A Big Data Journey While Seeking to Catalog our Universe

By James Reinders

January 16, 2019

It turns out, astronomers have lots of photos of the sky but seek knowledge about what the photos mean. Sound familiar? Big data problems are often characterized as transforming data into insights – which is exactly what some ambitious scientists are working to do with “Sky Survey” data. A Sky Survey is essentially astronomer speak for “lots and lots of images taken by telescopes, along with information of when and where they were taken.”

The Celeste collaboration is a group of scientists who have worked to catalog the visible universe in a way never before accomplished. They seek to create and refine a catalog which can detail the placement and characteristics (such as brightness and rotation) of every visible object in the sky.

Along the way, the Celeste collaboration has already proven that one high productive language (Julia) can offer high performance “at scale” (using hundreds of thousands of processor cores for compute), and their success certainly indicates that we will see more “at scale” big data work.

Journey of the Photons

No amount of effort to design an amazing telescope can overcome the effects that a very long journey has had upon the photons. Putting a telescope into orbit might cut out the last few hundred miles through our atmosphere, but that is just the tip of the iceberg when it comes to figuring out what each photo means. The techniques being developed by the Celeste collaboration are applicable to data regardless of whether it is earth-based or space-based.  So far, the earth-based data has supplied plenty of work to do.

Aside from inherent limitations of any sensing device in a telescope, the final image we get from a telescope is imperfect on account of point spread from the atmosphere, diffraction spikes from the telescope, and gravitational lensing that has occurred along the journey, among other causes. The Celeste collaboration has plugged away at addressing such challenges in their quest to build their meaningful catalog. As I have learned more about all they have done, I have been both amazed with the magnitude of their accomplishments and in awe of the enormous scope of future work that is possible. A truly big data project, Celeste has an insatiable appetite for more data, and for more sophisticated analysis work.

Lots of Compute, and Lots of (High Productivity) Programming

Collecting all known data about the visible universe into a meaningful model certainly is a big data problem. Celeste collaborators’ computational work has landed in the petascale world, meaning they have performed computations at a rate exceeding a thousand million million (1015) floating-point operations per second. They did this with over nine thousand CPUs, a high productivity language called Julia, and a 178 terabyte dataset representing 188 million stars and galaxies. Processing also involved intensive I/O due to the multiple passes over the dataset processed during a 14.6-minute run on the Cori supercomputer.

They did not use FORTRAN or C++ as the language for this task. Instead, they choose a high productivity language out of MIT known as Julia, and used it to very efficiently utilize Intel processors at a petascale. Specifically, they used 1.3 million threads on 9,300 Intel Xeon Phi processors (650,000 cores) to achieve 1.54 petaflops peak performance. This was the first showing of Julia at petascale, and it certainly will not be the last.

The Julia programming language developers explain Julia by saying: “Julia excels at numerical computing. Julia was designed from the beginning for high performance. Its syntax is great for math, many numeric datatypes are supported, and parallelism is available out of the box. Julia’s multiple dispatch is a natural fit for defining number and array-like datatypes.”

Keys to High-Performance Julia

The developers of the Celeste code have a few Julia-specific tips for making sure Julia is competitive with other compiled languages for high performance. Their tips were:

  1. Follow the performance tips given with Julia (no global state/eval/etc. in hotspots).
  2. Type stability (dynamic re-typing might seem cool, but it kills performance).
  3. Minimize dynamic memory allocations; use memory profiles to find allocations to reduce (double benefit: less time allocating also means less time doing garbage collection).

The final tip may be especially important in languages with garbage collection, but it is a great suggestion for programmers in all languages. Similarly, avoiding global state (the first tip) has enormous merit outside Julia as well.

Finally, the developers stress the need to profile to find and optimize hotspots. Hardly a Julia specific tip!  All in all, the experience of the developers with Julia mostly resembled the experience of any HPC programmer using C, C++, and Fortran. They would say that Julia offers a more productive programming environment, but also offers performance you would not find with other high productive languages such as Python. Despite some solid accelerated Python capabilities that are out there, no Python application has shown anything close to petaflops performance.

It seems that making Julia scale to petaflops performance involves the same thinking as effective parallel programming in any high-performance language.

