Benchmarking HPC in the Cloud

By Tiffany Trader

June 10, 2014

All clouds are not the same. It’s an adage that rings especially true when it comes to running high-performance computing (HPC) workloads. HPC middleware solutions vendor Techila Technologies recently took the time to benchmark and analyze three of the top cloud platforms – Amazon Web Services, Google Compute Engine, and Microsoft Azure – in the context of several real-world high-performance computing scenarios. The results are detailed in a subsequent report, titled simply “Cloud Benchmark – Round 1.”

“If the technical features of a cloud do not align with the needs of business, a solution which looks cost efficient can have a high cost of ownership.” This observation by Techila speaks to why the benchmarking was carried out, to explore which cloud offerings and instance types work best for a given application.

Techila HPC cloud benchmark Table1

Techila explains that the benchmark experiment was intended to provide HPC customers with an easy-to-understand analysis. Potential cloud adopters have told the company that FLOPS-per-dollar and Gbps-per-dollar are interesting but do not adequately answer their questions or address their concerns.

“Raw processor power, available memory, or theoretical maximum data transfer rate do not always translate directly to application performance,” writes Techila. “Because of this, the focus of [the] benchmark experiment is on testing the performance of AWS, Google Compute Engine GCE, and Azure in real-world HPC use-cases, and on studying how the leading clouds can respond to requirements arising from HPC scenarios.”

The test suite that Techila used was developed with the participation of cloud providers and users of MATLAB, R programming language, and simulation-backed engineering tools. After the first round of testing, the primary conclusion was that not all platforms demonstrate the same level of elasticity.

Tests fell into two categories: deployment and application performance. The first test zeroed in on a cloud’s ability to respond to computing needs. The focus was directed to embarrassingly parallel problems, which can scale to best use a large number of cores. (Techila says it is planning MPI-like tests in the future.)

The experiment set out to answer several questions, such as:

What instance types provide the best performance? Should I use the most expensive instance types?
Does the operating system of the cloud have effect on the throughput of the system?
Should I worry about the internal infrastructure of the cloud?

For convenience, Techila provides a chart of each cloud’s technical specifications (see above). With regard to instance types, for Azure, the report looked at A8 (with Windows) and the Extra Large (A4) (also with Windows). For AWS, two implementations of c3.8xlarge were examined, one with Windows and one with Linux. And for Google Compute Engine (GCE), they used n1-standard-8 (with Debian 7).

While cloud pricing has gone through many revisions, the prices at the time of the experiment are also listed. The price per CPU core/hour in US dollars ranges from .060 (for AWS with Linux) to .306 for Azure A8.

The deployment tests analyzed the deployment of a 256 CPU core virtual HPC environment in a cloud. Among the interesting findings, Techila observed that deployments with Microsoft Windows operating system take longer than instance types with a Linux operating system. The authors suggest this is likely related to System Preparation (Sysprep) phase, which occurs during the installation of Microsoft Windows.

Techila HPC cloud benchmark Fig1

Another finding relates to the shape of the AWS c3.8xlarge and Azure A8 Windows instances. The deployment is not linear. The report’s authors suggest that “a possible reason for this is that the availability of these instance types is still quite limited and datacenters have challenges in responding to a request for a large number of these instance types.”

Testing deployment on Azure was not possible in this experiment because Azure is designed as a Platform-as-a-Service (PaaS) and does not provide the needed Java management interfaces for the current version of the Techila Deployment Tool.

The configuration tests examined how MATLAB-based applications fare in a 256 CPU core virtual HPC environment. The findings show that configuration of an instance was slower in Azure than the other cloud offerings. They reason that this could be do to Azure’s PaaS-based design. AWS and GCW, however provide direct access to the infrastructure. “Because of the limitations of Azure’s PaaS design Techila middleware can not support Peer-to-Peer (P2P) transfer technology inside the HPC environment in Azure,” note the report’s authors.

Another key observation was that configuring the AWS instance was quicker with Linux than Windows. While the experimenters can’t confirm the basis for this, they think it might be explained by file system capabilities. The data transferred was said to contain approximately 33,000 files, and it’s been suggested that the file system on Windows performs slower when handling a large number of rather small files.

The HPC application tests looked at three common application scenarios:

  • model calibration (using MATLAB code)
  • portfolio simulation (implemented in R)
  • machine learning (implemented in C++)

Techila provides detailed assessments of each application case, with charts that include Wall-clock time, price per CPU core and cost of cloud computing.

Here are several of the interesting observations made by the experimenters:

For MATLAB code:

“The findings show that in this particular scenario MATLAB seems to perform better in Windows environment than on Linux environments.”

