Cloud-Readiness and Looking Beyond Application Scaling

By Chris Downing

April 11, 2018

Editor’s note: In a follow-on to his well-received “How the Cloud Is Falling Short for HPC” article, Red Oak’s Chris Downing turns his attention to getting applications cloud-ready.

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title application readiness, lets us examine how the run-time of the job is affected by the environment we are running in. The second, workflow readiness, forces us to think more broadly about how the jobs fit in to our day-to-day activities, and how effectively we are getting things done.

Application readiness

Application performance is fairly well understood in the HPC community. We go to great lengths to benchmark codes and determine the optimum job parameters based on the scaling characteristics observed. We avoid overheads and penalties such as those arising from virtualisation, and we insist on the most performant hardware our budgets can stretch to.

There are a few simple steps application developers can take to make their software more amenable to running in the cloud. The most crucial is a sane approach to checkpointing – the majority of well-developed apps do this by default, but it is a feature which could easily be overlooked in a home-spun tool which gradually grows in popularity and scope. Efficient checkpoint mechanisms are crucial to on-premise HPC, but even more so in the cloud where pre-emptible instances will be the de facto job environment.

Another aspect to consider is the potential for changes to temporary storage. The overwhelming majority of HPC applications write their outputs to simple text files, with the more keenly developed software making use of the likes of HDF5 or NetCDF to manage their data. Co-existence of HPC workloads with enterprise IT tools allows us to open up a few new avenues of research when figuring out how to deliver better performance – the simplest of which would be the use of databases. Running multiple “production” databases on a HPC cluster is not common due to the perceived fragility of the infrastructure, but in the cloud, it would be trivial. Depending on the application, a database could offer performance benefits in the analysis phase, as well as opening the door to providing results of large simulations to the wider community as a service.

Finally, users should remember that the many (perhaps most) applications do not scale particularly well anyway or are often only ran over a small number of nodes – in that case, using fewer cores for a longer duration is more efficient provided a longer wait is tolerable. While the poor price/performance of public clouds for multi-node scientific computing can easily be interpreted as a reason not to use these resources, it should instead be thought of as a gentle shove away from wasteful practices, and towards patience. The focus for applications running in the cloud should therefore be on extracting value from the outputs, which is a workflow problem rather than an application one.

Workflow readiness

The workflow which surrounds and links together applications is another area where optimisation will need to occur and is arguably the area where we ought to focus our attention when considering the cloud. At the design-of-experiments level, researchers who are being steered towards cloud usage should consider whether their research project is making best use of the available resource scaling. The sort of large scale, embarrassingly-parallel parameter space exploration which might have struggled to get approval to run on a crowded HPC system is a perfect model for the cloud – the researcher is effectively limited only by their budget and their ability to deal with the job outputs.

Storage utilisation is another area where workflows can be optimised for the cloud. When jobs directly interact with a permanent file system as is the case for traditional HPC, users do not need to worry much about what state their data is in until they actually want to perform their analysis. The same model could work in the cloud, but the ephemeral nature of cloud resources means that each job would need to first get data out of a separate persistent store (likely an object storage service), then put the file back at the end. Rather than seeing this as a nuisance, users should consider whether “serverless” computing offers a route to turn these put/get steps into part of an automated data analysis pipeline, for example by running data cleansing or analysis scripts programmatically. Rather than the user waiting for jobs to finish them performing a series of manual steps to extract something valuable, portions of the analysis can be turned into a scripted procedure which occurs automatically once the necessary data are available.

Containerised workflows are increasingly popular in HPC with Singularity leading the charge towards making reproducible user-defined environments the norm. Running in a container makes HPC jobs portable, both between different on-premise systems and between physical and cloud resources. Combining containerised applications with general-purpose serverless analysis scripts, it is easy to imagine how a community of researchers using the same code might be able to put together a set of computational and analysis pipelines, leading to more standardised outputs and easing the process of turning discoveries into publication-ready results. More importantly – rather than just sharing their outputs, researchers would have an easier way to share their whole pipeline. This might raise some questions regarding competition but is surely the best route to improving the reproducibility of science.

Making it happen

Most of the modifications described here are well outside the comfort zone of a novice research software engineer. Likewise, refactoring crusty Fortran code to accommodate modern system architectures is likely to be just as unappealing to the new wave of computer scientists as working on mainframe Cobol would be – perhaps even less so, given the likely salary differential. There is therefore room in the middle for a new skillset, one which brings together an interest in scientific computing with an acceptance that traditional HPC cluster designs might not be the future – something like Scientific DevOps.

As with “normal” research software engineering in years past (and, some would argue, still to this day), the problem will inevitably be money. Paying people to churn out publications as part of the process of scientific discovery is accepted practice but exploring new methods of how to get stuff done has proved to be a much tougher sell. Those responsible for dishing out grant money tend to be somewhat cautious, and traditionalists.

We should therefore be looking to the cloud providers themselves to drive this innovation – as the adage goes, you need to spend money to make money, and right now a large pool of scientific computing users are lagging far behind their enterprise counterparts in cloud adoption. Tapping into this market will naturally require some investment on the part of Amazon, Google and Microsoft – but they should recognise that people and skills are more important than new features when splashing around their marketing budget.

About the Author

Chris Downing joined Red Oak Consulting @redoakHPC in 2014 on completion of his PhD thesis in computational chemistry at University College London. Having performed academic research using the last two UK national supercomputing services (HECToR and ARCHER) as well as a number of smaller HPC resources, Chris is familiar with the complexities of matching both hardware and software to user requirements. His detailed knowledge of materials chemistry and solid-state physics means that he is well-placed to offer insight into emerging technologies. Chris, Senior Consultant, has a highly technical skill set working mainly in the innovation and research team providing a broad range of technical consultancy services. To find out more www.redoakconsulting.co.uk.

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!

First All-Petaflops Top500 List Debuts; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafloppers only. The entry point for the new list is 1.022 petaf Read more…

By Tiffany Trader

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its intention to make Arm a full citizen in the processing arch Read more…

By Tiffany Trader

Jack Wells Joins OpenACC; Arm Support Coming

June 17, 2019

Perhaps the most significant ISC19 news for OpenACC wasn’t in its official press release yesterday which touted growing user traction and the notable addition of HPC leader Jack Wells, director of science, Oak Ridge Le Read more…

By John Russell

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

For decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

5 Benefits Artificial Intelligence Brings to HPC

According to findings from Hyperion Research, simulation is primarily responsible for expanding the global HPC market from $2 billion in 1990 to a projected $38 billion in 2022. Read more…

At ISC: DDN Launches EXA5 for AI, Big Data, HPC Workloads

June 17, 2019

DDN, for two decades competing at the headwaters of high performance storage, this morning announced an enterprise-oriented end-to-end high performance storage and data management for AI, big data and HPC acceleration. I Read more…

By Doug Black

First All-Petaflops Top500 List Debuts; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Jack Wells Joins OpenACC; Arm Support Coming

June 17, 2019

Perhaps the most significant ISC19 news for OpenACC wasn’t in its official press release yesterday which touted growing user traction and the notable addition Read more…

By John Russell

At ISC: DDN Launches EXA5 for AI, Big Data, HPC Workloads

June 17, 2019

DDN, for two decades competing at the headwaters of high performance storage, this morning announced an enterprise-oriented end-to-end high performance storage Read more…

By Doug Black

Final Countdown to ISC19: What to See

June 13, 2019

If you're attending the International Supercomputing Conference, taking place in Frankfurt next week (June 16-20), you're either packing, in transit, or are alr Read more…

By Tiffany Trader

The US Global Weather Forecast System Just Got a Major Upgrade

June 13, 2019

The United States’ Global Forecast System (GFS) has received a major upgrade to its modeling capabilities. The new dynamical core that has been added to the G Read more…

By Oliver Peckham

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

The Spaceborne Computer Returns to Earth, and HPE Eyes an AI-Protected Spaceborne 2

June 10, 2019

After 615 days on the International Space Station (ISS), HPE’s Spaceborne Computer has returned to Earth. The computer touched down onboard the same SpaceX Dr Read more…

By Oliver Peckham

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Announcing four new HPC capabilities in Google Cloud Platform

April 15, 2019

When you’re running compute-bound or memory-bound applications for high performance computing or large, data-dependent machine learning training workloads on Read more…

By Wyatt Gorman, HPC Specialist, Google Cloud; Brad Calder, VP of Engineering, Google Cloud; Bart Sano, VP of Platforms, Google Cloud

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

In Wake of Nvidia-Mellanox: Xilinx to Acquire Solarflare

April 25, 2019

With echoes of Nvidia’s recent acquisition of Mellanox, FPGA maker Xilinx has announced a definitive agreement to acquire Solarflare Communications, provider Read more…

By Doug Black

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This