The Data: SDSS

Irénée du Pont Telescope at Las Campanas Observatory. (credit: Krzysztof Ulaczyk, CC BY-SA 4.0)

In 1998, the Apache Point Observatory in New Mexico began imaging every visible object from over 35 percent of the sky in a project known as the Sloan Digital Sky Survey. Today, data is also collected from the Irénée du Pont Telescope at Las Campanas Observatory in Chile (APOGEE-2S). The Sloan Digital Sky Survey (SDSS) has been one of the most successful surveys in the history of astronomy. After a decade of design and construction, the SDSS began regular survey operations in 2000. It has progressed through several phases, SDSS-I (2000-2005), SDSS-II (2005-2008), SDSS-III (2008-2014), and SDSS-IV (2014+). Each phase has involved multiple surveys with interlocking science goals. This project proudly shares that they have already created the most detailed three-dimensional maps of the Universe ever made, with deep multi-color images of one third of the sky, and spectra for more than three million astronomical objects. The project has released fourteen data versions of their datasets thus far. They continue to release new data sets annually. The dataset scheduled for the end of this year will include spectral data across the face of the nearest ten thousand galaxies, instead of the previous surveys which obtained spectra only at the centers of target galaxies. The SDSS team calls this work “Mapping Nearby Galaxies at APO (MaNGA).” The dataset in 2019 will include information from the Apache Point Observatory Galaxy Evolution Experiment (APOGEE-2) to observe the “archaeological” record embedded in hundreds of thousands of stars to explore the assembly history and evolution of the Milky Way. You could say that the details as to how the Galaxy evolved are preserved today in the motions and chemical compositions of its stars.

It’s not hard to image that these ever-expanding datasets will offer even more opportunities for the Celeste collaboration in their analysis work.

Version 1.0

Prior work focused on non-statistical models. The Celeste collaboration focused on a statistical model, a fully generative model to be precise. Over the course of their first three years, the Celeste collaboration developed a new parallel computing method that was used to process the dataset (about 178 terabytes) and produce the most accurate catalog of 188 million astronomical objects in just 14.6 minutes with state-of-the-art point and uncertainty estimates.

In addition to creating a catalog, an important objective of this work was to identify promising galaxies for spectrograph targeting with the hope of better understanding dark energy and the geometry of the universe.

A key design objective of Celeste is to help be an extensible model and inference procedure for use by the astronomical community. This will allow more computation to be applied selectively if deeper understanding of any particular object is desired (e.g., brightness, rotation). Other applications might include finding supernovas or detecting near-Earth asteroids. The teams see enormous potential in the framework they have built. An hour-long presentation offers many more details of the work of Celeste 1.0 and is available for viewing online.

To help grasp the processing being done, here is a sample (using a synthetic image) of processing being done by an early prototype for Celeste 2.0. The synthetic image (the “input” to an autoencoder) is first, then the recon_mean is the mean of the approximation we find to the “output” of an autoencoder. The fact that it appears the same as the input is exactly what is desired! In Celeste 2.0, the recon_mean is formed by summing the four images to the right – which are the “deblended” images. These four images are hopefully useful to astronomers.

Envisioning Version 2.0

They first reported their petascale results last year, and they’ve been busy since then envisioning and developing “Celeste 2.0.” The collaboration is focused on moving to a more sophisticated inference model to replace the purely graphical model approach of Celeste 1.0, which was quite successful in its own right using only conventional variable inference. A key objective of this work is not only more accurate placement and features, but also more accurate uncertainties (“error bars”) for these as well.

Celeste 2.0 utilizes an autoencoder (variable) with a recurrent neural network (RNN), that also employs bayesian inference, and adds a gravitational lensing capability. The Bayesian inference technique is commonly associated with big data and machine learning projects, and typically  gets sharper predictions from data than other techniques. Bayesian inference effectively aims to inject some common sense (bias based on additional knowledge) into an otherwise sterile statistical analysis. In the case of Celeste 2.0, the newer techniques capture meaning from the vast dataset more accurately.

Bayesian models are composable, meaning that they work well as add-ons. This enables work on using Bayesian models to create a new gravitational lensing capability to undo the distortions which have occurred by the time it reaches a telescope. This is an area of active development, which promises to further refine the catalog of visible objects.

Endless Possibilities

Of course, I’m guessing work will not end with Celeste 2.0. They’ve opened up the challenge of building a catalog of the universe, and like all big data problems it has an insatiable appetite for more data. The continually growing sources of data in the SDSS offers many opportunities for the analysis work of the Celeste collaboration[1]. One day, perhaps gravitational wave data from the newest source of astronomy data can be incorporated? By then, we might also be able to offer them a data feed from a telescope sitting on Mars. It will happen.

