HPC Funding Models Need to Encompass More Than Just the Purchase Price

By Andrew Jones

February 8, 2012

Beyond the question of how much funding should be invested in high performance computing resources, it is also important to strive for the optimum funding model: how funding is tied to the service and how it enables and drives user behavior. As it turns out, these models are wrapped up in an IT culture that is often at odds with the way HPC is used.

This article was inspired by recent topical discussions on funding and charging models for HPC in academic institutions in both the UK and the USA, including particularly a series of blog posts by Brock Palen. The issues are by no means limited to academic institutions, and in fact are equally pressing for industry users and providers of HPC resources.

For many of the biggest supercomputers in the world, there is always a separation between the big lump of cash for the machine and funding for the on-going operation, which is often due to different funding routes for each. However, the reality for most HPC systems outside the national supercomputing services, especially academic institutional and industry HPC systems, is that the funding and the service delivery are intricately linked.

To start, let’s constrain this discussion to in-house HPC resources. (I’ll come back to discuss cloud computing and other external models in a future article). The discussion takes in funding, measurement, professional experts, and finally culture changes.

Where does the funding come from?

Beyond the lump sum donation, there are essentially three models for funding HPC resources: through overheads, usage fees, or a combination of these two. I often call this latter model “baseline-plus.” Under the overheads model, the corporate or departmental HPC facility is provided to users as part of the infrastructure of the business or university and is including in the overheads of the business. There may be accounting, i.e., recording usage of the resources by each user, but not charging. Under the usage fees model, accounting leads directly to the users being billed for their actual consumption of resources.

Under the baseline-plus model, some elements of the service, like storage, may be included in the overheads whereas others, like CPU-cycle, may be charged according to consumption. Or the combination may be applied to everything, essentially subsidizing the usage fees by partially including the costs in overheads. Or a combination may be used to provide a service free of charge for “normal” consumption levels but apply charges for extreme usage, such as large storage requirements, large memory jobs, high core-count jobs, and so on.

Finding the model that keeps everyone happy

To see the benefits and issues of each model, let’s start with the viewpoint of the user – always a good place to start. The preferred model for users is almost always going to be “free,” that is, the overheads model. On the face of it, this offers the least burden on the user’s part. They just do whatever science or engineering simulations they need and pay as much attention to the costing of the HPC resources as they do to the company internet connection or the office lighting. However, this can also lead to tension when some users may suspect others of consuming an “unfair” share of the common resource.

At the other extreme, the “charge for everything” model is likely to be least favored by most users, simply because it creates a culture of having to justify every usage of the resource. That may be seen as a good thing by senior management, since the HPC resource is likely to represent a significant investment. However, it might limit the freedom of the researcher or engineer to “just try this,” that is, engage in speculative work that isn’t tied to a clear goal, but which may spark significant innovation.

In theory, the baseline-plus model allows the best of both worlds, enabling speculative work and reduced attention to monitoring consumption, whilst ensuring users who dominate consumption are seen to contribute. However, the potential complexity of the model – what is included in the core service and what is charged at usage – can lead to both confusion and debate amongst users about the “right” way to configure the complexity.

Shifting to the HPC manager’s viewpoint, the instinct is often to prefer the overheads model, as this usually works in practice as a predictable way to budget the resource. The usage fees model is often seen as least desirable because it turns every HPC manager into a sales person trying to keep their customers coming back for more, and involves an uncertain budget.

Allowing for growth and innovation

In all of this discussion so far, we have assumed a static size budget and resource. In reality, the critical aspects of these models is one in which the HPC provision evolves.

Under the overheads model, growing the resources, for example, buying a larger supercomputer or providing more support staff, usually means going back to management with a case to increase the budget. Without a direct link between the end users and the resource provided and consumed, that case is harder to make.

With usage accounting (not necessarily charging) this becomes easier. Changing the balance of the HPC provision, such as providing more large memory nodes, or more cores instead of storage space, is almost impossible because each user will see a different need.

The usage fees model solves this problem. As usage grows, the resource can be increased with the fee income. The type of resource provided can also be changed to meet the needs of users as they direct their fee-paying usage onto different elements of the service.

However, the pure usage fees model creates other problems. What about the resources that the HPC manager knows users will need but which users are resistant to paying directly for? Code performance expertise is a common example of this, as is interconnect bandwidth.

