When Time Is of the Egress: Optimizing Your Transfers

By Andrew Kaczorek and Dan Harris

July 31, 2012

Traditionally running scientific workloads in AWS provides a diverse toolkit that allows researchers to easily sling data around different time zones, regions, or even globally once the data is inside of the infrastructure sandbox. However, getting data in and out of AWS has historically been more of a challenge. The available resources are still evolving and those pesky laws of physics tend to get in the way. Considering the rise of enterprises utilizing cloud for larger data and compute needs and the complexities that come with it, we thought it would be helpful to offer tips on optimizing ingress and egress transfers.

Within scientific computing there is a massive disconnect from theoretical conversations and the real world of data movement. We recently performed a data transfer to Amazon’s Elastic Compute Cloud using their Import/Export service. The service allows customers to mail in data on physical media which is then placed into a S3 bucket or EBS volume of their choice. As an experiment to compare this transfer to network-based transfer mechanisms like multi-stream upload to S3, we recorded all the time it took to prepare and ship the drive to Amazon.

There were several steps to transfer the 317 GBs of DNA sequence data into EC2:

  1. Installed AWS Import/Export command line tools.

  2. Created an Import job using AWS command line tools including a manifest and signature.

  3. Realized that the drive is an ext3 file system (and mounting ext3 on OS X is non-trivial).

  4. Created an Ubuntu virtual machine.

  5. Mounted the drive on the Ubuntu VM and wrote the signature file and manifest to the drive.

  6. Physically labeled the drive with a transfer ID that was provided by the registration process.

  7. Packaged and addressed the drive with a specific address that was to be used for the shipment.

  8. Headed to the local FedEx and shipped the drive overnight.

  9. Waited….

  10. Viewed completed transfer logs.

The next step had us moving the data from S3 to an EC2 instance to use it in a computation run. Direct to EBS snapshot is an option, but due to its higher costs as an image of the drive, the unknowns associated with the newness of the feature, and the constrains to the specific content of the file system, we decided against it.

Table of Shipping and Transport Times:

Prepare Drive

3 hr (concurrent with other project work)

Drive Shipped

4:12 PM EST (FedEx log)

Drive Arrives IAD

3:20 AM EST (FedEx log)

Drive Arrives at Amazon facility

9:45 AM EST (FedEx log)

Drive accepted by Amazon

1:13 PM EST (I/E toolkit log)

Data transfer begins

5:40 PM EST (I/E toolkit log)

Data transfer completes

9:17 PM EST (I/E toolkit log)

Here is a summary of the entire activity:

Total time to transfer 317GB

32 hours

Extrapolated total time to transfer 1TB

39.8 hours

Throughput of active AWS transfer

199 Mbps

Active AWS transfer of 317GB

3.6 hours

Extrapolated active AWS transfer of 1TB

11.4 hours

Overall throughput of 317GB transfer

22.5 Mbps

Extrapolated overall throughput of 1TB transfer

57.2 Mbps

This import job was compared to the results on some recent multi-stream upload tests performed with an envy-inducing 5 Mbps upload speed compared to 1 Mbps.

File Size

Transfer Time

Avg Speed

250 MB – one thread

413 seconds

.605 MB/sec (4.84 Mbit/sec)

250 MB – 30 threads

412 seconds

.606 MB/sec (4.84 Mbit/sec)

1 GB – one thread

1,695 seconds

.604 MB/Sec (4.83 Mbit/sec)

1 GB – 30 threads

1,693 seconds

.605 MB/sec (4.84 Mbit/sec)

We were able to saturate upload bandwidth and ingress at customer sites, which have much higher outbound data rates in the 50 Mbps range. Further, if there’s a bottleneck for delivering data over the wire it’s on the source end and not on the EC2 end of the line.

