SC17: Singularity Preps Version 3.0, Nears 1M Containers Served Daily

By John Russell

November 1, 2017

Just a few months ago about half a million jobs were being run daily using Singularity containers, the LBNL-founded container platform intended for HPC. That was already a big number for the young open source project. Today, the number is closer to one million per day says Gregory Kurtzer, former HPC Systems Architect for Lawrence Berkeley National, the original driving force behind Singularity, and now the CEO of SingularityWare LCC.

It’s increasingly clear that the appetite for containers in scientific computing is every bit as strong as it is in enterprise computing where several technologies are jostling but Docker remains firmly the king. Containers, of course, aren’t new. (I’ve probably written exactly those words before) Like virtual machines (VM) before them, containers embody the idea of encapsulating compute environments to enhance application and workflow portability (and reproducibility) across varying compute infrastructures. Gather what you need into a single ‘box’, wrap it with enough standardized hooks to play nicely on other machines running Singularity, and game on. Build it once. Use (and share) it many times.

This separation of the software environment (and all of its dependencies) from the systems it runs on is an important paradigm change, says Kurtzer, especially in HPC where tight coupling of software to the specific underlying hardware has long been the guiding principle for squeezing out maximum performance. It still is. But for many jobs squeezing out optimum performance is less important than achieving portability and consistency of adequate performance.

HPCwire recently talked with Kurtzer about Singularity’s rapid growth, its technology roadmap, its staffing challenge, and Kurtzer’s plans to create a little excitement for Singularity at SC17. He is chary of spending on an SC booth given that SC newcomers often land in far corners with limited traffic. Instead Kurtzer is sponsoring a scavenger hunt involving many booths of Singularity stakeholders (preliminary list of participating booths at the end of the article). We’ll see how that works out.

In the meantime there’s lots to talk about. Singularity 2.4 was released last month with 3.0 expected early in 2018. (The Singularity FAQ page provides a good overview of features.) Importantly support from the big and small government and academic computing centers has been significant. Many of the familiar supercomputing centers, for example OCLF, TACC, NASA Ames, SDSC, Sandia, NIH, and LBNL, are using Singularity in production. Though not comprehensive the Singularity registry (voluntary) provides snapshot of Singularity user base.

It’s also noteworthy that now that Singularity has made substantial inroads into the traditional HPC community, Kurtzer is planning expansion into the enterprise computing arena. That seems in keeping with many traditional HPC technology suppliers who have pivoted to the enterprise in search of growth. What follows is a portion of our conversation plus a few slides from a recent presentation by Kurtzer to a national lab.

HPCwire: Seems like the use of Singularity is growing quickly, even in the relatively short time since we talked last (see, Singularity HPC Container Technology Moves Out of the Lab). Besides adoption what been happening with Singularity itself.

Gregory Kurtzer, SingularityWare CEO

Greg Kurtzer: We’ve been focusing a lot on the 2.4 release which I am hoping will actually be out maybe even tonight (it’s now out). There are a couple important new features in 2.4. The biggest is something we are calling instance support. Basically it’s the ability to run persistent namespaces such that you can come back and join it after the fact. It’s almost like a virtual machine; you can start up a Singularity container, run your jobs and everything you want in it, and when you want to leave it and exit, you can leave and exit and then you can come back to it later and it is still running.

The other big change is we have moved to compressed, immutable images. By default, previously everything was embedded in a read write format that emulates a file system. Now what we are doing is something that will be a little bit different because it is much more optimal in terms of space consumption on your hard drive because it is always compressed and actually even runs compressed. You can execute and use a compressed container without ever having to un-compress it.

Also, we now have ability to do things like persistent overlays so you can capture all the deltas in a container and then use that as a data container, a new concept we are playing with.

HPCwire: I understand that 3.0 is actually not far off. Can you give a preview? What’s on the technology roadmap.

Kurtzer: I can give you a little bit of a heads ups on what is coming in future versions. We’re coining a new term, or at least I think we are as I have not heard it used before us, called data containers which is really along the line of encapsulation of data. Currently we have encapsulations of environments, operating environments, etc. but there are also really good reasons to encapsulate your data. We are going to have additional abilities with security to further limit security exposure to host systems. That’s a big one. A lot of supercomputing centers won’t even have to make Singularity “setuid” root; they’ll basically just use additional Linux capabilities for increasing security privileges in order to run containers.

