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!

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together about 30 participants from industry, government and academia t Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Researchers Scale COSMO Climate Code to 4888 GPUs on Piz Daint

October 17, 2017

Effective global climate simulation, sorely needed to anticipate and cope with global warming, has long been computationally challenging. Two of the major obstacles are the needed resolution and prolonged time to compute Read more…

By John Russell

HPE Extreme Performance Solutions

Transforming Genomic Analytics with HPC-Accelerated Insights

Advancements in the field of genomics are revolutionizing our understanding of human biology, rapidly accelerating the discovery and treatment of genetic diseases, and dramatically improving human health. Read more…

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Cluster Competition coverage has come to its natural home: H Read more…

By Dan Olds

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together ab Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Read more…

By Dan Olds

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan Read more…

By John Russell

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

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

Intel Debuts Programmable Acceleration Card

October 5, 2017

With a view toward supporting complex, data-intensive applications, such as AI inference, video streaming analytics, database acceleration and genomics, Intel i Read more…

By Doug Black

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

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

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

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

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

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

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

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Leading Solution Providers

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

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

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

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. 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

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

Intel, NERSC and University Partners Launch New Big Data Center

August 17, 2017

A collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel and five Intel Parallel Computing Cente Read more…

By Linda Barney

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