Reed's Ruminations: A Blog by Dan Reed

Analog Computing: Time for a Comeback?

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.

In the early dates of the automobile, there was a lively competition among disparate technologies for hegemony as the motive power source. Steam engines were common, given their history in manufacturing and locomotives, and electric vehicles trundled through the streets of many cities. The supremacy of the internal combustion engine as the de facto power source was by no means an early certainty. Yet it triumphed due to a combination of range, reliability, cost and safety, relegating other technologies to historical curiosities.

Thus, it is ironic that we are now assiduously re-exploring several of these same alternative power sources to reduce carbon emissions and dependence on dwindling global petroleum reserves. Today's hybrid and electric vehicles embody 21^st century versions of some very old ideas.

There are certain parallels to the phylogenic recapitulation of the automobile now occurring in computing. Perhaps it is time to revisit some old ideas.

Analog History

Use of the word "computer" conjures certain images and brings certain assumptions. One of them, so deeply ingrained that we rarely question it, is that computing is digital and electronic. Yet there was a time not so long ago when those adjectives were neither readily assumed nor implied when discussing computing, just as the internal combustion engine was not de rigueur in automobile design.

The alternative to digital computing – analog computing – has a long and illustrious history. Its antecedents lie in every mechanical device built to solve some problem in a repeatable way, from the sundial to the astrolabe. Without doubt, analog computing found its apotheosis in the slide rule, which dominated science and engineering calculations for multiple centuries, coexisting and thriving alongside the late comer, digital computing.

The attraction of analog computing has always been its ability to accommodate uncertainty and continuity. As Cantor showed, the real numbers are non-countably infinite, and their discretization in a floating-point representation is fraught with difficulty. Because of this, the IEEE floating-point standard is a delicate and ingenious balance between range and precision.

All experimental measurements have uncertainty, and quantifying that uncertainty and its propagation in digital computing models is part of the rich history of numerical analysis. Forward error propagation models, condition numbers, and stiffness are all attributes of this uncertainty and continuity.

Hybrid Futures

I raise the issue of analog computing because we face some deep and substantive challenges in wringing more performance from sequential execution and the von Neumann architecture model of digital computing. Multicore architectures, limits on chip power, near threshold voltage computation, functional heterogeneity and the rise of dark silicon are forcing us to confront fundamental design questions. Might analog computing and sub-threshold computing bring some new design flexibility and optimization opportunities?

We face an equally daunting set of challenges in scientific and technical computing at very large scale. For exascale computing, reliability, resilience, numerical stability and confidence can be problematic when input uncertainties can propagate, and single and multiple bit upsets can disturb numerical representations. How can we best assess the stability and error ranges on exascale computations? Could analog computing play a role?

Please note that I am not advocating a return to slide rules or pneumatic computing systems. Rather, I am suggesting that we step back and remember that the evolution of technologies brings new opportunities to revisit old assumptions. Hybrid computing may be one possible way to address the challenges we face on the intersecting frontiers of device physics, computer architecture and software.

A brave new world is aborning. Might there be a hybrid computer in your hybrid vehicle?

Surrounded By Opportunities

Perhaps the closest I have approached the feeling of intellectual omnipotence (and it has not been often) was when I was a graduate student, teaching undergraduate FORTRAN programming for science and engineering students. Those were the days of punched cards, batch processing and line printer output. It was a time when dinosaurs roamed the earth, my slide rule was still a fond memory, and we were computing on massive machines with less performance than my smartphone has today. (Ignore that part about the dinosaurs, as it was a wee bit earlier.)

During my office hours, students would troop through for help with their programming assignments, bringing program printouts. These printouts began with a "day file" showing the timeline of compilation and execution steps (JCL steps on IBM S/360), followed by the program and then any output. After having seen a few students, you realize that elementary programming errors have just a few equivalence classes, often identifiable from the day file alone. I took great pride in listening to the student description of the problem, predicting the root cause and then immediately locating it on the printout. The student was inevitably amazed. Yes, it was a cheap parlor trick, but as a graduate student, you take gratification where you can find it.

All of this reminds me that sequential programming remains difficult and parallel programming even more difficult, despite all our efforts to tame the unruly beast with syntactic sugar, specification and scripting languages, libraries and tools. I am no expert in parallel programming language design, nor am I a cognitive psychologist, but like many of my readers, I have pulled a few all-nighters writing code. Perhaps that at least qualifies me to have an opinion.

In a world of parallel programming, multicore architectures, dark silicon, and rising software complexity, we face some difficult choices. It's time to learn from the past.

Fading Abstractions

We have been fortunate to date. Although sequential programming at scale is intellectually challenging, we have been riding the wave of ever-increasing processor performance, made possible by semiconductor advances. In turn, these were based on an abstraction – the Mead-Conway design rules –that largely separated device physics from microprocessor architecture. Finally, the von Neumann abstraction has also served us well, allowing decades of software development atop an easily understood model of sequential instruction execution.