In the meantime, the Celeste collaboration continues to make excellent use of the Intel processors in the Cori supercomputer with the Julia language. And this provides a wealth of encouragement for all big data projects looking to scale.

[1] The key contributors to the Celeste collaboration have been: Jeffrey Regier, Bryan Liu and Jon McAuliffeat of UC Berkeley ; Andy Miller and Ryan Adams of Harvard; David Schlegel of LBL Physics; and Prabhat of NERSC.

James Reinders is an HPC enthusiast and author of eight books with more than 30 years of industry experience, including 27 years at Intel Corporation (retired June 2016).

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!

IBM Research Scales to 11,400 Cores for EDA

August 5, 2021

For many HPC users, their needs are not evenly distributed throughout a year: some might need few – if any – resources for months, then they might need a very large system for a week. For those kinds of users, large Read more…

Careers in Cybersecurity Featured at PEARC21

August 5, 2021

The PEARC21 (Practice & Experience in Advanced Research Computing) Student Program featured a Cybersecurity Careers Panel. Five experts shared lessons learned from more than 100 years of combined experience. While it Read more…

HPC Career Notes: August 2021 Edition

August 4, 2021

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it’s a promotion, new company hire, or even an accolade, we’ Read more…

The Promise (and Necessity) of Runtime Systems like Charm++ in Exascale Power Management

August 4, 2021

Big heterogeneous computer systems, especially forthcoming exascale computers, are power hungry and difficult to program effectively. This is, of course, not an unrecognized problem. In a recent blog, Charmworks’ CEO S Read more…

Digging into the Atos-Nimbix Deal: Big US HPC and Global Cloud Aspirations. Look out HPE?

August 2, 2021

Behind Atos’s deal announced last week to acquire HPC-cloud specialist Nimbix are ramped-up plans to penetrate the U.S. HPC market and global expansion of its HPC cloud capabilities. Nimbix will become “an Atos HPC c Read more…

AWS Solution Channel

Pushing pixels, not data with NICE DCV

NICE DCV, our high-performance, low-latency remote-display protocol, was originally created for scientists and engineers who ran large workloads on far-away supercomputers, but needed to visualize data without moving it. Read more…

Berkeley Lab Makes Strides in Autonomous Discovery to Tackle the Data Deluge

August 2, 2021

Data production is outpacing the human capacity to process said data. Whether a giant radio telescope, a new particle accelerator or lidar data from autonomous cars, the sheer scale of the data generated is increasingly Read more…

Careers in Cybersecurity Featured at PEARC21

August 5, 2021

The PEARC21 (Practice & Experience in Advanced Research Computing) Student Program featured a Cybersecurity Careers Panel. Five experts shared lessons learn Read more…

Digging into the Atos-Nimbix Deal: Big US HPC and Global Cloud Aspirations. Look out HPE?

August 2, 2021

Behind Atos’s deal announced last week to acquire HPC-cloud specialist Nimbix are ramped-up plans to penetrate the U.S. HPC market and global expansion of its Read more…

What’s After Exascale? The Internet of Workflows Says HPE’s Nicolas Dubé

July 29, 2021

With the race to exascale computing in its final leg, it’s natural to wonder what the Post Exascale Era will look like. Nicolas Dubé, VP and chief technologist for HPE’s HPC business unit, agrees and shared his vision at Supercomputing Frontiers Europe 2021 held last week. The next big thing, he told the virtual audience at SFE21, is something that will connect HPC and (broadly) all of IT – into what Dubé calls The Internet of Workflows. Read more…

How UK Scientists Developed Transformative, HPC-Powered Coronavirus Sequencing System

July 29, 2021

In November 2020, the COVID-19 Genomics UK Consortium (COG-UK) won the HPCwire Readers’ Choice Award for Best HPC Collaboration for its CLIMB-COVID sequencing project. Launched in March 2020, CLIMB-COVID has now resulted in the sequencing of over 675,000 coronavirus genomes – an increasingly critical task as variants like Delta threaten the tenuous prospect of a return to normalcy in much of the world. Read more…

IBM and University of Tokyo Roll Out Quantum System One in Japan

July 27, 2021

IBM and the University of Tokyo today unveiled an IBM Quantum System One as part of the IBM-Japan quantum program announced in 2019. The system is the second IB Read more…

Intel Unveils New Node Names; Sapphire Rapids Is Now an ‘Intel 7’ CPU

July 27, 2021

What's a preeminent chip company to do when its process node technology lags the competition by (roughly) one generation, but outmoded naming conventions make it seem like it's two nodes behind? For Intel, the response was to change how it refers to its nodes with the aim of better reflecting its positioning within the leadership semiconductor manufacturing space. Intel revealed its new node nomenclature, and... Read more…

Will Approximation Drive Post-Moore’s Law HPC Gains?