What gets measured is what gets the focus

This leads to another key aspect of the models, namely what to measure (and thus charge for in the fees or baseline-plus models).

The most common unit of consumption to measure is CPU usage. Users are accounted for how many CPU-hours they consume and are charged appropriately. The price of the CPU-hour includes the cost of running the system, but to many users, that’s not fair. For example, why should I pay a high price per CPU-hour when I don’t need that fast interconnect that is driving the price up? Or, I’ve never used the support team, so why can’t I have a discount price? That other user consumes way more memory or disk than me, surely he should pay more? And so on.

It’s temptingly easy to respond by having separate charges for different elements of the service – CPU-hours, support staff, disk usage, high memory nodes, etc. However, this rarely works well in practice for either users or HPC managers.

Measuring CPU-hours alone is horrendously bad practice though. The processors are often the cheapest part of the supercomputer to buy — behind memory and interconnect and maybe disk — and certainly small compared to operating costs, like power and staff salaries.

Idle processor cores are seen as “a bad thing,” but the memory probably cost as much to buy and nearly as much to sit there consuming electricity. Yet few HPC services monitor memory utilization. Or interconnect utilization.

Science and business output, not busy CPUs

And, speaking of utilization, there are competing interests of users and HPC managers. For users, high utilization means more contention for resource, for example, longer job queues, and is a bad thing. For HPC managers, high utilization means demand and provision are, in theory, closely matched, which is an efficient use of budget.

But efficient use of the budget should be tied to the science or engineering outputs achieved, not to the detailed consumption of the resource that enables that innovation. Which is better budget use, a low utilization system that is available on the timescales of the researchers’ or engineers’ needs (and gives them freedom to try out new ideas or support customer requests at short notice) or a high utilization system that means only planned business can be done?

The complexity of matching the model to the needs of the business, both funders and users, means that the strategy for HPC provision is rarely as simple as assumed. That’s why there is a role for professionals who have experience in finding the best model for a given situation. “HPC manager” or the equivalent is a valuable and distinct role within the organization, as is the potential support from independent experts available to provide consulting advice.

The wrong culture?

Perhaps a key part of the answer is that HPC is not really IT. It is built using computer technology but it is really a scientific instrument or engineering facility. I have written about this before. So maybe we need to move away from the funding, measuring and user cultures inherited from traditional IT.

The success of an optical telescope in astronomy might be measured by what new objects are observed, not by the amount of time an eyeball is attached to the end of it. The success of a wind tunnel might be measured by the quality and quantity of the design information gained, not necessarily the amount of time the fan is spinning. It is expected that the instrument or facility will be supported by experts whose profession is the technology of the instrument and that this will be a fundamental part of the funded resource, not an optional extra.

What can you see as cultures from the traditional IT world that are holding back HPC’s potential?

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!

University of Stuttgart Inaugurates ‘Hawk’ Supercomputer

February 20, 2020

This week, the new “Hawk” supercomputer was inaugurated in a ceremony at the High-Performance Computing Center of the University of Stuttgart (HLRS). Officials, scientists and other stakeholders celebrated the new sy Read more…

By Staff report

US to Triple Its Supercomputing Capacity for Weather and Climate with Two New Crays

February 20, 2020

The blizzard of news around the race for weather and climate supercomputing leadership continues. Just three days after the UK announced a £1.2 billion plan to build the world’s largest weather and climate supercomputer, the U.S. National Oceanic and Atmospheric Administration... Read more…

By Oliver Peckham

Indiana University Researchers Use Supercomputing to Model the State’s Largest Watershed

February 20, 2020

With water stressors on the rise, understanding and protecting water supplies is more important than ever. Now, a team of researchers from Indiana University has created a new climate change data portal to help Indianans Read more…

By Staff report

TACC – Supporting Portable, Reproducible, Computational Science with Containers

February 20, 2020

Researchers who use supercomputers for science typically don't limit themselves to one system. They move their projects to whatever resources are available, often using many different systems simultaneously, in their lab Read more…

By Aaron Dubrow

China Researchers Set Distance Record in Quantum Memory Entanglement

February 20, 2020

Efforts to develop the necessary capabilities for building a practical ‘quantum-based’ internet have been ongoing for years. One of the biggest challenges is being able to maintain and manage entanglement of remote q Read more…