The big change we are going for [in 3.0] is introducing something called the Singularity image format, SIF for short. We are changing the major version name from 2.x to 3.x because we are changing the image format. Every time we have changed the image format we have changed the major number. SIF is basically a single file image because the whole context of Singularity is a single image; that’s really the main point of what a singularity container is, differentiating itself from other container systems or at least that is one of the main ones. We are spending a lot of effort defining what that single image looks like.

This SIF file will have the ability to have multiple data regions within the file. In a manner of speaking you can have multiple containers or multiple container layers, within a single file. Let’s say it’s running CentOS with MySQL and some sort of genomics application that queries the SQL database and then can do other things. You can have that in the base image and that base is immutable, it cannot change. Then when we have another data region which is writeable where we capture all of the modifications to that image in the writable layer of the SIF file. This is important because we can checksum and sign the multiple data regions independently while still allowing the data within the container to change or evolve.

Also we are bringing forth the idea of cryptographically signing of containers which is actually huge because the other container systems that are out there, even in enterprise, don’t have the ability to cryptographicly sign a container in its runtime form. Thanks to the SIF format we can have sections of that file which are signed and other sections which are using overlays. To give a use case, let’s stick with the genomics example; let’s say you have a database server running and you are querying and updating that genomics database and as time goes on that database is going to grow. If you think about this in a traditional way, that would break the signing, it would break the validation of your container. You wouldn’t be able to verify it anymore because you just changed your image. But because it is based on an immutable base and the data in that base layer can be signed independently within the image, we have the ability to have containers that evolve with time without breaking signatures.

HPCwire: What else is on the docket for 3.0 or beyond?

Kurtzer: The SIF image format includes image signing and image verification/validation, and we are also additional support for network namespaces. Right now we have network namespace isolation, but this new set of features will give us the ability for a container to come up as a virtual IP address. That obviously will require a privileged user; that can’t be done with a regular user because it will be changing network configurations. We’re also looking into CGroup support (control groups, Linux), and virtual booting of instances. The idea of virtual booting of those instances would basically allow you to run a Singularity container just like a VM. We have already done prototypes of this and it only takes it a fraction of a second to boot so it is very fast.

Container performance monitoring is also on the roadmap but I am not sure if we are going to get that into 3.0. It’s basically the ability to do performance profiling through a container. Our goal, really, is to make Singularity a feature-full, science enabling platform.

HPCwire: Maybe this is a good spot to review the Singularity user base. How is support from the major science computing centers? What does a typical user look like? Is it mostly the “long tail of science” type of users and organizations?

Kurtzer: Well, Titan (OCLF supercomputer center at Oak Ridge National Lab) among several other top 10 supercomputers on the Top500 are running Singularity. We have had very strong support from a wide range of major computing centers including older and new machines. As for who the user is, we have people that are not only on the long tail of science but also people on the cutting edge of their computational domain. This is because Singularity changes the software distribution and archival paradigm as the container can fit within a researcher’s existing data management solutions.

Singularity is already running on most of the large centers; what I want to focus on now is enabling the independent scientists as much as we can and to focus on this notion of enterprise HPC. I’ve been contacted by multiple vendors, tier 1 and tier 2, on something that they are calling “Enterprise HPC.” It’s basically enterprise sites who have little or no expertise in HPC but who are starting to run HPC-like workloads (machine learning and compute driven analytics). Singularity is being targeted by many organizations as the vector for basically dealing with these applications and these workloads because these enterprises don’t have the HPC expertise to be building or maintaining all of these workflows. The vendors are talking about building workflows – e.g., machine learning or analytics workflows – and saying ‘we want to distribute those as singularity containers.’ So singularity is going to be really big, I think, in this kind of hybrid mix between enterprise and HPC.

Many of these enterprise HPC jobs are not the tightly-coupled highly parallelized jobs we’ve come to see as commonplace on HPC. When someone says HPC it means something really specific to traditional HPC folks; it’s tightly coupled, we’ve got some sort of low latency interconnect, parallel file systems, designed to run high performance, highly scalable custom applications. But today, this has changed. HPC has come to mean pretty much any form of scientific computing and as a result, its breadth has grown in terms of what kind of applications we need to support. The traditional HPC architecture is not as applicable to a general wide use case scenario.