The results showed that 50 Mbps of upload speed could saturate a company’s network therefore throttling transfer at 70 percent total bandwidth for an outbound rate 35 Mbps. Interestingly, the transfer speed is faster than the Import/Export service. This shows that almost 500 GB could be moved in the same time it took to transfer by shipping the drive. This drive wasn’t filled to capacity and the theoretical Import/Export throughput would use a full drive by extrapolating the time to load 1TB. Loading that extra data would take about 8 more hours and increase the throughput of the Import/Export approach to 58 Mbps. The rate could also increase if the time it takes to prep the drive was reduced.

What we found from our experiment is that the nature of your workflow should be considered when deciding which transfer method to use. If producing a constant flow of data at a rate that matches the allotted upload bandwidth, streaming over the network is a better option. On the other hand, if there is a large, pre-existing data set and no time to wait for it to upload consider using Amazon’s Import/Export service.

Initiating a transfer entirely in software and having the data eventually make its way into the cloud without getting up from your desk is not always practical. For example, a 317 GB payload would take approximately 30 hours to transfer to AWS if using the Import/Export job approach and 30 days to import 1 Mbps uplink was saturated 24/7. Given a typical enterprise uplink of 50 Mbps, the tables would be turned. Let’s not forget non-technical factors involved in the use of the Import/Export approach such as the hassle handling USB drives, cardboard, packing tape, and cranky shipping depot employees.

Lastly, if the over-the-wire transfer is projected to take longer than a business week, use an AWS Import/Export job instead. AWS Import/Export is an extremely viable way of managing the ingress and egress of data until bandwidth becomes more ubiquitous and plentiful.

Editor’s Note: The original byline was incorrectly attributed to Cycle Computing CEO Jason Stowe.

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!

Topological Quantum Superconductor Progress Reported

February 20, 2018

Overcoming sensitivity to decoherence is a persistent stumbling block in efforts to build effective quantum computers. Now, a group of researchers from Chalmers University of Technology (Sweden) report progress in devisi Read more…

By John Russell

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penalties to HPC applications. Even as these patches are rolled o Read more…

By Pete Beckman

Intel Touts Silicon Spin Qubits for Quantum Computing

February 14, 2018

Debate around what makes a good qubit and how best to manufacture them is a sprawling topic. There are many insistent voices favoring one or another approach. Referencing a paper published today in Nature, Intel has offe Read more…

By John Russell

HPE Extreme Performance Solutions

Safeguard Your HPC Environment with the World’s Most Secure Industry Standard Servers

Today’s organizations operate in an environment with ever-evolving threats, and in order to protect themselves they must continuously bolster their security strategy. Hewlett Packard Enterprise (HPE) and Intel® are addressing modern security challenges with the world’s most secure industry standard servers powered by the latest generation of Intel® Xeon® Scalable processors. Read more…

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penal Read more…

By Pete Beckman

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

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

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

The Food Industry’s Next Journey — from Mars to Exascale

February 12, 2018

Global food producer and one of the world's leading chocolate companies Mars Inc. has a unique perspective on the impact that exascale computing will have on the food industry. Read more…

By Scott Gibson, Oak Ridge National Laboratory

Singularity HPC Container Start-Up – Sylabs – Emerges from Stealth

February 8, 2018

The driving force behind Singularity, the popular HPC container technology, is bringing the open source platform to the enterprise with the launch of a new vent Read more…

By George Leopold

Dell EMC Debuts PowerEdge Servers with AMD EPYC Chips

February 6, 2018

AMD notched another EPYC processor win today with Dell EMC’s introduction of three PowerEdge servers (R6415, R7415, and R7425) based on the EPYC 7000-series p Read more…

By John Russell

‘Next Generation’ Universe Simulation Is Most Advanced Yet

February 5, 2018

The research group that gave us the most detailed time-lapse simulation of the universe’s evolution in 2014, spanning 13.8 billion years of cosmic evolution, is back in the spotlight with an even more advanced cosmological model that is providing new insights into how black holes influence the distribution of dark matter, how heavy elements are produced and distributed, and where magnetic fields originate. 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

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

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

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

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

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

Leading Solution Providers

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

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

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

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

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

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 wa Read more…

By John Russell

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