Name that supercomputer 2 (Quiz)

It's a long time since I did an HPC quiz, so here is one to keep some fun in these odd times. Can you name these supercomputers?

I'm looking for actual machine names (e.g. 'Fugaku') and the host site (e.g. RIKEN CCS). Bonus points for the machine details (e.g. Fujitsu A64FX).

Submit your guesses or knowledgeable answers either through the comments field below, or to me on twitter (@hpcnotes).

Answers will be revealed once there have been enough guesses to amuse me. Have fun!

1. Maybe it's Italian style, but this oily system has a purely descriptive name, a bit like the name of a robot with a short circuit.


2. In spite of the name, this one is a step away from the very top.


3. The seven daughters of Atlas.


4. Arising from a beautiful reef, this top supercomputer is named after one of my co-presenters at my SC19 tutorial (or so we think).


5. This border system's owner often tells how it was renamed in planning due to a bigger newer super that took it's original name.


6. It has no name, at least not publicly, and the operator has not been open with full details, but with 10,000 GPUs it can do a lot of AI.


7. On the road to exascale, but not there yet, this system will be housed next year in a chilly northern European location, and shares some similar architecture to two of the first exascale systems.


8. A chicken with green-ish / brown-ish eyes. Or is it a type of nut?


9. In a rare move, this number 9 is named after a living scientist, actually one of its users.


10. Sing a song for this one, because it is named to be hit hard.

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What makes a Supercomputer Centre a Supercomputer Centre?

When is a Supercomputer Center not a Supercomputer Center?

The world of HPC has always been a place of rapid change in technology with slower change in business models and skill profiles, but what actually makes a supercomputer center a supercomputer center?

Tin (or Silcon maybe)

Is it having a big HPC system? How big counts? Does it matter what type of "big" system you have?

Does it matter if there is not one big supercomputer but instead a handful of medium sized ones of different types?

Does it count if the supercomputers are across the street, or in a self-owned/operated datacentre the other side of town? What if the supercomputers are located hundreds of miles away from the HPC (eg to get cheap power & cooling)?

Who and How

Or is it having a team of HPC experts able to help users? How many experts? What level of expertise counts? How many have to be RSE (Research Software Engineer) types?

Is it having the vision and processes to recognise they are primarily a service provider to their users ("customers") rather than thinking of themselves mainly as a buyer of HPC kit?

What if you mainly have AI workloads rather than "traditional" HPC? What if you only run many small simulation jobs and no simulations that span thousands of cores? What if users only ever submit jobs via web portals and never log in to the supercomputers directly?

Is it essential to have a .edu, .gov, .ac.uk etc. address? Or can .com be a supercomputer center too?

This but not that?

If you have no supercomputers of your own, but have 50 top class HPC experts who work with users on other supercomputers and also research future technologies - is that a supercomputer center?

If you have a very large HPC system but only the bare miuminm of HPC staff and no technology R&D efforts - is that a supercopmputer center?

Which of the last two adds more value to your users?

Declare or Earn?

Is it merely a matter of declaration - "we are a supercomputer center"? Or it is a matter of other supercomputer centers accepting you as a peer? But then who counts as other supercomputer centers to accept you? What if some do and some don't?

Is there a difference between a supercomputer center and a supercomputing center?

What do you think? And does your answer depend on whether you are a user, or work at a "traditional" supercomputer center, or a new type of supercomputing center, or a HPC vendor, or from outside the HPC field?

SC17 Tutorials - HPC cost models, investment cases and acquisitions

Following our successful HPC tutorials at SC16 and OGHPC17, I'm delighted to report that we've had three tutorials accepted for SC17 in Denver this November, all continuing our mission to provide HPC training opportunities for HPC people other than just programmers.

At SC17, we will be delivering these three tutorials:

• [Sun 12th, am] "Essential HPC Finance: Total Cost of Ownership (TCO), Internal Funding, and Cost-Recovery Models"
• [Sun 12th, pm] "Extracting Value from HPC: Business Cases, Planning, and Investment"
• [Mon 13th, am] "HPC Acquisition and Commissioning"

In a last minute bit of co-ordination, Sharan Kalwani will be following these with his related tutorial "Data Center Design" on Mon 13th pm.

Are these tutorials any good?

The HPC procurement tutorial was successfully presented at SC13 (>100 attendees) and SC16 (~60 attendees). Feedback from the SC16 attendees was very positive: scored 4.6/5 overall and scored 2.9/3 for “recommend to a colleague”.

The HPC finance tutorial was successfully presented at SC17 (~60 attendees) and at the Rice Oil & Gas HPC conference 2017 (~30 attendees). Feedback from the SC16 attendees was very positive: scored 4.3/5 overall and scored 2.7/3 for “recommend to a colleague”.

The HPC business case tutorial is new for SC17.

What is the goal of the tutorials?

The tutorials provide an impartial, practical, non-sales focused guide to the business aspects of HPC facilities and services (including cloud), such as total cost of ownership, funding models, showing value and securing investing in HPC, and the process of purchasing and deploying a HPC system. All tutorials include exploration of the main issues, pros and cons of differing approaches, practical tips, hard-earned experience and potential pitfalls.

What is in the tutorials?

Essential HPC Finance Practice: Total Cost of Ownership (TCO), Internal Funding, and Cost-Recovery Models

• Calculating and using TCO models
• Pros and cons of different internal cost recovery and funding models
• Updated from the SC16 base, with increased consideration of cloud vs in-house HPC

Extracting Value from HPC: Business Cases, Planning, and Investment

• Applicable to either a first investment or an upgrade of existing capability
• Most relevant to organizations with a clear purpose (e.g., industry) or those with a clear service mission (e.g., academic HPC facilities)
• Identifying the value, building a business case, engaging stakeholders, securing funding, requirements capture, market survey, strategic choices, and more

HPC Acquisition and Commissioning

• Procurement process including RFP
• Specify what you want, yet enable the suppliers to provide innovative solutions beyond the specification both in technology and in the price
• Bid evaluation, benchmarks, clarification processes
• Demonstrate to stakeholders that the solution selected is best value for money
• Contracting, project management, commissioning, acceptance testing

Who are the tutors?

Me (Andrew Jones, @hpcnotes), Owen Thomas (Red Oak Consulting), and Terry Hewitt. We have been involved in numerous major HPC procurements and other strategic HPC projects since 1990, as service managers, bidders to funding agencies, as customers and as impartial advisors. We are all from the UK but have worked around the world and the tutorials will be applicable to HPC projects and procurements anywhere. The tutorials are based on experiences across a diverse set of real world cases in various countries, in private and public sectors.

What if you need even more depth?

These SC17 tutorials will deliver a lot of content in each half day. However, if you need more depth, or a fuller range of topics, or are looking for a CV step towards becoming a future HPC manager, then our joint TACC-NAG summer training institute is the right thing for you: "Where will future HPC leaders come from?"



Hope to see you at one (or more!) of our tutorials at SC17 this November in Denver.
@hpcnotes

Is cloud inevitable for HPC?

In 2009, I wrote this article for HPC Wire: "2009-2019: A Look Back on a Decade of Supercomputing", pretending to look back on supercomputing between 2009 and 2019 from the perspective of beyond 2020.

The article opens with the idea that owning your own supercomputer was a thing of the past:

"As we turn the decade into the 2020s, we take a nostalgic look back at the last ten years of supercomputing. It’s amazing to think how much has changed in that time. Many of our older readers will recall how things were before the official Planetary Supercomputing Facilities at Shanghai, Oak Ridge and Saclay were established. Strange as it may seem now, each country — in fact, each university or company — had its own supercomputer!"

I got this bit wrong:

"And then the critical step — businesses and researchers finally understood that their competitive asset was the capabilities of their modelling software and user expertise — not the hardware itself. Successful businesses rushed to establish a lead over their competitors by investing in their modelling capability — especially robustness (getting trustable predictions/analysis), scalability (being able to process much larger datasets than before) and performance (driving down time to solutions)."

Hardware still matters - in some cases - as a means of gaining a competitive advantage in performance or cost [We help advise if that is true for our HPC consulting customers, and how to ensure the operational and strategic advantage is measured and optimized].

And, of course, my predicted rush to invest in software and people hasn't quite happened yet.

Towards the end, I predicted three major computing providers, from which most people got their HPC needs:

"We have now left the housing and daily care of the hardware to the specialists. The volume of public and private demand has set the scene for strong HPC provision into the future. We have the three official global providers to ensure consumer choice, with its competitive benefits, but few enough providers to underpin their business cases for the most capable possible HPC infrastructure."

Whilst my predictions were a little off in timing, some could be argued to have come true e.g., the rise to the top of Chinese supercomputing, the increasing likelihood of using someone else's supercomputer rather than buying your own (even if we still call it cloud), etc.

With the ongoing debate around cloud vs in-house HPC (where I am desperately trying to inject some impartial debate to balance the relentless and brash cloud marketing), re-visiting this article made an interesting trip down memory lane for me. I hope you might enjoy it too.