Our run of good luck is over. Put another way, we are surrounded by opportunities! Parallelism is now the norm for almost all software developers, not just those of us in high-performance computing. More perniciously, heterogeneous parallelism is here, with disparate cores, accelerators and functional units. To manage dark silicon and exploit continuing transistor density increases, this trend will only accelerate, with a dizzying array of heterogeneous chips and devices challenging code portability and performance tuning. Finally and most worrisome, our device physics abstractions are breaking down, with device physics affecting architecture. We face real silicon reliability, process variation and energy challenges, and near threshold voltage computations emerging.

Holistic Collaboration

The abstractions of device physics, processor architecture and programming models naturally led to a corresponding intellectual specialization. Consequently, those working in materials science and device physics have little in common with those designing software tools. I believe that must change if we are to address our challenges.

We need new, integrated design methodologies and rich collaborations that consider end-to-end design and capability. In many ways, this is a return to the past, necessitated by the desire for continuing increases in performance, mediated by the desire for reliability, scalability, programmability and economic feasibility. It will not be easy, but it is a way forward.

Thoughts on 9/11

I had the unusual experience of being on the Capitol Hill of the U.S. during the event. A brief summary of my experience is recounted here: I Could Smell the Smoke.

Why We Compute

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.

Why do we, as researchers and practitioners, have this deep and abiding love of computing? Why do we compute? Superficially, the question seems as innocuous as asking why the sky is blue or the grass is green. However, like both of those childhood questions, the simplicity belies the subtlety beneath. Just ask someone about Raleigh scattering or the quantum efficiency of photosynthesis if you doubt that simple questions can unearth complexity.

At its most basic, computing is simply automated symbol manipulation. Indeed, the abstract Turing machine does nothing more than manipulates symbols on a strip of tape using a table of rules. More deceptively, the rules seem simpler than some board games. Though vacuously true, the description misses the point that symbol manipulation under those rules captures what we now call the Church-Turing thesis.

However, as deep and as beautiful as the notion of computability really is, I doubt that is the only reason most of us are so endlessly fascinated by this malleable thing we call computing. Rather, I suspect it is a deeper, more primal yearning, one that underlies all of science and engineering and that unites us in a common cause. It is the insatiable desire to know and understand.

Lessons from Astronomy

When I recently I stood atop Mauna Kea, looking at the array of telescopes perched there, I was again struck by our innate curiosity. Operated by a diverse array of international partnerships and built on Mauna Kea at great expense, they are there because we care about some fundamental questions. What is the evolutionary history and future of the universe? What are dark matter and dark energy? Why is there anything at all?

Answers to these questions are not likely to address our current economic woes, improve health care or address our environmental challenges. We care about the answers, nevertheless.

As I pondered the gloaming sky, my thoughts turned to Edwin Hubble, who first showed that some of those faint smudges in the sky were "island universes" – galaxies like our own. The universe was a far bigger place than we had heretofore imagined. As Hubble observed about this age-old quest to understand:

From our home on the Earth, we look out into the distances and strive to imagine the sort of world into which we are born. Today we have reached far out into Space. Our immediate neighborhood we know rather intimately. But with increasing distance our knowledge fades, and fades rapidly, until at the last dim horizon we search among ghostly errors of observations for landmarks that are scarcely more substantial. The search will continue. The urge is older than history. It is not satisfied and it will not be suppressed.

Hubble's comment was about the observational difficulties of distance estimation and the challenges associated with identifying standard candles. However, it could just as easily have been a meditation on computing, for we are driven by our own insatiable desires for better algorithms, more flexible and reliable software, new sensors and data analytics tools, and by ever larger and more faster computers.

Computing the Future

Why do we compute? I suspect it is for at least two, related reasons, neither relating to publication counts, tenure, wealth or fame. The first is the ability to give life to the algorithmic instantiation of an idea, to see it dance and move across our displays and devices. We have all felt the exhilaration when the idea takes shape in code, then begins to execute, sometimes surprising us in its unexpected behavior and complexity. Computing's analog of deus ex machina brings psychic satisfaction.

The second reason is that computing is an intellectual amplifier, extending our nominal reach and abilities. I discussed the power of computing to enable and enhance exploration in another CACM blog. (See Intellectual Amplification via Computing.) It is why those of us in computational science continually seek better algorithms and faster computer systems. From terascale to petascale and the global race to exascale, it is a quest for greater fidelity, higher resolution and finer time scales. The same deep yearning drives astronomers to seek higher resolution detectors and larger telescope apertures. We are all chasing searching the ghostly signals for landmarks.

It is our ability to apply our ideas and their embodiment in code to a dizzying array of problems, from the prosaic to the profound, that attracts and compels us. It is why we compute.

Hubble was right. We compute because we want to know and understand. The urge is deep and unsatisfied. It cannot be denied.

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.