers

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This