Comprehensively Evaluating HPC Cloud Cost Benefits

By Ian Armas Foster

July 30, 2013

HP Labs partnered with the University of Illinois at Champaign-Urbana to comprehensively evaluate the feasibility of running high performance applications in the cloud. The research set out to answer many questions, including wondering how HPC applications fare in the cloud versus supercomputers (they used the Ranger and Taub machines for those tests), which applications were best suited for cloud deployment, and what the cost benefits were for certain organizations in maintaining their high performance needs in a cloud.

Below is a grid of all the platforms they used in testing their various applications. As one can see, the Ranger and Taub systems are there along with public and private cloud instances.

It is important to note the approach the research team took with setting up their cloud systems. While they could have built a dedicated instance that would perform closer to supercomputing standards, they figured that such an instance would be unlikely in the scenario of a mid-sized enterprise or startup looking to purchase on-demand HPC resources.

With that said, they still took steps to optimize the performance. “To get maximum performance from virtual machines, we avoided any sharing of physical cores between virtual cores. In case of cloud, most common deployment of multi-tenancy is not sharing individual physical cores, but rather done at the node, or even coarser level. This is even more true with increasing number of cores per server.”

They tested those cloud systems and the control supercomputers on a variety of applications, including Jacobi2D, used for scientific simulation and image processing, NAMD, a molecular dynamics application, ChaNGa, used for cosmology simulation, and the NQueens problem among others.

The graphs above show how well the various machines’ performance scaled relative to the various applications. The applications that reportedly found trouble scaling were those that were communication intensive. “IS is a communication intensive benchmark and involves data reshuffling and permutation operations for sorting. Sweep3Dalso exhibits poor weak scaling after 4–8 cores on cloud. Other communication intensive applications such as LU, NAMD and ChaNGa also stop scaling on private cloud around 32 cores,” the report noted.

In all instances except for the public cloud, the EP, Jacobi2D and NQueens applications scaled up to 256 cores, while the public cloud imposed performance penalties once more than four cores were used.

Once the performance drop off was established for clouds, a fact that was altogether not surprising, the next task was to determine exactly what kind of penalty was suffered, such that they could relate that to the cost of apportioning those systems in the process of determining if cloud is indeed a cost effective means of securing HPC resources.

To quantify the amount of variability on cloud and compare it with a supercomputer, we calculated the coefficient of variation (standard deviation/mean) for execution time of ChaNGa across 5 executions,” the report stated. According to the research team, the amount of variability increases as they scale up as a result of decrease in granularity. “For the case of 256 cores at public cloud, standard deviation is equal to half the mean, implying that on average, values are spread out between 0.5x mean and 1.5x mean resulting in low predictability of performance across runs. In contrast, private cloud shows less variability.”

Overall, latency and bandwidth on cloud ended up coming in a couple of orders of magnitude below that of their Ranger and Taub machines, as shown in the logarithmic graphs below.

These bandwidth and latency issues make it difficult on those aforementioned communication intensive applications, where obviously contact among cores and nodes to complete a problem is key.

Again, the researchers note that a dedicated public cloud instance would solve a great deal of these problems. However, such an instance would likely cost more and therefore become less feasible for the mid-sized companies and startups that would utilize it. The multi-tenancy cloud setup renders many high performance applications untenable. “The performance of many HPC applications is very sensitive to the interconnect, as we showed in our experimental evaluation. In particular low latency requirements are typical for the HPC applications that incur substantial communication. This is in contrast with the commodity Ethernet network (1Gbps today moving to 10Gbps) typically deployed in cloud infrastructure,” the report noted.

With that said, it is still prudent for those smallmedium companies to enlist cloud-based HPC services, as the cost analysis shows below.

Even the communication intensive applications work well up to a certain amount of cores, an amount of cores unlikely to be exceeded by a medium institution. “The ability to take advantage of a large variety of different architectures (with different interconnects, processor types, memory sizes, etc.) can result in better utilization at global scale, compared to the limited choices available in any individual organization,” the report argued. Below is a sample of what such an architecture that relies on just four-core cloud-based machines would look like.

The report does go on to say that dedicated instances would be advantageous to large institutions looking for burst capacity, a concept that has been discussed here.

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!

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 “pre-exascale” award), parsed out additional information ab Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid whoops and hollers from the crowd, Thomas Sterling presented t Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out plans to push deeper into climate science and develop more gran Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale companies and their embrace of AI and deep learning – tha Read more…

By Doug Black

HPE Extreme Performance Solutions

Creating a Roadmap for HPC Innovation at ISC 2017

In an era where technological advancements are driving innovation to every sector, and powering major economic and scientific breakthroughs, high performance computing (HPC) is crucial to tackle the challenges of today and tomorrow. Read more…

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network designed to emulate and compete with the human brain. In thi Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big data and artificial intelligence software to its top-of-the-l Read more…

By Alex Woodie

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “global” launch event in Austin TX. In many ways it was a fu Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it, analysts and journalists want to report on it. Deep learni Read more…

By Doug Black

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

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid wh Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out pla Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big d Read more…

By Alex Woodie

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “g Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it Read more…

By Doug Black

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. Just how close real-wo 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 cam Read more…

By John Russell

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 a Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

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

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

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

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

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

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