Singularity has lowered the barrier of entry to HPC considerably. People can create their own workflows, can leverage Docker, can and leverage other people’s containers via singularity hub to recapitulate people’s workflows and then further expand on this basis.

Even people who are doing more traditional HPC type jobs that are tightly coupled and whatnot are looking into containers to escape some of the dependency issues and some of the difficulties in creating those workflows and archive and/or distribute their software stacks.

HPCwire: Surely not all HPC applications are easily containerized.

Kurtzer: True. Fluent (CFD simulation, ANSYS) for example is extremely difficult to containerize. This is because to do a multi-node parallel processing job Fluent wants to run the MPI but the host resource manager should be controlling the MPI. We end up in this chicken and egg problem as the MPI within the container actually wants to be the MPI outside the container. Having vendor buy-in and support in how we properly containerize these applications is critical (hint, hint ANSYS, let’s talk.).

HPCwire: Let’s change gears and talk about the Singularity organization. How’s it going?

Kurtzer: It is fantastic. I’ve created a few companies previously, and several open source projects, including CentOS Linux, and the growth and commercial interest in Singularity has surpassed all of them! At this point, I am building my core team. I am looking for experienced developers, great minds, and people who want to change the face of computing. It is an extremely surreal experience, and I am looking for the most fantastic of people to join in this project. Funding is available so it just comes down to finding the right people.

To that point, if there are any readers out there interested in being part of this endeavor reach out to me, and let’s talk.

Preliminary List of Organizations Supporting Singularity at Their Booths

  1. Bright Computing
  2. Globus
  3. HPCwire
  4. MVAPICH2/Ohio State University
  5. Penguin Computing
  6. RedBarn Computing
  7. Rutgers
  8. SSERCA (Sunshine State Educational and Research Computing Alliance)
  9. Texas Tech University Booth
  10. University of Michigan / Michigan State

Slide Source: Kurtzer

Subscribe to HPCwire's Weekly Update!

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

Weekly Wire Roundup: July 8-July 12, 2024

July 12, 2024

HPC news can get pretty sleepy in June and July, but this week saw a bump in activity midweek as Americans realized they still had work to do after the previous holiday weekend. The world outside the United States also s Read more…

Nvidia, Intel not Welcomed in New Apple AI and HPC Development Tools

July 12, 2024

New Mac developer tools will leverage Apple's homegrown chips, limiting HPC users' ability to use parallel programming frameworks from Intel or Nvidia. Apple's latest programming framework, Xcode 16, was introduced at Read more…

Virga: Australia’s New HPC and AI Powerhouse

July 11, 2024

Australia has officially added another supercomputer to the TOP500 list with the implementation of Virga. Officially coming online in June 2024, Virga is the newest HPC system to come out of the Australian Commonwealth S Read more…

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and implementation phases of the Quantum Quantum Science and Technolo Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the software, and selecting the best user interface. The National Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three of the 10 highest-ranking Top500 systems, but some other ne Read more…

Shutterstock 2203611339

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and imple Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the softw Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three Read more…

Generative AI to Account for 1.5% of World’s Power Consumption by 2029

July 8, 2024

Generative AI will take on a larger chunk of the world's power consumption to keep up with the hefty hardware requirements to run applications. "AI chips repres Read more…

US Senators Propose $32 Billion in Annual AI Spending, but Critics Remain Unconvinced

July 5, 2024

Senate leader, Chuck Schumer, and three colleagues want the US government to spend at least $32 billion annually by 2026 for non-defense related AI systems.  T Read more…

Point and Click HPC: High-Performance Desktops

July 3, 2024

Recently, an interesting paper appeared on Arvix called Use Cases for High-Performance Research Desktops. To be clear, the term desktop in this context does not Read more…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. Read more…

Shutterstock_1687123447

Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Shutterstock_1687123447

Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Leading Solution Providers

Contributors

AMD Clears Up Messy GPU Roadmap, Upgrades Chips Annually

June 3, 2024

In the world of AI, there's a desperate search for an alternative to Nvidia's GPUs, and AMD is stepping up to the plate. AMD detailed its updated GPU roadmap, w Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. Read more…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire