COSMOS Team Achieves 100x Speedup on Cosmology Code

By Tiffany Trader

August 24, 2015

One of the most popular sessions at the Intel Developer Forum last week in San Francisco, and certainly one of the most exciting from an HPC perspective, brought together two of the world’s foremost experts in parallel programming to discuss current state-of-the-art methods for leveraging parallelism on processors and coprocessors. The speakers, Intel’s Jim Jeffers and James Reinders, are also the co-editors of the just-published “High Performance Parallelism Pearls Volume Two: Multicore and Many-core Programming Approaches.”

You aren’t likely to meet two more engaging and engaged programmers who make learning about this stuff fun, even for this non-coder interloper. In writing the two volumes, the duo saw “example after example get performance and performance portability with ‘just parallel programming.'”

Many of the chapters focus on porting codes to the MIC Phi multicore processor, but in the process, the Xeon processors also accrued significant speedups, often 5x or more. As for why developers did not exploit this parallelism until they had the Phi in hand, Reinders and Jeffers refer to this phenomenon as the “inspiration of 60+ cores.”

High Performance Parallelism Pearls Vol2 coverLike the first volume, High Performance Parallelism Pearls Volume Two (Morgan Kaufmann, 2015) offers a sampling of successful programming efforts, demonstrating how to leverage parallelism from Intel Xeon and Xeon Phi processors across multiple vertical domains in science and industry. The book has been published with the spirit of knowledge sharing and all of the figures and source code are available for download to facilitate further exploration.

The IDF15 session (see slides) was focused on providing the developer audience with useful stories and examples for programming for high performance. While the editors are careful in saying they don’t have favorite chapters, the success story related in chapter 10, titled “Cosmic Microwave Background Analysis: Nested Parallelism in Practice,” stands out for its scientific accomplishment and for its programming prowess.

The chapter, which is featured on the cover of the book, highlights the work of researchers in Stephen Hawking’s group at the University of Cambridge, who achieved over a 100x speedup with optimizations carried out in the process of porting their code to the Intel Xeon Phi coprocessor (Knights Corner). The theoretical physicists at Cambridge use a simulation code called MODAL to probe the Cosmic Background Radiation (CMB), a microwave frequency background radiation left over from the Big Bang. In analyzing this data from the origin of the universe and verifying it against theoretical observations, the team is reconstructing the CMB bispectrum for the first time. What is truly remarkable is that in seeking to understand how the universe emerged out of an intense period of expansion, called inflation, the research team has found evidence of extra dimensions.

A production run using the original Modal code (unoptimized, pure MPI) takes about six hours on 512 Intel Xeon E5-4650L cores of the COSMOS SGI supercomputer. If it can be sped-up then it will greatly enhance the cross-validation process, which requires the code be run many times.

Write the authors:

“The calculation performed by Modal is a prime candidate for Intel Xeon Phi coprocessors — the inner product calculations are computationally very expensive, independent of one another, and require very little memory (with production runs using only O(100) MB of RAM and writing only O(1) MB to disk). However, the code as written does not express this calculation in a way that is conducive to the utilization of modern hardware. Our acceleration of Modal therefore has two components: tuning the code to ensure that it runs efficiently (i.e., optimization); and enabling the code to scale across vectors and many cores (i.e., modernization). Extracting performance from current and future generations of Intel Xeon processors and Intel Xeon Phi coprocessors is impossible without parallelism, and the process of optimization and modernization presented here is imperative for ensuring that COSMOS stay at the forefront of cosmological research.”

The chapter — written by James P. Briggs, James R. Fergusson, Juha Jäykkä, Simon J. Pennycook and Edward P. Shellard — details the 10-step process of optimizations, illustrated below:

Accelerating Cosmic Microwave Background Briggs speedup

Accelerating Cosmic Microwave Background Briggs code versions 1-10

The experiment was carried out using a dual socket Intel Xeon processor E5-4650L and an Intel Xeon Phi coprocessor 5110P with the Intel Composer XE 2015 (v15.0.0.090) compiler.

In addition to showcasing the potentially paradigm-changing science that is being enabled, the chapter, and a related paper from the authors, are salient teaching tools, reflecting the hallmarks of effective parallelism, including one that is sometimes omitted from discussion.

Here Jeffers begins reviewing what he and Reinders have long identified as the three most important vectors of parallelism: “data locality, that is making sure your data is structured properly for the parallelism pipeline; threading or scalability; and then vectorization, taking advantage of the syncing capability.”

“But what did we forget?” Reinders calls out.

“What we forgot,” said Jeffers, “is that you should actually analyze your code and see from an algorithm standpoint what you might be able to do to improve your code.”

“So the biggest leap here was this,” Jeffers continues. “[The developers] were moving forward with parallelism, you see they are getting pretty good gains up through [code version] six. They are moving forward. They have the original code. They did some loop modifications and then at number three, Intel MKL integration routines come in. When they hit step seven, they have been v-tuning their code, looking at the hotspots, and then boom, the MKL integration routine is the hotspot. So they picked the one that best met the inputs and outputs they wanted. It turns out they didn’t need all the power of that, the precision, etc. So they wrote their own. They used the new trapezium rule integrator and bang [performance shoots up] — so, it’s not all about the three vectors.”

“So don’t forget your algorithms,” adds Reinders, emphatically. “Do you really need the algorithm you are using? They went from a 10x to a 60x speedup in that one step, and it was an algorithm change and it affected Xeon and Xeon Phi almost equally.”

“And this is a production code,” Jeffers emphasizes, “extremely important to them, to their analysis, and really to the world in understanding the universe.”

From the COSMOS team: “We find that using a simple trapezium rule integrator combined with hand-selected sampling points (to improve accuracy in areas of interest) provides sufficient numerical accuracy to obtain a physically meaningful result, and the reduced space and time requirements of this simplified method give a speed-up of O(10x).”

A summary of the team’s conclusions appears in a presentation posted to manycore.com:

CMD Intel Cambridge Briggs Conclusions
“The total speed-up relative to the original baseline code is close to 100x on both platforms,” the authors write in chapter 10 of the new Pearls volume. “Further the results shown here use only two processor sockets or one coprocessor–by dividing the complete problem space across nodes using MPI, and then subdividing across the processor and coprocessor present in each node, the calculation can be accelerated even further. These optimizations have thus enabled COSMOS to completely change the way in which the code is used; rather than running on the entire system for hours, after careful selection of cosmological parameters, Modal can now be incorporated as part of a larger Monte Carlo pipeline to quickly evaluate the likelihood of alternative parameters.”

On the IDF15 showroom floor, Intel demonstrated a visualization of the cosmic background radiation rendered with the open-source OSPRay Ray Tracing engine running live on two pre-production Intel Knights Landing cards connected by the Omni-Path pre-production fabric. Being able to observe the Planck data with this tool allows scientists to see correlations predicted by Einstein’s theory of general relativity.

Intel COSMOS CBR visualization IDF15 1200x

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!

Scalable Informatics Ceases Operations

March 23, 2017

On the same day we reported on the uncertain future for HPC compiler company PathScale, we are sad to learn that another HPC vendor, Scalable Informatics, is closing its doors. Read more…

By Tiffany Trader

‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies

March 23, 2017

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings (John Wiley & Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

HPE Extreme Performance Solutions

HFT Firms Turn to Co-Location to Gain Competitive Advantage

High-frequency trading (HFT) is a high-speed, high-stakes world where every millisecond matters. Finding ways to execute trades faster than the competition translates directly to greater revenue for firms, brokerages, and exchanges. Read more…

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break ‘Speed Limit’

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory (LANL) is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Intel Ships Drives Based on 3D XPoint Non-volatile Memory

March 20, 2017

Intel Corp. has begun shipping new storage drives based on its 3D XPoint non-volatile memory technology as it targets data-driven workloads. Intel’s new Optane solid-state drives, designated P4800X, seek to combine the attributes of memory and storage in the same device. Read more…

By George Leopold

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Leading Solution Providers

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

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