Essential Analogies for the HPC Advocate or the Trouble with Trying to Explain HPC

By Andrew Jones, NAG

October 25, 2013

Following Part 1, here are some more analogies for HPC …

Duh! Clue’s in the name: Big computer

I see this in so many “Intro to HPC” type courses – defining HPC as a computer 1000x more powerful than a desktop computer. Or worse, a computer that costs several million dollars, requires a megawatt of power, and fills a room. For bonus points the weight of the machine or how much cooling water it churns can be used. This is not really an analogy – simply a statement of the fact that HPC usually involves extreme computer hardware (albeit a narrow definition of HPC). But the reader/listener is left clueless as to the reason why anyone would fill a room full of computers and stump up for a $1m/year electricity bill. In fact, I would go as far as to say that this type of description of HPC (“it’s a big computer”) should be banned from the repertoire of any HPC person wishing to retain the community respect. Unless used in conjunction with a solid and inspiring description of the purpose and benefits of HPC.

Not special, just normal: Library

One of the great HPC analogies I have heard is one that describes where HPC should sit in the make-up of R&D organizations, especially universities. This one says that HPC should occupy the same position in any research organization (university) as a library – i.e., a core part of the essential infrastructure and a research tool that can be turned to many projects. A university for the last few centuries without a library? As silly as a modern R&D organization without access to HPC facilities. There are tiers of libraries too. Supporting the university library are national libraries with greater breadth of material. Equally important are the local research group libraries with much more specialized texts that may not be found in the larger more general purpose libraries. And the local libraries have a lower barrier to access. I’m sure the reader can work out the analogies to the traditional pyramid of HPC tiers.

Imagine a silly task: Aircraft vs. Car

One of the favorite hunting grounds for HPC analogies is explaining the nature and usefulness of the capability vs. capacity distinction. First, let me get a common mistake out of the way – I often see people trying to describe capability as the role of a supercomputer and capacity as the role of a cluster. There is no reason why a well architected commodity cluster cannot do capability computing and certainly poorly implemented supercomputers can be useless for capability work.

Usually we start by asking the reader/listener to imagine a task that needs doing/solving. Let’s say we have to move a thousand shoe boxes from one city to another. We can load up a car (or a group of cars if we have a team of willing friends) with boxes and drive them to the new location, and repeat as needed. As the problem gets bigger (more boxes or more distant cities) the cars take longer to complete the task, or more cars are needed. However the cars can still do the job. Now, imagine the destination city is across an ocean. It doesn’t matter how many cars are put on to the job or how much time is allocated, the cars cannot move the boxes across the ocean. But a cargo airplane can. This is capability – a job that cannot be achieved without that platform.

In HPC, capability computing jobs are those that cannot be completed by waiting longer or using a collection of smaller resources. This is often equated to jobs that require the use of the whole supercomputer (or half of it or some other large fraction) – but this is not a general answer to capability. Capability might only require a small fraction of the machine, but needs some special features it has. And not all jobs that use the full size of a system are capability jobs. There is also a great derived analogy – the aircraft can be used for both jobs (assuming availability of runways etc.). And so a capability computing system can be used for capacity work too – but the reverse is not true. Although of course, a system designed for capability might not be as cost-effective when used for capacity workloads.

Monuments: Ecosystems

Another aspect of HPC that cries out for effective analogies is the need to explain why supercomputing needs proper resourcing – i.e., people and software, not just a room filling lump of silicon and copper. One impactful analogy I have heard is to describe supercomputers purchased or deployed without adequate matching investment in software and people as “monuments.” Great to look at, but not very functional. One analogy is to consider a long haul passenger airplane. To deliver its mission, the airplane must be supplemented by an entire ecosystem of pilots, cabin crew (or flight attendants if on a US-based airline), runways, passenger terminals, air traffic control, processes/procedures, etc.

Likewise, HPC needs an ecosystem of people, software, datacenters, I/O subsystems, etc., to deliver its mission. And just like air travel, much of the complexity is in the ecosystem beyond the hardware product. And, here is the important bit, the differentiation and economic impact comes from getting the ecosystem right. Airlines have the same aircraft as their competitors just as companies normally have access to the same HPC technology as their competitors. But, how the staff interacts with the customers, quality of the back-end support, the processes/policies – these are what distinguish one airline from another. Likewise, the software, the support staff, the policies, etc., are what enables each company to gain a competitive advantage over their peers who may be using the same HPC technology.

The HPC Hotel

This analogy is great for explaining many different HPC concepts. Imagine your job is to refurbish a hotel. Clearly this task is easier if you have additional workers – more people means the job can be done quicker. And you can accept contracts to refurbish bigger hotels. But you need to coordinate all these extra workers of course. I’m sure you can see the use of this analogy for explaining parallelism and scalability (decomposition, coordination, scheduling conflicts, resource contention, etc.). You can also use it to introduce special vs. general purpose processors (everyone can do any job vs. combination of plumbers, electricians, plasterers, etc.). It can be used to explain that a variety of skills are needed to make the refurbishment (HPC simulation) effective.

The HPC hotel analogy can be used to show that the job of running a hotel is not the same as the job of designing a hotel is not the same as the job of building/refurbishing a hotel is not the same as staying in a hotel. In the same way, it is silly that one person expects to be expert in using HPC, and writing the applications, and running the cluster, and designing the cluster, and so on. The analogy can also be used to describe areas of differentiation – hotels (HPC) can differentiate from each other on both the rooms (hardware) and the services/staff/policies (support & software).

So, there you go – a light touch run-through of some common HPC analogies. What analogies do you use to describe HPC? Which ones have you found through feedback to be effective? Which ones are best left with those packing boxes that have been in the corner of the datacenter since before anyone can remember?

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!

SC17 Student Cluster Competition Configurations: Fewer Nodes, Way More Accelerators

November 16, 2017

The final configurations for each of the SC17 “Donnybrook in Denver” Student Cluster Competition have been released. Fortunately, each team received their equipment shipments on time and undamaged, so the teams are r Read more…

By Dan Olds

Student Clusterers Demolish HPCG Record! Nanyang Sweeps Benchmarks

November 16, 2017

Nanyang pulled off the always difficult double-play at this year’s SC Student Cluster Competition. The plucky team from Singapore posted a world record LINPACK, thus taking the Highest LINPACK Award, but also managed t Read more…

By Dan Olds

Student Cluster LINPACK Record Shattered! More LINs Packed Than Ever before!

November 16, 2017

Nanyang Technological University, the pride of Singapore, utterly destroyed the Student Cluster Competition LINPACK record by posting a score of 51.77 TFlop/s at SC17 in Denver. The previous record, established by German Read more…

By Dan Olds

HPE Extreme Performance Solutions

Harness Scalable Petabyte Storage with HPE Apollo 4510 and HPE StoreEver

As a growing number of connected devices challenges IT departments to rapidly collect, manage, and store troves of data, organizations must adopt a new generation of IT to help them operate quickly and intelligently. Read more…

Hyperion Market Update: ‘Decent’ Growth Led by HPE; AI Transparency a Risk Issue

November 15, 2017

The HPC market update from Hyperion Research (formerly IDC) at the annual SC conference is a business and social “must,” and this year’s presentation at SC17 played to a SRO crowd at a downtown Denver hotel. This w Read more…

By Doug Black

SC17 Student Cluster Competition Configurations: Fewer Nodes, Way More Accelerators

November 16, 2017

The final configurations for each of the SC17 “Donnybrook in Denver” Student Cluster Competition have been released. Fortunately, each team received their e Read more…

By Dan Olds

Student Clusterers Demolish HPCG Record! Nanyang Sweeps Benchmarks

November 16, 2017

Nanyang pulled off the always difficult double-play at this year’s SC Student Cluster Competition. The plucky team from Singapore posted a world record LINPAC Read more…

By Dan Olds

Student Cluster LINPACK Record Shattered! More LINs Packed Than Ever before!

November 16, 2017

Nanyang Technological University, the pride of Singapore, utterly destroyed the Student Cluster Competition LINPACK record by posting a score of 51.77 TFlop/s a Read more…

By Dan Olds

Hyperion Market Update: ‘Decent’ Growth Led by HPE; AI Transparency a Risk Issue

November 15, 2017

The HPC market update from Hyperion Research (formerly IDC) at the annual SC conference is a business and social “must,” and this year’s presentation at S Read more…

By Doug Black

2017 Student Cluster Competition Benchmarks, Workloads, and Pre-Planned Disasters

November 15, 2017

The students competing in the 2017 Student Cluster Competition in Denver are facing a grueling 48 hour marathon of HPC benchmarks and real scientific applicatio Read more…

By Dan Olds

Nvidia Focuses Its Cloud Containers on HPC Applications

November 14, 2017

Having migrated its top-of-the-line datacenter GPU to the largest cloud vendors, Nvidia is touting its Volta architecture for a range of scientific computing ta Read more…

By George Leopold

HPE Launches ARM-based Apollo System for HPC, AI

November 14, 2017

HPE doubled down on its memory-driven computing vision while expanding its processor portfolio with the announcement yesterday of the company’s first ARM-base Read more…

By Doug Black

OpenACC Shines in Global Climate/Weather Codes

November 14, 2017

OpenACC, the directive-based parallel programming model used mostly for porting codes to GPUs for use on heterogeneous systems, came to SC17 touting impressive 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

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

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

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

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

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

Leading Solution Providers

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

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

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

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

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

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