Cycle Spins Up 50,000-core Cluster in Amazon Cloud

By Tiffany Trader

April 24, 2012

The case for utility supercomputing just got a lot bigger, literally. Cycle Computing has created a 50,000 core virtual supercomputer to assist in the development of novel drug compounds for cancer research. The cluster, codenamed Naga, sits in the Amazon infrastructure and is the biggest utility supercomputer yet. Using this mega-cluster, computational chemistry outfit Schrödinger was able to analyze 21 million drug compounds in just 3 hours for less than $4,900.

molecules

Developing a real compound, or assay, is very expensive, so before you do this you need to test all leads on a computer. Schrödinger and research partner Nimbus Discovery are working to identify important targets that have so-far been unsuccessful. They are looking for “hits” – a process that occurs very early on in the drug discovery cycle. After the hit stage comes the hit-to-lead phase and then lead optimization. Lead optimization produces a development candidate for human trials. But the process has to start somewhere, and that’s where virtual screening comes in. It’s the foundation for 2 to 5 years of discovery.

Schrödinger uses its proprietary docking application, Glide, to virtually screen different compounds against a potential cancer drug target. Out of a huge number of compounds, the computer model will whittle the initial pool down to the most worthy candidates. With Glide, as with most computer models, there’s a trade-off between accuracy and speed. Shortcuts are commonly employed to accommodate resource and time limitations.

Glide employs a series of progressive refinements, each an order of magnitude more computationally-intensive than the last. The first pass is performed with the fastest, least-accurate Glide algorithm, HTVS, which stands for, high-throughput virtual screening. About 10 percent of the initial candidates make it through to the next round, which is called SP, for standard precision. The third and final phase is XP for extra precision. This round takes 10 percent of the compounds from the previous run and outputs only the most worthy drug candidates, the ones most capable of affecting the targeted disease proteins.

The same tradeoff that allows researchers to analyze more compounds at a faster rate also leads to a significant number of false negatives and positives because the lower-quality algorithm may fail to identify good candidate compounds, while letting false positives slip by. The greater risk here is that potential blockbuster drugs are just passed over.

The utility nature of the Amazon supercomputer allows the scientists to skip the first step and move right to the second, more accurate mode. It also allows them to increase their compound set by a factor of three. So while normally they input 6 to 7 million compounds, they can now start with 21 million. Applying the higher-quality algorithm to a larger compound set reduces the problem of false negatives. The researchers are then able to identify the active compounds that would otherwise fall through the cracks.

While Schrödinger makes heavy use of its internal clusters, it requires additional resources for particularly compute-intensive workloads. With the Naga cluster, Schrödinger researchers were able to run this exceptionally large workload in record time, 21 million compounds and confirmations in just 3 hours. By comparison, running the same job on their internal 400-core cluster would take about 275 hours. Initial data sets are on the order of tens of gigabytes of molecule data, and depending on Internet bandwidth, uploading the data can take about 5 to 6 hours. Since the library of compounds is largely a static data set, it only needs to be updated once every six months or so.

“This project reflects the major trends we are seeing in medicine today. It’s the age of analytics and simulation, meaning big data and big compute,” remarks Cycle Computing CEO Jason Stowe. “We’re also seeing requirements on time-to-market and being capital-efficient. Building a 50,000 core infrastructure is a $20-30 million endeavor,” he adds.

Naga AWS deployment map
A map of the AWS compute resources harnessed during the Naga run

 

Next page >>>

A cutting-edge scientific research outfit, Schrödinger still has to face the economic realities that go along with being a small business. CEO Ramy Farid agrees that purchasing a system like this outright would be extremely expensive. Even more to the point, virtual screening is done sporadically. Farid estimates that over the course of a year, they do maybe 25 virtual screens at 3 hours each. While they do have other computational work to perform, it’s not enough to justify additional in-house resources, and certainly not a supercomputer of this ilk.

Farid points out that this dramatic increase in the number of processors lets you do better science. “It’s been like that since computing started,” he notes. As an example, Farid recalls the days when scientists had to intentionally omit hydrogen atoms on structures because computers just weren’t fast enough.

Schrödinger also uses the Cycle-based Amazon cloud to offload some of its lead optimization work, which involves doing calculations to predict binding affinity. Although not at the scale of the virtual screening process, lead optimization is still quite compute-intensive. Farid characterizes this work as the holy grail of computational chemistry, and using the Cycle setup, they’ve been able to take work that would require several months on a cluster down to a weekend on the cloud.

This speaks to the paradigm shift that Stowe is so passionate about. Despite the exponential advances in compute power driven by Moore’s Law, access to HPC resources is still one of the biggest constraints in research. Utility computing is creating a new dynamic by providing virtually unlimited computing power on-demand, and the user only has to pay for what they use. Researchers are accustomed to having to frame their questions according to the resources they have available, but the new model allows researchers to ask the most important questions, the ones that will actually move the science forward.

This 50,000-core cloud is the largest Cycle has constructed for a client, but the HPC software company has created a number of notable clusters. Last year, they did a 10,000-core run with Genentech, and a 30,000-core run with a top 5 pharmaceutical firm. Stowe points out that those organizations, however, were quite large, so theoretically-speaking, could have purchased a cluster of that size outright. What makes Schrödinger such an ideal use case, according to Stowe, is how Cycle and Amazon were able to provide a resource that otherwise would have been out of reach.

“Cycle Cloud automates the process of turning raw infrastructure into usual HPC environments,” says Stowe. “It’s like using a TOP500 supercomputer for a few hours and then turning it off.”

 Naga: Facts and Figures
 Metric  Count
 Compute Hours of Work  109,927 hours
 Compute Days of Work  4,580 days
 Compute Years of Work   12.55 years
 Ligand Count  ~21 million ligands
 Run Time  ~3 hours
 Grand Total Cost at Peak: $4,828/hour    ( $0.09 / core / hour )

 

 

 

 

 

 

 

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!

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

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

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

HPE Extreme Performance Solutions

Object Storage is the Ideal Storage Method for CME Companies

The communications, media, and entertainment (CME) sector is experiencing a massive paradigm shift driven by rising data volumes and the demand for high-performance data analytics. Read more…

Weekly Twitter Roundup (Feb. 16, 2017)

February 16, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

Alexander Named Dep. Dir. of Brookhaven Computational Initiative

February 15, 2017

Francis Alexander, a physicist with extensive management and leadership experience in computational science research, has been named Deputy Director of the Computational Science Initiative at the U.S. Read more…

Here’s What a Neural Net Looks Like On the Inside

February 15, 2017

Ever wonder what the inside of a machine learning model looks like? Today Graphcore released fascinating images that show how the computational graph concept maps to a new graph processor and graph programming framework it’s creating. Read more…

By Alex Woodie

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for 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

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

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

Cray Posts Best-Ever Quarter, Visibility Still Limited

February 10, 2017

On its Wednesday earnings call, Cray announced the largest revenue quarter in the company’s history and the second-highest revenue year. Read more…

By Tiffany Trader

HPC Cloud Startup Launches ‘App Store’ for HPC Workflows

February 9, 2017

“Civilization advances by extending the number of important operations which we can perform without thinking about them,” Read more…

By Tiffany Trader

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

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

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

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

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

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

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

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

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

Leading Solution Providers

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

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

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

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. 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

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. 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

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