July 26, 2021

“Hardware-based improvements are going to get more and more difficult,” said Neil Thompson, an innovation scholar at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). “I think that’s something that this crowd will probably, actually, be already familiar with.” Thompson, speaking... Read more…

With New Owner and New Roadmap, an Independent Omni-Path Is Staging a Comeback

July 23, 2021

Put on a shelf by Intel in 2019, Omni-Path faced a uncertain future, but under new custodian Cornelis Networks, OmniPath is looking to make a comeback as an independent high-performance interconnect solution. A "significant refresh" – called Omni-Path Express – is coming later this year according to the company. Cornelis Networks formed last September as a spinout of Intel's Omni-Path division. Read more…

AMD Chipmaker TSMC to Use AMD Chips for Chipmaking

May 8, 2021

TSMC has tapped AMD to support its major manufacturing and R&D workloads. AMD will provide its Epyc Rome 7702P CPUs – with 64 cores operating at a base cl Read more…

Berkeley Lab Debuts Perlmutter, World’s Fastest AI Supercomputer

May 27, 2021

A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…

Ahead of ‘Dojo,’ Tesla Reveals Its Massive Precursor Supercomputer

June 22, 2021

In spring 2019, Tesla made cryptic reference to a project called Dojo, a “super-powerful training computer” for video data processing. Then, in summer 2020, Tesla CEO Elon Musk tweeted: “Tesla is developing a [neural network] training computer called Dojo to process truly vast amounts of video data. It’s a beast! … A truly useful exaflop at de facto FP32.” Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

CentOS Replacement Rocky Linux Is Now in GA and Under Independent Control

June 21, 2021

The Rocky Enterprise Software Foundation (RESF) is announcing the general availability of Rocky Linux, release 8.4, designed as a drop-in replacement for the soon-to-be discontinued CentOS. The GA release is launching six-and-a-half months after Red Hat deprecated its support for the widely popular, free CentOS server operating system. The Rocky Linux development effort... Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Iran Gains HPC Capabilities with Launch of ‘Simorgh’ Supercomputer

May 18, 2021

Iran is said to be developing domestic supercomputing technology to advance the processing of scientific, economic, political and military data, and to strengthen the nation’s position in the age of AI and big data. On Sunday, Iran unveiled the Simorgh supercomputer, which will deliver.... Read more…

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

Leading Solution Providers

Contributors

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

AMD-Xilinx Deal Gains UK, EU Approvals — China’s Decision Still Pending

July 1, 2021

AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…

GTC21: Nvidia Launches cuQuantum; Dips a Toe in Quantum Computing

April 13, 2021

Yesterday Nvidia officially dipped a toe into quantum computing with the launch of cuQuantum SDK, a development platform for simulating quantum circuits on GPU-accelerated systems. As Nvidia CEO Jensen Huang emphasized in his keynote, Nvidia doesn’t plan to build... Read more…

Microsoft to Provide World’s Most Powerful Weather & Climate Supercomputer for UK’s Met Office

April 22, 2021

More than 14 months ago, the UK government announced plans to invest £1.2 billion ($1.56 billion) into weather and climate supercomputing, including procuremen Read more…

Quantum Roundup: IBM, Rigetti, Phasecraft, Oxford QC, China, and More

July 13, 2021

IBM yesterday announced a proof for a quantum ML algorithm. A week ago, it unveiled a new topology for its quantum processors. Last Friday, the Technical Univer Read more…

Q&A with Jim Keller, CTO of Tenstorrent, and an HPCwire Person to Watch in 2021

April 22, 2021

As part of our HPCwire Person to Watch series, we are happy to present our interview with Jim Keller, president and chief technology officer of Tenstorrent. One of the top chip architects of our time, Keller has had an impactful career. Read more…

Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008

July 14, 2021

After more than a decade of planning, the United States’ first exascale computer, Frontier, is set to arrive at Oak Ridge National Laboratory (ORNL) later this year. Crossing this “1,000x” horizon required overcoming four major challenges: power demand, reliability, extreme parallelism and data movement. Read more…

Senate Debate on Bill to Remake NSF – the Endless Frontier Act – Begins

May 18, 2021

The U.S. Senate today opened floor debate on the Endless Frontier Act which seeks to remake and expand the National Science Foundation by creating a technology Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire