Reed's Ruminations: A Blog by Dan Reed

Holiday Explanations: So, What Do You Do?

Any young researcher, whether in academia or a government laboratory, who spends time with childhood friends and extended family over the holidays, will undoubtedly be asked, “What do you do?” Red Alert! Shields Up! Brace for Impact!

This is not an academic question, posed by a colleague and potential collaborator, where an extended, deeply technical answer is both expected and desired.   No, this is a potential social trap, one that has ensnared more than one naïve academic in its steely jaws. With the exception of your parents, and maybe your therapist, the putative interlocutor does not really want to know; they are simply feigning interest to stimulate social discourse.

The Social Conundrum

Yes, you are excited and passionate about what you are doing, and you should be.  After all, the scientific process may be the most successful intellectual endeavor in human history, demonstrably capable of illuminating the three big and enduring questions:  matter and the universe, life and its processes, and the human condition.  It has been and always will be about the age-old desire to explore and to understand, and in doing so, make life better for those around us.

Alas, you are fighting ingrained stereotypes and misinformation.  Everyone knows what a plumber does, but few people understand how science is really conducted.  Their models are usually the (sadly) boring experiences of primary and secondary school science, which often made after-school detention look attractive by comparison.  Class, what are the steps of the scientific method?  What is DNA? What causes it to snow?  These are important and interesting questions, but they are desiccated recitations, lacking all the joy of curiosity and the excitement of discovery that motivate every intellectual explorer, whether aged 8 or 50, who doggedly and persistently keeps asking “Why?” and “How?” (Editorial comment: We must stop ingraining scientific disdain and dislike in generations of students. See The Epistemology of Science and For Science and Society, the Future Begins with Better Dreams.)

Alternatively, the scientific understanding of your family and friends may be shaped by the Hollywood stereotypes perpetuated on television and the movies.  You know the ones I mean.  There’s the hacker tapping on the keyboard for a few seconds, then exclaiming, “I’m in.” (Brute force attacks on public key cryptography systems are exceedingly time consuming and difficult. Oh, and there are fascinating mathematical topics at the intersection of quantum computing and homomorphic encryption. Stand down; your spidey-sense tingling – the social trap is near.) Then there’s Spock or Scotty conjuring a scientific miracle to save the Enterprise before the next commercial break. Or, and this one beggars the imagination, the crime laboratory that does precise facial recognition using nine pixels from a VHS video tape recovered from a convenience store’s ancient, analog surveillance system.

You know the reality is far different. Science involves long hours of toil, filled with dead ends – experiments that do not work, which can be even more frustrating than failing, and elegant theories that are crushed by contradictory data – confounded with balky instruments, uncooperative specimens, and buggy code – and often fueled by hot coffee and cold pizza. Conversely, the reward is something transcendent, unraveling a mystery older than history and being the first person to ever understand the answer to a vexing scientific question.  At its best, science is child-like wonder as a lifelong passion.  Why wouldn’t you be excited? After all, you are doing Cool Stuff^TM that solves nature’s mysteries and might change the world! (See Science: It’s About the Wide-Eyed Wonder.)

Yes, you are fighting stereotypes as you sit at the holiday dinner table. Among them is the hoary cliché, one every scholar resignedly learns at some point in their career:

You learn more and more about less and less, until you know everything about nothing.  Then, we give you a Ph.D.

The positive interpretation speaks to the specialization and depth required to truly understand a topic and extend, even marginally, the boundaries of human knowledge. The explosive growth of knowledge means it is now impossible – and even laughable – for even the most gifted, determined, and learned to make any credible claim to know all that is worth knowing, even in a narrow academic field. These are the days of Renaissance teams, not Renaissance individuals. (See Renaissance Teams: Reifying the School at Athens.) You are an expert, but in a necessarily limited area. You learned a lot, but experience, wisdom, and humility also taught you that compared to what there is to know, you know almost nothing.

The negative interpretation – that you are filled with knowledge but lacking in practical “smarts” – is the one you seek to avoid at all costs. There’s an even more colorful, though less polite version of the everything about nothing aphorism that has one learning B.S., then M.S., and until finally, it’s Piled Higher and Deeper.

If you are still a graduate student, it can be even worse, when you may be asked “the question.”  It’s the one that bedevils every Ph.D. student, “When are you going to finish?” (See Doctoral Comedy: Which Way Is The Door?) Few understand that completing a Ph.D. is a qualitatively different process, involving open-ended, original research, not just completing additional classes. Consequently, the question stirs angst and ennui in even the most accomplished Ph.D. student.  Will I finish and if so, when?  Or, will I die homeless and alone, an ABD? (That’s All But Dissertation (ABD) for those of you playing the game at home.)

Explaining What You Do

Despite your well-justified passions, resist all temptation to open your laptop and walk your uncle through the brilliant presentation on early galaxy formation you just gave at the departmental seminar.  Do not fetch the 3-D prototype you just built (still in your backpack) and explain additive manufacturing and laser sintering to your cousin. Avoid explaining the innovative laboratory protocol you are using to screen for SARS-CoV-2 vaccine resistance to your mother – she’s already proud of you. 

Do not ask your nieces and nephews if they have an opinion on the P=NP problem, and if you foolishly do, thank your precocious eight-year-old niece who delightedly tells you that they are equal when N = 1. (Note to self: while the other adults are telling stories about their lives, make sure to talk to her about the joy of science and mathematics. Encouragement really matters!)

Finally, whatever you do, do not expound on Tomasulo’s algorithm and pipelined microprocessor design at a holiday party.  (I am personally guilty of the latter. Trust me; it’s rarely an effective ice breaker. If it is, you are in a safe place, with “your people.” See The Magic behind the Curtain: Hardware and Software for why I was (and still am) so excited.)

When asked what you do, pause, take a deep breath, and think. Few in your family really want to know all the details, and certainly not at the depth and breadth with which you are tempted to reply. This is your time to shine, by channeling your inner Mr. Wizard or Richard Feynman, by being clear, thoughtful, and memorable, but most of all, funny, self-deprecating, and brief.  (See Understanding and Explaining: The Feynman Technique.)

Keep your inner Sheldon Cooper in check, and whatever you do, do not engage in an argument, especially if some of your family members think COVID-19 vaccines were a government conspiracy, the Apollo moon landings were fake, or that humans and dinosaurs were temporally co-resident. Do not bring a knife (logical arguments) to a gunfight (emotional belief). Even if you win (and you won’t), you lose. (See Just the Facts, Ma’am: Reasoning is Not Dead, Jim.)

If asked by family members what I do, I have learned to check my propellor beanie at the door and focus on the potential impact of what I am doing – designing and building cheaper and faster computers -- and how those faster computers help create new medicines or enable better weather forecasts.  How research improves lives is something people generally understand and appreciate; the technical details are rarely important to them.  If they ask and clearly want to know, I will explain but try to do so in ways connected with their experience.

This is my most important piece of advice to young scientists and would-be science advocates.  Do not try to impress people with your erudition, and do not use obscure, though technically accurate nomenclature.  Speak clearly and in ways everyone can understand. It’s about the societal outcomes, not the personal quest for academic success or even expanding the human knowledge base. (See Simplifying Communication and Research, Education, Engagement and Bovine Transport.)

Above all, do not be flippant.  I learned this from painful experience. When asked if I were going to teach during the summer, I explained that I did not teach during the summertime because the federal government paid me to think. Though vacuously true, this response was met with stunned silence, especially after I unhelpfully added that if I had a good idea, I was supposed to write it down, omitting all discussion of research proposal submissions and peer review. The facial expressions alone confirmed my extended family member’s worst fears about my lack of common sense and wasteful government spending! (See Research Summers.)

Trust me, few people really want to hear an extended holiday discourse on exascale algorithmic efficiency, deep learning models, decidability and Zermelo-Fraenkel set theory, CRISPR DNA editing, or Lambda-CDM cosmology models – endlessly fascinating though they all are.  Even when enlivened with wild gesticulations and impromptu diagrams drawn in the mashed potatoes and gravy, in-depth science explanations are enough to make even your beloved grandmother look longingly at the holiday sherry, and they will confirm everyone’s unstated but deeply held belief that you are a “special child.”

Eschew the obscurity; chew the turkey. Stick to football if you can, opine on religion or politics if you must, but try to avoid the treacherous intellectual waters of scientific explanations over the holiday cranberry sauce and sliced turkey.  If they really want to know, they will pull you aside during the football game with specific questions. Only then should you unleash a small glimpse of your inner geek.

SenseCam Experiences: What I Really Do

As an academic researcher, what do I spend my time? Sometimes I wonder myself. To find out, while at RENCI, I once spent several weeks wearing a Microsoft SenseCam on a lanyard around my neck, and the University of North Carolina even wrote a story about my experiences.

A SenseCam takes periodic, still photographs, triggered either by elapsed time (roughly a minute) or by motion.  The resulting images then form a stop motion movie of the wearer’s day. The technology has also found use in assisting patients with memory loss, by replaying their experiences to strengthen memory pathways.

The Microsoft SenseCam was an early example of wearable computing, part of the Microsoft MyLifeBits life-logging project, long before smartphones with digital cameras were available. In turn, MyLifeBits was an attempt to realize Vannevar Bush’s 1940s vision of a Memex, which he described in a seminal essay, As We May Think:

Consider a future device for individual use, which is a sort of mechanized private file and library. It needs a name, and, to coin one at random, "memex" will do. A memex is a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.

My friend and future colleague, Gordon Bell, then at Microsoft, popularized the SenseCam for personal recording of his life experiences. Gordon is, of course, best known as a parallel computing pioneer, creator of the eponymously named Gordon Bell Prize, and the father of the Digital Equipment Corporation VAX 11/780, one of the first superminicomputers.

The New Yorker has a great 2007 article, entitled Remember This? about Gordon and his digital knowledge experiences with MyLifeBits. About the same time, I wrote about the coming library revolution, with digital archives and portable devices as potentially transformative educational forces, something I still believe, particularly when coupled with adaptive artificial intelligence.

The first sight of me wearing a SenseCam (see the photograph of a younger me above) triggered expressions of shock and concern for my health, as my colleagues initially feared I had suffered a near fatal heart attack and had been outfitted with an external pacemaker. After assuaging those fears, I explained the SenseCam’s functions, and I offered to put it in my pocket if they were concerned about being recorded during our meeting.  Some were, but most were simply amused by yet another manifestation of my geek disposition. Years later, the response was the same when I donned both a Narrative Clip and a Google Glass.  (See Though a Google Glass, Darkly.)

For a few months, I wore the SenseCam literally all over the world, from Australia to many trips across the United States.  Without exception, it prompted questions about the technology and operation (most common) and about privacy and intended uses (second most common).

So, what did I learn from my SenseCam experience?  Perhaps unsurprisingly, my professional life largely consists of three things: drinking prodigious amounts of coffee, reading a deluge of email, and participating in meetings. In this regard, the broad outlines of my professional life – and that of most other academics – differed little from that of most office workers. However, betwixt these prosaic activities were the distinguishing attributes of my professional life, the all too brief periods of thinking and writing – research papers, conference presentations, and that bane of all researchers, project reports and funding proposals.

Coda: A Safe Harbor

There is, of course, one safe harbor, where being a geek is valued at the holidays. I speak, of course, about the assembling, installing, configuring, and/or testing of any electronic devices the family received as gifts.  Here, family members will temporarily put aside their skepticism of your life choices and dubious assessment of your practical skills if you can connect the Wi-Fi or produce a needed USB hub from your backpack. If they are wondering why you have – at a minimum – a spare USB hub, as well as a set of HDMI and DisplayPort adapters, USB keys of varying capacity, and a microphone headset, then they’ve never been a road warrior. Bask in your momentary glory, then ask for a second helping of pecan pie.

Come to the Supercomputing Big Data Edge

SC17, the annual gathering of the supercomputing community is nigh. As always, there will be talk of next-generation technology – hardware, software, algorithms – and new applications. All of this will take place against the backdrop of the race to exascale computing – bigger and bigger, faster and faster. We will all geek out, and we will love it.

While we discuss operations/joule, quantum and neuromorphic computing, and new and exotic memory technologies to enable ever faster systems, there is another computing revolution underway, at the other extreme. As Richard Feynman famously said about nanoscale devices, "There's plenty of room at the bottom." Folks, I'm talking edge devices, the world of streaming data sensors that is changing the nature of computing and increasingly, of science as well. This is the subject of the forthcoming Big Data and Extreme-scale Computing (BDEC) report.

Computing History 101

The history of computing is one of punctuated equilibrium, with each pendulum swing, from centralized to distributed, bringing new and unexpected changes. It has also been a story of disruption from below, as successive generations brought order of magnitude shifts in price-performance ratios and sales volumes. Mainframes beget minicomputers, which begat workstations, which begat personal computers, which begat smartphones. The same has been true in scientific computing. Monolithic supercomputers (think the original Cray series) gave way to symmetric multiprocessors (SMPs) and distributed memory supercomputers. These gave way to clusters, now augmented with accelerators. Mind you, I still miss the lights on the Thinking Machines CM-5.

In both technical and "mainstream" computing, our largest systems are of unprecedented scale, whether globe spanning networks of massive cloud data centers or petascale and soon-to-be exascale HPC systems. As the Internet of Things sweeps across the computing landscape like a tsunami, one should ask about its analog in technical computing – the burgeoning networks of sensors that now capture science, engineering, environment, health and safety, and social data. (See Mobility, Transduction, and Garages)

The Sensor Edge

Today's smartphone-cloud ecosystem places most of the intelligence and data in the cloud. Indeed, all of the intelligent assistants – Siri, Cortina, and others – run in the cloud, dependent on precious wireless network bandwidth and relying on both behavioral history and deep learning algorithms. Limited by smartphone processing speeds, memory capacity, power budgets, and battery lifetime, and human expectations and physiology, there are good economic and technical reasons for this design.

There are also disadvantages, particularly in science. First, transmitting raw, time sensitive data to central sites places major demands on national and international networks. When the Large Hadron Collider (LHC) was under construction, this bandwidth and data processing demand was a major concern, triggering an international collaboration to build a distributed data storage hierarchy; those concerns remain as luminosity upgrades to the HLC are being planned.

The planned data pipeline for the Large Synoptic Survey Telescope (LSST) will send the raw data from the telescope in Chile to a dedicated supercomputer at NCSA, with a sixty second (maximum) delay before global alerts of transient phenomena must be distributed. Although this centralized approach can and will work when the daily data volume is measured in terabytes, the data deluge of the Square Kilometer Array (SKA) will require in situ reduction.

My friends in Chicago's Array of Things project grappled with another aspect of this local/remote tradeoff when designing urban sensors to capture and analyze human interactions. Communities were happy to support social dynamics analysis, but they did not want Big Brother video to leave the sensors. The privacy and social constraint requires low power, high-performance machine learning solutions on the sensors themselves.

Scale Challenges

Centralization also brings operational and security risks. My former Microsoft colleague, James Hamilton (now at Amazon), when asked why cloud data centers are not even bigger, said, "… the cost savings from scaling a single facility without bound are logarithmic, whereas the negative impact of blast radius is linear." We have all experienced the blast radius effect, at least metaphorically; when a cloud data center goes down, it affects both social networks and business, and it is international news. There is, perhaps, a salutatory lesson here for the ultimate, maximum size of supercomputers, but that is a discussion for another day.

In addition, the stateless design of the venerable TCP/IP stack includes no notion of data geo-location or intermediate data processing. Everyone who has debugged "last mile" network performance problems knows this all too well. To remediate this, research groups and companies have designed a variety of domain-specific network overlays, data caches, publisher-subscriber systems, and content delivery networks (CDNs).

Dramatic declines in computing costs mean it is now possible to deploy truly large numbers of inexpensive sensors. How dramatic? I have posed the following question on my campus. What would you do with 10,000, disposable ~$10 sensors? I was met with surprise at the scale of my question. For many of us, even those who came of age in the smartphone era, this scaling is a difficult question to answer because it defies our worldview of the possible. It is not a science fiction fantasy; it is a present reality.

A traditional hub and spoke model of data storage and sensor data analysis is increasingly neither scalable nor reliable. Amazon's Greengrass architecture is an early example of a generic, distributed approach, while NVidia's Metropolis system targets video in smart cities. There is an even more general model, where generic services support distributed data caching and processing throughout the network. Micah Beck (Tennessee) has made a thoughtful case for the design of such an approach, which he calls an Exposed Buffer Processing Architecture. Similarly, the Array of Things leaders have argued that edge processing is essential when

• The data volume overwhelms the network capability
• Privacy and security encourage short-lived data
• Resilient infrastructure requires local actuation/response
• Latency constraints demand the response times of dedicated resources

Simply put, these requirements imply a much more complex, distributed data storage and processing architecture for data-driven scientific computing than we have developed to date.

Supercomputing Edge Manifesto

This brave new world of streaming data has profound implications for the future of supercomputing. Here are just a few:

• Streaming data is continuous and requires a reconceptualization of workflow and scheduling systems. Periodic, batch processing is necessary but not sufficient.
• Sensor scale and complexity span many orders of magnitude, from inexpensive, disposable devices to nation-scale instruments. New orders of magnitude always expose new bottlenecks and necessitate architectural change to identify and decompose domain-specific and domain-independent elements.
• Systemic resilience takes on new urgency, extending beyond central HPC to include networks and distributed devices.
• Machine learning, modeling, and data assimilation apply concurrently at many sites and multiple levels, subject to energy and packaging constraints at the edge.
• We in HPC are not the only players in this space. Much as we learned and adapted much from the open source Linux community, we must learn from and embrace distributed data analytics from the sensor community.

As the name implies, the Big Data and Extreme-scale Computing (BDEC) project has been examining this nexus and the changing nature of scientific discovery enabled by the fusion of big data – from sensors large and small – machine learning, and computational modeling. It is a rapidly shifting environment, with new hardware and software ecosystems evolving rapidly.

Much of it remains largely unknown to the traditional HPC community, just as software containerization and machine learning were just a few years ago. That will change very soon. If you are attending SC17, come to the Birds of a Feather (BOF) discussion, late Wednesday, November 15.

A Literary Coda

In his book, Hell's Angels, the inimitable gonzo journalist Hunter S. Thompson once wrote about living on the edge – riding flat out – in the dark – on a motorcycle:

The Edge…There is no honest way to explain it because the only people who really know where it is are ones who have gone over. The others–the living–are those who pushed their control as far as they felt they could handle it, and then pulled back, or slowed down, or did whatever they had to when it came time to choose between Now and Later.

In supercomputing, we have always felt the need for speed, exploiting every new technology in the endless search for higher performance and greater capacity. It's time for the supercomputing community to come to the edge, where sensors and streaming data meet HPC, computational models, and machine learning.

There's lots of room at the edge for innovation and discovery. Let's not crash in the dark.

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NCSA@30: Reflections on the Revolution

When Larry Smarr, the founding director of the National Center for Supercomputing Applications (NCSA), quietly confided to me in the fall of 1999 that he was moving the UCSD, I was Head of the Department of Computer Science during the midst of the first dot.com boom, itself enabled by NCSA Mosaic and the first web revolution. Netscape and its Illinois alumni were remaking Silicon Valley; Max Levchin had just founded PayPal while a student in the department; alumnus Tom Siebel was flying high with new CRM technology at Siebel Systems; and we had just celebrated HAL's birthday (from 2001: A Space Odyssey) with a massive Cyberfest event that attracted worldwide attention. ("I am a HAL 9000 computer, production number 3; I became operational at the HAL plant in Urbana Illinois on January 12, 1997.")

Students were beating down the doors, begging to get into computer science; we were hiring faculty at a frantic pace, unable to keep up with demand; venture capitalists were calling, and virtual reality (VR) was all the rage. In many ways, it was much like today's deep learning and VR boom, powered by cloud services and big data. As Yogi Berra might say, it's like déjà vu all over again, as we consistently underestimate the power of computing to reshape our society.

When Larry and I talked about the future and the NCSA directorship in the fall of 1999, I already had had a long association with NCSA. I had worked on joint research projects, and I had done some of the early visualization of web traffic when NCSA's web server had been the world's busiest. I was also the leader of the data management team – what we would now call big data – for the National Computational Science Alliance (the Alliance), which was anchored by NCSA as part of the NSF Partnerships for Advanced Computational Infrastructure (PACI) program. Based on that conversation with Larry, it was clear that both NCSA and my life were about to change in some profound ways. However, despite the universal sense that computing was a revolutionary force, none of us could have predicted just how profound those changes would be. I am very proud of the NCSA team and humbled by what they accomplished.

In the space of four years, we broke ground on three new buildings, deployed the first Linux clusters in national allocation by NSF, negotiated creation of a 40 gigabit per second transcontinental optical network for research, developed an early GPU cluster based on Sony PlayStation 2's, launched the NSF TeraGrid, and committed to support the Large Synoptic Survey Telescope (LSST). In between all that, I joined the President's Information Technology Advisory Committee (PITAC), there were national meetings (the High-end Computing Revitalization Task Force (HECRTF)) and Congressional hearings about the future of supercomputing, and plans for petascale systems set stage for what would become Blue Waters. It was, in a very real way, an inflection point for the nature of computational science and scientific computing that continues today.

At the end of the 1990s, custom-built, parallel systems (MPPs) such as the Thinking Machines CM5 and SGI Origin 2000 dominated high-performance computing, having displaced vector machines such as the Cray Y-MP. As commodity PCs increased in performance, NCSA and the Alliance began experimenting with home-built Linux clusters, and in 2001, we deployed the first two of these – a one teraflop IA-32 cluster and one teraflop IA-64 Itanium cluster.

As strange as it may seem now, with Linux clusters the unquestioned standard for scientific computing, this was then viewed as a radical and highly risky decision. People often asked me, "Who do you call for support when something fails?" and "Who is responsible for the software?" The answers, of course, were that we were galvanizing a community and building a new support model, as we reinvented the very nature of scientific computing. NCSA led the way, as it has throughout its history.

What did not happen is not as well known. We had an opportunity to launch a big data initiative, in partnership with Microsoft. In 2000, secured a contingent $40M gift from Microsoft for a Windows-based data analytics system that would complement the Linux computational infrastructure, bringing database technology to high-performance computing. Alas, we did not receive the necessary federal funding. I will always wonder if we might have kickstarted the big data revolution a decade earlier. The irony is not lost on me that a few years later I would find myself leading the eXtreme Computing Group at Microsoft to design next-generation cloud computing infrastructure in support of deep learning and cloud services, using lessons and ideas from high-performance computing.

Despite the data analytics setback, the success of two Linux clusters provided the evidence needed for us to propose and deploy the NSF TeraGrid, connecting NCSA, SDSC, Argonne and Caltech in the world's largest open computing infrastructure for scientific discovery. Anchored by NCSA and SDSC, and in partnership with Intel and IBM, the TeraGrid brought distributed Grid services into national production and the subsequent expansion of the Extended Terascale Facility (ETF) created what became the NSF XSEDE program.

The TeraGrid also bought a dramatic increase in national bandwidth, as Charlie Catlett and I negotiated with Qwest to create a 40 gigabit per second wide area network that connected the Illinois and California TeraGrid sites. Our goal was to catalyze a new way of thinking about big data pipes. Ironically, Charlie and I almost nixed this deal, as we had been seeking an even faster 160 gigabits per second connection, only to settle for 40. To put this in perspective, at the time, the Internet2 transcontinental backbone operated at only 2.5 gigabits per second.

While system building, we also had great fun, made possible by the truly wonderful people that were and are the NCSA faculty, staff and students. We built (display) walls and played with smart badges for the SC02 conference. Donna Cox and her team created incredible video of severe storms and galactic evolution that illuminated the beauty of scientific discovery. Rob Pennington and his staff bought Playstation2 systems on eBay and built a GPU-accelerated Linux cluster that caught the attention of the NY Times and helped usher in today's GPU clusters.

In the midst of all this, we ran out of space – offices and computing facilities. The University of Illinois committed to an expansion of the Advanced Computation Building (ACB) that allowed us to deploy the terascale Linux clusters and the TeraGrid. ACB had originally been built to house ILLIAC IV and served as the machine room for NCSA, but the new clusters required more power, more cooling and more space. We designed the ACB expansion with plans for yet another expansion, one rendered unnecessary by the construction of the National Petascale Computing Facility.

I must give enormous credit to the university leadership team, because they built the ACB expansion before we had secured the NSF funding for new machines. I made a handshake, $100M deal with then Provost Richard Herman that if he built the ACB expansion, that NCSA would fill it with hardware. Both of us took a huge political risk, but we honored that commitment. It was just one event in a long history that reflects the repeated willingness of the University of Illinois to invest in the future.

Collectively, the team also secured funding for the NCSA office building and the new Siebel Center for Computer Science, creating an informatics quadrangle that continues to support student, faculty and staff discovery. During this time, I distinctly remember talking to Tom Siebel about the old Digital Computer Laboratory (DCL), which he described as looking like a prison. I remarked that he could help fix that, which began a long series of campus conversations that culminated in his remarkable $32M gift. Likewise, I was pleased that the NCSA building finally came to fruition, after many years of delays. It is not well known, but the two buildings were designed to allow them to connected via an extension to the NCSA building and an arch extending from the northwest corner of Siebel Center.

It was an exhilarating and exciting time. None of it would have happened without an incredible team. We built on Larry Smarr's groundbreaking vision for a supercomputing center. We created machines, software and tools; we engaged researchers, scholars and artists; we invented the future. That is – and always will be – NCSA's mission. Congratulations on 30 great years!

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Exascale Computing and Big Data: Time to Reunite

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog. In addition, you can find the video of an interview and the full text of the CACM article, co-authored with Jack Dongarra.

In other contexts, I have written at length about the cultural and technical divergence of the data analytics (aka machine learning and big data) and high-performance computing (aka big iron) communities. I have euphemistically called them "twins separated at both." (See HPC, Big Data and the Peloponnesian War and Scientific Clouds: Blowin' in the Wind.) Like all twins, they share technical DNA and innate behaviors, despite their superficial differences. After all, in a time long, long ago, they were once united by their use of BSD UNIX and SUN workstations for software development.

Since then, both communities have successfully built scalable infrastructures using high-performance, low cost x86 hardware and a rich suite of (mostly) open source software tools. Both have addressed ecosystem deficiencies by developing special-purpose software libraries and tools (e.g., SLURM and Zookeeper for resource management and MPI and Hadoop for parallelism), and both have optimized hardware for their problem domains (e.g., Open Compute hardware building block standardization, FPGAs for search and machine learning, and GPU accelerators for computational science).

Like many of you, I have seen this evolution firsthand, as a card-carrying geek in both the HPC and cloud computing worlds. One of the reasons I went to Microsoft was to bring HPC ideas and applications to the nascent world of cloud computing. While at Microsoft, I led a research team to explore energy-efficient cloud hardware designs and new programming models, and I launched a public-private partnership between Microsoft and the National Science Foundation on cloud applications. Now that I am back in academia, I am seeking to bring cloud computing ideas back to HPC.

In that spirit, Jack Dongarra and I recently co-authored an article for the Communications of the ACM on the twin ecosystems of HPC and big data and the challenges facing both. Entitled, Exascale Computing and Big Data, the article examines the commonalities and differences, and discusses many of the unresolved issues associated with resilience, programmability, scalability, and post-Dennard hardware futures. Most importantly, the article makes an impassioned plea for hardware and software integration and cultural convergence.

The possibilities for this convergence are legion. The algorithms underlying deep machine learning would benefit from the parallelization and data movement minimization techniques commonly used in HPC applications and libraries. Similarly, the approaches to failure tolerance and systemic resilience common in cloud software have broad applicability to high-performance computing. Both domains face growing energy constraints on the maximum size of feasible systems, necessitating shared focus on domain-specific architectural optimizations that maximize operations per joule.

Perhaps most important of all, there is increasing overlap of application domains. New generations of scientific instruments and sensors are producing unprecedented volumes of observational data, and intelligent, in situ algorithms are increasingly required to reduce raw data and identify important phenomena in real time. To see this, one need look no further than applications of machine learning to astronomy, which now include automated object identification. Conversely, client plus cloud services are increasingly model-based, with rich physics, imaging processing and context that depend on parallel algorithms to meet real-time needs; augmented reality applications are one such exemplar.

The explosive growth of Docker and containerized software management speaks to the need for lightweight, flexible software configuration management for increasingly complex and rich software environments. My hope is that we can develop a unified hardware/software ecosystem that leverages the technical and social strengths of each community. Each would benefit from the experiences and insights of the other. It is past time for the twins to have a family reunion.

White Spaces: Celebrating the Cambridge Trial

Today, in Cambridge, UK, we celebrated the ten months of experiences and successes from the Cambridge white spaces trial, which was organized by a consortium of companies, including Microsoft. The event brought together telecom regulators from around the world, hardware, software and content providers and interested parties to discuss the potential and the reality of dynamic spectrum management. The "white spaces" – the unused channels in the TV bands at each location – can be used for wireless communication as long as there is no interference with the primary (television broadcast) usage.

This white spaces spectrum is particularly valuable due to its signal propagation characteristics. Sometimes called "Super Wi-Fi," it can provide broad wireless coverage with relatively few access points, for rural areas that are often digitally disenfranchised, for machine-to-machine communication that can enable smart grids, smart cities and intelligent transportation systems, and for mobile data traffic offload in urban areas.

White spaces use has been approved by the U.S. Federal Communications Commission (FCC), and approval by the U.K. regulator (Ofcom) is near. Other regional and national bodies are also moving forward. In this spirit, today, we also announced formation of a new consortium to launch a new trial in Singapore. Details on all of this can be found on the Microsoft on the Issues blog, where I wrote about Broadband White Spaces – Ready to Go Global. For more details and news coverage, type the words, "white spaces" and "Cambridge" into your favorite search engine.

It Is Fall: There Must Be Clouds

Today, I testified to the Subcommittee on Technology and Innovation of the U.S. House of Representatives Committee on Space, Science and Technology. The topic of the hearing was "The Next IT Revolution?: Cloud Computing Opportunities and Challenges." At the hearing, I was joined by Mike Capellas (CEO, VCE), Nick Combs (Federal CTO, EMC), and David McClure (Associate Administrator, GSA).

This hearing drew on some of the content from the TechAmerica Commission on the Leadership Opportunity in U.S. Deployment of the Cloud (CLOUD²), on which I had the privilege to serve as the commercial vice-chair. In addition, my own remarks were augmented by Microsoft's cloud engagement experiences with the U.S. National Science Foundation and other research funding agencies. (See Innovation Via Client Plus Cloud: Microsoft-NSF Partnership for background.)

My complete written testimony will ultimately appear on the Subcommittee web site here, and the hearing charter, the subcommittee press release, and the hearing video capture most of context. However, there are a few points from my testimony that seem appropriate to repeat here. These concern research empowerment via the cloud, support for basic research and education and broadband expansion.

Accelerating Scientific Discovery for Research via the Cloud

Throughout the history of science, data has been scarce and precious. Indeed, the modern scientific method is defined by a careful cycle of hypothesis and experiment, which gathers experimental data to test the hypothesis. In a few short years, scientists and engineers have gone from scarcity to an incredible richness, necessitating a significant change in how they manage and extract insight from all this data. In a parallel shift, many of our scientific, engineering and societal questions increasingly lie at the intersections of traditional disciplines.

Increasing data volumes and the complexity of collaboration on interdisciplinary problems are challenging our historical approaches to discovery and innovation via computing. Most researchers and research institutions are ill-prepared for the large-scale computing infrastructure management challenges posed by large data sets and complex models. The cloud and associated applications and tools offer a possible solution to this challenge by letting scientists be scientists.

The U.S. government can accelerate this transition by encouraging the purchase of cloud services, in addition to the acquisition of local IT infrastructure, and by supporting new tools that facilitate distributed collaboration and simplify access to multidisciplinary scientific data. As I have noted before, Microsoft is acting on this belief, working in partnership with the National Science Foundation.

Fostering Continued Support for Computing Research and Education

Today's cloud technology is derived from basic computing research conducted over the past four decades. To ensure that the U.S. continues to remain at the forefront of cloud technology, continued investment in basic research is critical. There are deep and open questions in areas as diverse as the future of silicon scaling and system-on-a-chip design, energy-efficient systems, primary and secondary storage, data mining and analytics, wired and wireless networks, system resilience and reliability, privacy and security, and user interfaces and accessibility, to name just a few. Insights and innovations from this research will spawn new companies, create jobs and reshape our future.

In addition to continued research investment, it is critical to support the pipeline that produces researchers, and others who will able to invent new uses of the cloud and information technology. The U.S. Bureau of Labor Statistics estimates that the computing sector will have 1.5 million job openings over the next 10 years, yet the number of graduates receiving Bachelors, Masters or Ph.D. computer science degrees is far short of that. In addition, we must strengthen the quality of and access to computing education at all levels. Consistent with these concerns about the IT workforce and computing education, Microsoft is a founding member of the Computing in the Core coalition, which supports computer science education, particularly at the K-12 level.

Broadband: The Need for Speed

The web and cloud services depend on broadband communications. Without them, service and information sharing are impossible. Concomitantly, ensuring reliable wired and wireless connectivity, with adequate bandwidth and latency, is critical to ensuring successful adoption of the cloud and realization of its benefits. The phenomenal growth of digital data, the rise of streaming media services, and the explosive growth of Internet-connected devices are all straining our nation's broadband infrastructure.

It is critical that we continue to design and deploy new backbone networks that support higher data rates, develop and deploy new protocols and infrastructure for the next generation of wireless networks and define the global standards that will shape the future of the globe-encircling cloud. We must also remember that digital access to information and services is increasingly the enabler of economic competitiveness, of lifelong education in a rapidly changing world, and of government efficiency and service delivery.

21^st Century Innovation

This is an exciting time, when the future becomes the present. Computing can be a great equalizer, providing access to the world's knowledge base to individuals, anywhere, anytime; empowering entrepreneurs and companies large and small to sell their products and ideas globally; and enabling scientists and engineers to discover and innovate in ways that will define the future.

White Spaces and Adaptive Communication

I have been spending quite a bit of time on approaches to address the spectrum crisis – the seemingly insatiable demand for more wireless communication as the number of Internet-connected devices continues to grow. We are working with a consortium in the UK to host a major white spaces trial. Details can be found on the Microsoft blog, along with a video explaining the challenges and opportunities.

HPC In The Clouds: ISC Reflections

I just spent an all too brief two days in Hamburg, attending the International Supercomputing Conference (ISC). It was a chance to catch up with some old friends, discuss the ever changing nature of computing technology and reflect on the Weltanschauung that is the HPC community. In addition to the buzz around the latest Top500 list and the new Japanese machine atop the list, there were many debates about the intersection of HPC and cloud computing.

My friend and former collaborator in North Carolina, Wolfgang Gentzsch, organized a session at ISC on HPC and the Cloud, where I was delighted to participate. In my talk, I made four points, several of which were echoed by Josh Simons from VMWare in a great talk that followed mine.

First, HPC has experienced several punctuated equlibria, as we transitioned from one parallel computing model to another. From instruction level parallelism in the mainframe era through vector systems, MPPs, SMPs, clusters and clusters plus accelerators, each transition has brought technical and cultural challenges. The technical challenges have been well documented; the cultural challenges are less often discussed, for they affected the technology providers (vendors), the technology operators (HPC centers and facilities) and the technology users (scientists, engineers and other researchers.) Clouds bring another set of technical and cultural challenges.

When any new technology appears, there is a great temptation to see it through the lens of the old, either in nomenclature or behavior (see "horseless carriage"), or attempt to converge the new into a variant of the old (see assimilation by the Borg). Many corporate acquisitions fail for just this reason, failing to recognize one is acquiring something precisely because it is not like the acquirer. Successful adoption and exploitation occur when all parties are willing to work together to help create the ecosystem of capabilities needed. I believe we are moving down the path of successful adoption with cloud-based HPC.

Second, I talked about the need to continue to democratize access to HPC, targeted the "excluded middle," those potential and actual technical computing users who need more computing than available on desktops but who find traditional HPC too difficult to use. They are the majority of scientists and engineers. Client plus cloud acceleration is one possible solution to this problem, and could empower a new set of HPC users. See HPC and the Excluded Middle.

Third, I reminded the audience that there is another burgeoning challenge and opportunity – big data. We have big problems with big data, and the scientific and engineering communities are drowning in the data produced by a new generation of sensors and scientific instruments. We need simple, easy to use tools that allow researchers to mine and extract insight from that data. In that spirit, I discussed our continuing work on Excel DataScope, which allows one to use the familiar Excel interface while applying rich analytics algorithms in the cloud. See The Zeros Matter: Bigger Is Different.

Finally, I highlighted the worldwide progress of Microsoft's research cloud partnerships. We now have over 75 active projects, launched in partnership with research agencies in the U.S., the EU, Japan, Australia and Taiwan. (See Cloud Seeding: Stimulating Discovery and Innovation.) The partnerships are an opportunity for us to work together to understand how clouds can best support technical computing and discovery, driving change in the cloud infrastructure space and service providers and also educating researchers and HPC providers about this technical and cultural transition.

It's always exciting to be a part of these technical collaborations.

On Licking the Cookies

All organizations and cultures develop their own idiosyncratic acronyms, abbreviations, lingo and metaphors. (Yes, there are probably a few similes and alliterations as well.) One need look no further than the social networks and Twitter to find a veriable cornucopia of such phrases and acronyms (e.g., LOL or OMG). The hacker community (the original one) also gifted us with the gems RTFM and UTSL.

I was once offered my first full time job in the software industry because I knew what JCL was needed to apply a PTF to BTAM. That's IBM speak for applying a software patch to telecommunications software. In this sense, Microsoft is not unique, for its worldwide employees have developed their own lexicon of metaphors and abbreviations, as well as appropriating others were useful. As a relative newcomer to the corporate world after twenty-five years in academia, several of these abbreviations, words and phrases immediately caught my ear.

Licking the Cookies

Not to be confused with the joy of double stuff Oreos or even licking the spoon, licking the cookies refers to claiming action or skill in an area but in a way that prevents anything from happening. It is intended to conjure an image of a cookie plate, where one licks the cookies then returns them to the plate, rendering them inedible by all. As the phrase suggests, you have not eaten the cookie (i.e., taken action) but you have prevented others from doing so either.