As I recently posted on LinkedIn:

"Cloud will never be the right solution for everyone/every use case. Cloud is rightly the default now for corporate IT, hosted applications, etc. But, this cloud-for-everything is unfortunately, wrongly, extrapolated to specialist computing (e.g.,  high performance computing, HPC), where cloud won't be the default for a long time.

For many HPC users, cloud is becoming a viable path to HPC, and very soon perhaps even the default option for many use cases. But, cloud is not yet, and probably never will be, the right solution for everyone. There will always be those who can legitimately justify a specialized capability (e.g., a dedicated HPC facility) rather than a commodity solution (i.e., cloud, even "HPC cloud"). The reasons for this might include better performance, specific operational constraints, lower TCO, etc. that only specialized facilities can deliver. 

The trick is to get an unbiased view for your specific situation, and you should be aware that most of the commentators on cloud are trying to sell cloud solutions or related services, so are not giving you impartial advice!"

[We provide that impartial advice on cloud, measuring performance, TCO, and related topics to our HPC consulting customers]


@hpcnotes

Essential Analogies for the HPC Advocate

This is an update of a two-part article I wrote for HPC Wire in 2013: Part 1 and Part 2.

An important ability for anyone involved in High Performance Computing (HPC or supercomputing or big data processing, etc.) is to be able to explain just what HPC is to others.

"Others” include politicians, Joe Public, graduates possibly interested in HPC, industry managers trying to see how HPC fits into their IT or R&D programs, or family asking for the umpteenth time “what exactly do you do?”

One of the easiest ways to explain HPC is to use analogies that relate the concepts to things that the listener is more familiar with. So here is a run-through of some useful analogies for explaining HPC or one of its concepts:

• The simple yet powerful: A spade
• The moral high ground: A science/engineering instrument
• Duh! Clue’s in the name: Big computer
• The testosterone favorite: Formula 1
• The TARDIS factor: Time Machine
• Not special, just normal: Library
• Imagine a silly task: Aircraft vs. Car
• Monuments: Ecosystems
• The HPC Hotel

The simple yet powerful: A spade

Need to dig a hole? Use the right tool for the job – a spade. Need to dig a bigger hole, or a hole through tougher material like concrete? Use a more powerful tool – a mechanical digger.

Now instead of digging a hole, consider modeling and simulation. If the model/simulation is too big or too complex – use the more powerful tool: i.e. HPC. It’s nice and simple – HPC is a more powerful tool that can tackle more complex or bigger models/simulations than ordinary computers.

There are some great derived analogies too. You should be able to give a spade to almost anyone and they should be able to dig a hole without too much further instruction. But, hand a novice the keys to a mechanical digger, and it is unlikely they will be able to effectively operate the machine without either training or a lot of on the job learning. Likewise, HPC requires training to be able to use the more powerful tool effectively. Buying mechanical diggers is also requires expertise that buying a spade doesn’t. And so on.

It neatly focuses on the purpose and benefit of HPC rather than the technology itself. If you’ve heard any of my talks recently you will know this is an HPC analogy that I use myself frequently.

The moral high ground: A science/engineering instrument

I’ve occasionally accused the HPC community of being riddled with hypocrites – we make a show of “the science is what matters” and then proceed to focus the rest of the discussion on the hardware (and, if feeling pious or guilty, we mention “but software really matters”).

However, there is a critical truth to this – the scientific (or engineering) capability is what matters when considering HPC. I regularly use this perspective, often very firmly, myself: a supercomputer is NOT a computer – it is a major scientific instrument that just happens to be built using computer technology. Just because it is built from most of the same components as commodity servers does not mean that modes of usage, operating skills, user expectations, etc. should be the same. This helps to put HPC into the right context in the listeners mind – compare it to a major telescope, a wind tunnel, or even LHC@CERN.

The derived analogies are effective too – expertise in the technology itself is required, not just the science using the instrument. Sure, the skills overlap but they are distinct and equally important.

This analogy focuses on the purpose and benefit of HPC, but also includes a reference to it being based on a big computer.

China supercomputer to be world's fastest (again) - Tianhe-2

It seems that China's Tianhe-2 supercomputer will confirmed as the world's fastest supercomputer at next Top500 list to be revealed at the ISC'13 conference next week.

I was going to write about the Chinese Tianhe-2 supercomputer and how it matters to the USA and Europe - then I found these old blog posts of mine:

• Why does the China supercomputer matter to western governments?
• Comparing HPC across China, USA and Europe