For R users:

“An interesting observation is related to the performance of AWS c3.8xlarge performance. When compared to Azure A8 and Azure Extra Large, we can see that in this case, the Azure Extra Large provides a very similar performance as AWS c3.8xlarge, and Azure A8 provides double performance compared to AWS c3.8xlarge and Azure Extra Large. Because the cost of Azure Extra Large is affordable and Azure supports a fine granularity billing, this can make Azure Extra Large a great value option for users of R programming language.”

“Another interesting observation is that in this case AWS c3.xlarge with Linux provides clearly better performance than AWS c3.8xlarge running Windows operating system.”

For machine learning:

“Another interesting observation is that in this specific case Azure A8 and AWS c3.8xlarge with Windows operating system provided very similar performance, despite of differences observed in other test cases. It was suggested that this could be related to the fact that some scenarios are well suited for hyper threading and can benefit of it. Because of this, if the goal is to get the most out of a hyper threading platform, it is important to understand the suitability of the applications for the platform.”

Based on the results of Techila’s first cloud benchmarking round, the company is confident that cloud computing will have a role to play in HPC. The experimenters also believe that cloud will have a profound democratizing effect on HPC, writing:

“HPC will no longer be science, which would require special training and expensive upfront investments. Cloud will bring HPC to new desks and simplified user experience will empower new users to benefit of it.”

The testing process also served as a reminder that commercial cloud platforms follow more of a hardware path in that they don’t use version numbering. Vendors are constantly pushing out new instance types and features, and prices too are under constant revision. Because of this, any benchmarking must be regarded as work in progress. To stay relevant with these changes, Techila is planning to keep its report up to date by repeating tests periodically.

Techila also raises the point that elasticity is not truly unlimited. Resource provisioning, even at the scale of Amazon, etc., is still limited by physical boundaries. Aside from impacting the planning stage, Techila maintains that the physical architecture is the reason why HPC in the cloud needs middleware.

“Performing such experiments in a loosely coupled infrastructure, such as the cloud, requires a middleware, which enables horizontal scaling and can hide the possible nonlinearities of the physical infrastructure,” the report states. “After all, cloud is built of very similar units what we see in our offices. When we come to the limits to the physical unit’s scalability, we need a solution, which enables scaling over the limit, which in this experiment was the Techila HPC middleware.”

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!

TACC Researchers Test AI Traffic Monitoring Tool in Austin

December 13, 2017

Traffic jams and mishaps are often painful and sometimes dangerous facts of life. At this week’s IEEE International Conference on Big Data being held in Boston, researchers from TACC and colleagues will present a new Read more…

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in what has become an overwhelmingly two-socket landscape in the d Read more…

By John Russell

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as tech giants jockey to establish a pole position in the race toward commercialization of quantum. This week, Microsoft took the next step in advanci Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Explore the Origins of Space with COSMOS and Memory-Driven Computing

From the formation of black holes to the origins of space, data is the key to unlocking the secrets of the early universe. Read more…

ESnet Now Moving More Than 1 Petabyte/wk

December 12, 2017

Optimizing ESnet (Energy Sciences Network), the world's fastest network for science, is an ongoing process. Recently a two-year collaboration by ESnet users – the Petascale DTN Project – achieved its ambitious goal t Read more…

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in wha Read more…

By John Russell

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as tech giants jockey to establish a pole position in the race toward commercialization of Read more…

By Tiffany Trader

HPC Iron, Soft, Data, People – It Takes an Ecosystem!

December 11, 2017

Cutting edge advanced computing hardware (aka big iron) does not stand by itself. These computers are the pinnacle of a myriad of technologies that must be care Read more…

By Alex R. Larzelere

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

Microsoft Spins Cycle Computing into Core Azure Product

December 5, 2017

Last August, cloud giant Microsoft acquired HPC cloud orchestration pioneer Cycle Computing. Since then the focus has been on integrating Cycle’s organization Read more…

By John Russell

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

HPE In-Memory Platform Comes to COSMOS

November 30, 2017

Hewlett Packard Enterprise is on a mission to accelerate space research. In August, it sent the first commercial-off-the-shelf HPC system into space for testing Read more…

By Tiffany Trader

SC17 Cluster Competition: Who Won and Why? Results Analyzed and Over-Analyzed

November 28, 2017

Everyone by now knows that Nanyang Technological University of Singapore (NTU) took home the highest LINPACK Award and the Overall Championship from the recently concluded SC17 Student Cluster Competition. We also already know how the teams did in the Highest LINPACK and Highest HPCG competitions, with Nanyang grabbing bragging rights for both benchmarks. Read more…

By Dan Olds

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

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

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

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

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Leading Solution Providers

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

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

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

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

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

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

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

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