Cookie licking is endemic to any large organization or culture where multiple teams compete for credit and accomplishment. We have all seen this behavior in our organizations and occasionally been guilty of it ourselves. This is one of the more pernicious forms of non-collaborative, non-team play.

After understanding the phrase, I realized that subtle and not so subtle forms of cookie licking occur in academia as well. I have seen it in debates over academic directions, discussions of research priorities and in paper and grant proposal reviewing. Of course, academia has its own set of arcane phrases.

Eating Your Own Dog Food (aka Dogfooding)

Dogfooding is a hoary classic, well used and abused across the computing milieu. It refers to using early (alpha or beta) versions of a system you and others have developed before it is released, often as an informal tester. It's not haute cuisine or even nouveau cuisine; it's dog food, edible and nutritious, but neither elegant nor tasty.

Learnings

Finally, there are learnings, as in "Our learnings on this suggest optimization is our first priority." The first time I heard this linguistic oddity, I was nonplussed and puzzled, confused and uncertain, perhaps even mortified, but I was not chagrined. After hearing it several times, I finally asked if the speaker really meant insights, understanding, observation, or even experience. The answer, of course, was yes. Nobody I have yet asked, however, knows the origin of the usage, though I am sure the exegesis would be fascinating.

BFN, I am OOF.

Microsoft/NSF Cloud Research Partnership

Today, Microsoft and the U.S. National Science Foundation (NSF) announced the latest awardees for the joint Microsoft-NSF research partnership in cloud computing. (See the NSF announcement: The Sky Is No Limit: 13 Research Teams Compute in the Clouds.) This is a continuation of the program that then NSF Assistant Director for Computer and Information Science and Engineering (CISE), Jeannette Wing, and I announced in February 2010 (See Microsoft and the National Science Foundation Enable Research in the Cloud and Democratizing Research: How "Client Plus Cloud" Computing Can Amplify What's Possible for Scientists).

As part of this agreement with the NSF, Microsoft is making available access to Windows Azure, Microsoft's cloud computing platform. In addition, a support team of Microsoft researchers and developers is working with grant recipients to equip them with a set of common tools, applications and data collections that can be shared with the broad academic community, and also providing expertise in research, science and cloud computing. Broader details on the program and its research tools and software for Windows Azure can be found here.

Diverse Projects

Increasingly, the important scientific questions lie at the intersections of traditional disciplines, and insights from multidisciplinary collaborations drive innovation, economic development and our response to complex problems and natural disasters. This is one of many reasons I am so pleased with the technical diversity among the list of NSF-Microsoft award recipients.

All of the award recipients were selected via NSF's rigorous peer review process, which emphasized the scientific merit of the proposed work. Projects range from exploring scientific applications of cloud computing as well as extensions of cloud computing with new tools and techniques.

• Cornell University (Kenneth Birman) - Building Scalable Trust in Cloud Computing
• J. Craig Venter Institute (Audrey Tovchigrechko) - Bettering Interactive Protein-Protein Docking
• SUNY at Buffalo (Tevfik Kosar) - Enhancing Stork Data Scheduler for Azure
• University of California, San Diego (Kenneth Yocum) - Utilizing Continuous Bulk Processing
• University of Colorado, Boulder (Richard Han) - Enabling Mobile Cloud Computing
• University of Michigan, Ann Arbor (Qiaozhu Mei) - Refining Language Models using Web-scale Language Networks
• University of North Carolina at Charlotte (Zhengchang Su) - Predicting Transcription Factor Binding Sites for Genes
• University of South Carolina (Jonathan Goodall) and the University of Virginia (Marty A. Humphrey) - Managing Large Watershed Systems
• University of Southern California (Viktor Prasanna) - Tackling Large Scale Graph Problems
• University of Texas at Austin (Michael Walfish) - Storing Data with Minimal Trust
• University of Washington (Magdalena Balazinska) - Understanding Relational Data Markets
• Virginia Tech (Wuchun Feng) - Conducting Intensive Biocomputing
• Virginia Tech (Kwa-Sur Tam) - Effectively and Widely Using Renewable Energy Sources

  Worldwide Reach

The Microsoft/NSF partnership is but one part of a broader international program that the eXtreme Computing Group (XCG) and Microsoft Research are building with the scientific research agencies worldwide. We believe cloud computing, coupled with powerful client tools, can transform the nature of research. This worldwide program currently targets collaborations with several national and international research partnerships, including

• National Institute of Informatics of Japan Info-plosion project
• European Commission FP7 funding program, Venus-C project
• U.K. Engineering and Physical Sciences Research Council (EPSRC) Horizon project
• French INRIA-Microsoft collaboration on cloud computing
• Australian engagements with NICTA, ANU and CSIRO

Power via Simplicity

This Microsoft/government partnership is about so much more than access to cloud computing resources. The deep partnership between academic and Microsoft researchers, creation and release of easy-to-use client tools and an exploration of the new world of massive data are all key elements of our shared journey to a new model of data rich analysis enabled by powerful client tools and cloud services.

Any successful technology ultimately becomes invisible, enabling and empowering without requiring the user to focus on the idiosyncrasies of the technology itself. Technical computing can and should be an invisible intellectual amplifier, as easy to use as any other successful consumer technology. As I wrote last year when we first launched this program, it is really about transforming how we conduct research by broadening researcher capabilities, fostering collaborative research communities, and accelerating scientific discovery by shifting the focus from infrastructure to empowerment.