By John Russell

AWS Solution Channel

Challenging the barriers to High Performance Computing in the Cloud

Cloud computing helps democratize High Performance Computing by placing powerful computational capabilities in the hands of more researchers, engineers, and organizations who may lack access to sufficient on-premises infrastructure. Read more…

IBM Accelerated Insights

Intelligent HPC – Keeping Hard Work at Bay(es)

Since the dawn of time, humans have looked for ways to make their lives easier. Over the centuries human ingenuity has given us inventions such as the wheel and simple machines – which help greatly with tasks that would otherwise be extremely laborious. Read more…

New Algorithm Allows PCs to Challenge HPC in Weather Forecasting

February 19, 2020

Accurate weather forecasting has, by and large, been situated squarely in the domain of high-performance computing – just this week, the UK announced a nearly $1.6 billion investment in the world’s largest supercompu Read more…

By Oliver Peckham

US to Triple Its Supercomputing Capacity for Weather and Climate with Two New Crays

February 20, 2020

The blizzard of news around the race for weather and climate supercomputing leadership continues. Just three days after the UK announced a £1.2 billion plan to build the world’s largest weather and climate supercomputer, the U.S. National Oceanic and Atmospheric Administration... Read more…

By Oliver Peckham

Japan’s AIST Benchmarks Intel Optane; Cites Benefit for HPC and AI

February 19, 2020

Last April Intel released its Optane Data Center Persistent Memory Module (DCPMM) – byte addressable nonvolatile memory – to increase main memory capacity a Read more…

By John Russell

UK Announces £1.2 Billion Weather and Climate Supercomputer

February 19, 2020

While the planet is heating up, so is the race for global leadership in weather and climate computing. In a bombshell announcement, the UK government revealed p Read more…

By Oliver Peckham

The Massive GPU Cloudburst Experiment Plays a Smaller, More Productive Encore

February 13, 2020

In November, researchers at the San Diego Supercomputer Center (SDSC) and the IceCube Particle Astrophysics Center (WIPAC) set out to break the internet – or Read more…

By Oliver Peckham

Eni to Retake Industry HPC Crown with Launch of HPC5

February 12, 2020

With the launch of its Dell-built HPC5 system, Italian energy company Eni regains its position atop the industrial supercomputing leaderboard. At 52-petaflops p Read more…

By Tiffany Trader

Trump Budget Proposal Again Slashes Science Spending

February 11, 2020

President Donald Trump’s FY2021 U.S. Budget, submitted to Congress this week, again slashes science spending. It’s a $4.8 trillion statement of priorities, Read more…

By John Russell

Policy: Republicans Eye Bigger Science Budgets; NSF Celebrates 70th, Names Idea Machine Winners

February 5, 2020

It’s a busy week for science policy. Yesterday, the National Science Foundation announced winners of its 2026 Idea Machine contest seeking directions for futu Read more…

By John Russell

Fujitsu A64FX Supercomputer to Be Deployed at Nagoya University This Summer

February 3, 2020

Japanese tech giant Fujitsu announced today that it will supply Nagoya University Information Technology Center with the first commercial supercomputer powered Read more…

By Tiffany Trader

Julia Programming’s Dramatic Rise in HPC and Elsewhere

January 14, 2020

Back in 2012 a paper by four computer scientists including Alan Edelman of MIT introduced Julia, A Fast Dynamic Language for Technical Computing. At the time, t Read more…

By John Russell

Cray, Fujitsu Both Bringing Fujitsu A64FX-based Supercomputers to Market in 2020

November 12, 2019

The number of top-tier HPC systems makers has shrunk due to a steady march of M&A activity, but there is increased diversity and choice of processing compon Read more…

By Tiffany Trader

SC19: IBM Changes Its HPC-AI Game Plan

November 25, 2019

It’s probably fair to say IBM is known for big bets. Summit supercomputer – a big win. Red Hat acquisition – looking like a big win. OpenPOWER and Power processors – jury’s out? At SC19, long-time IBMer Dave Turek sketched out a different kind of bet for Big Blue – a small ball strategy, if you’ll forgive the baseball analogy... Read more…

By John Russell

Intel Debuts New GPU – Ponte Vecchio – and Outlines Aspirations for oneAPI

November 17, 2019

Intel today revealed a few more details about its forthcoming Xe line of GPUs – the top SKU is named Ponte Vecchio and will be used in Aurora, the first plann Read more…

By John Russell

IBM Unveils Latest Achievements in AI Hardware

December 13, 2019

“The increased capabilities of contemporary AI models provide unprecedented recognition accuracy, but often at the expense of larger computational and energet Read more…

By Oliver Peckham

SC19: Welcome to Denver

November 17, 2019

A significant swath of the HPC community has come to Denver for SC19, which began today (Sunday) with a rich technical program. As is customary, the ribbon cutt Read more…

By Tiffany Trader

Fujitsu A64FX Supercomputer to Be Deployed at Nagoya University This Summer

February 3, 2020

Japanese tech giant Fujitsu announced today that it will supply Nagoya University Information Technology Center with the first commercial supercomputer powered Read more…

By Tiffany Trader

51,000 Cloud GPUs Converge to Power Neutrino Discovery at the South Pole

November 22, 2019

At the dead center of the South Pole, thousands of sensors spanning a cubic kilometer are buried thousands of meters beneath the ice. The sensors are part of Ic Read more…

By Oliver Peckham

Leading Solution Providers

SC 2019 Virtual Booth Video Tour

AMD
AMD
ASROCK RACK
ASROCK RACK
AWS
AWS
CEJN
CJEN
CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
IBM
IBM
MELLANOX
MELLANOX
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
SIX NINES IT
SIX NINES IT
VERNE GLOBAL
VERNE GLOBAL
WEKAIO
WEKAIO

Jensen Huang’s SC19 – Fast Cars, a Strong Arm, and Aiming for the Cloud(s)

November 20, 2019

We’ve come to expect Nvidia CEO Jensen Huang’s annual SC keynote to contain stunning graphics and lively bravado (with plenty of examples) in support of GPU Read more…

By John Russell

Top500: US Maintains Performance Lead; Arm Tops Green500

November 18, 2019

The 54th Top500, revealed today at SC19, is a familiar list: the U.S. Summit (ORNL) and Sierra (LLNL) machines, offering 148.6 and 94.6 petaflops respectively, Read more…

By Tiffany Trader

Azure Cloud First with AMD Epyc Rome Processors

November 6, 2019

At Ignite 2019 this week, Microsoft's Azure cloud team and AMD announced an expansion of their partnership that began in 2017 when Azure debuted Epyc-backed instances for storage workloads. The fourth-generation Azure D-series and E-series virtual machines previewed at the Rome launch in August are now generally available. Read more…

By Tiffany Trader

Intel’s New Hyderabad Design Center Targets Exascale Era Technologies

December 3, 2019

Intel's Raja Koduri was in India this week to help launch a new 300,000 square foot design and engineering center in Hyderabad, which will focus on advanced com Read more…

By Tiffany Trader

In Memoriam: Steve Tuecke, Globus Co-founder

November 4, 2019

HPCwire is deeply saddened to report that Steve Tuecke, longtime scientist at Argonne National Lab and University of Chicago, has passed away at age 52. Tuecke Read more…

By Tiffany Trader

IBM Debuts IC922 Power Server for AI Inferencing and Data Management

January 28, 2020

IBM today launched a Power9-based inference server – the IC922 – that features up to six Nvidia T4 GPUs, PCIe Gen 4 and OpenCAPI connectivity, and can accom Read more…

By John Russell

Cray Debuts ClusterStor E1000 Finishing Remake of Portfolio for ‘Exascale Era’

October 30, 2019

Cray, now owned by HPE, today introduced the ClusterStor E1000 storage platform, which leverages Cray software and mixes hard disk drives (HDD) and flash memory Read more…

By John Russell

D-Wave’s Path to 5000 Qubits; Google’s Quantum Supremacy Claim

September 24, 2019

On the heels of IBM’s quantum news last week come two more quantum items. D-Wave Systems today announced the name of its forthcoming 5000-qubit system, Advantage (yes the name choice isn’t serendipity), at its user conference being held this week in Newport, RI. Read more…

By John Russell

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