N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACMblog.
How often have you picked up a scholarly journal in a discipline far removed from your expertise, only to be stymied and mystified by the disciplinary jargon? It can be humbling and intimidating when one fails to understand the meaning of all the words in an article's title or the abstract. When coupled with the contextual knowledge often implicitly assumed by the authors, the gulf of understanding yawns wide and deep.
This epistemological and linguistic chasm separates and isolates even within the broad tent of our own discipline, which spans everything from the fundamental theory of computability to the professional practice of informatics. If you have any doubt, open the ACM Digital Library and scan a few articles in a specialty far removed from your own.
In a world where discoveries increasingly lie at the boundaries of traditional research disciplines, simplifying communication and encouraging multidisciplinary dialog and partnership have never been more necessary. In almost every case, computing is an essential element of disciplinary and multidisciplinary research. Thus, it is time for us to embrace writing as a collaborative enabler, rather than a research burden.
All too often, we academics write in a strange argot of disciplinary esoterica that can obscure the very ideas we seek to communicate. If you have ever encountered an article like the following, you know what I mean.
"Spatiotemporal domicile proximity to retroverting domestic ruminants," I. B. Smart, I. A. Postdoc & O. Authors, International Journal of Bovine Mobility, Vol. 123, No. 11, pp. 2143-2147, 2013
However linguistically facile and intellectually adept, the authors and putative ruminant experts failed to say what they really meant ("wait for the cows to come home") and why that might matter.
In a similar spirit, the late Richard Hamming once famously noted, "The purpose of computing is insight, not numbers." The academic publishing cognate is best summarized as, "The purpose of writing is communication, not obscuration." There is also an important corollary, "Write to communicate, not to impress or intimidate." Yes, subtlety and nuance are important, but they are mere handmaidens to clarity and lucidity.
Even when we avoid these linguistic traps, another, equally deadly one waits to ensnare – turgid and passive prose that invites only slumber. As anyone knows who has either served as a journal editor or reviewed a seemingly endless stack of conference paper submissions, passive, wordy and meandering prose makes identifying the key ideas and assessing their importance even more difficult.
Technical papers are not page turning spy novels, nor should they be, but they can still be interesting, clear and engaging as they convey the essential facts. As a writer, one's job is to make the reading easy; you want your papers to be read and appreciated.
The Message is the Message
It is always dangerous to write an essay about writing, lest one be lampooned for the very deficiencies one seeks to highlight. Such is life. My goal is to focus attention on an important issue.
While continuing to pursue core research in our own discipline of computing, I believe we must also communicate effectively with our peers in the arts and humanities, science and engineering, medicine and public policy. We cannot all be polymaths, but as writers, we can do more to lower the disciplinary drawbridges and invite readers into our intellectual castles.
N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACMblog.
Petascale high-performance computing (HPC) is here, with multiple machines achieving more than ten petaflops on the Linpack (HPL) Top500 benchmark. These achievements have not been without teething problems, as the scale and complexity of the systems have made debugging, acceptance testing and application scaling ever more challenging. Nevertheless, these systems are now operational, and they are being used for scientific research and national security applications.
In many ways, it amazing that a decade ago we celebrated crossing the terascale threshold. When I christened the NSF Distributed Terascale Facility (DTF) as the TeraGrid in 2002, Ian Foster asked me if we should worry about embedding a performance level in a facility name. I responded that I was confident the capabilities and the name would evolve, and they have.
Planning is now underway for exascale computing, though both the depth of the technical challenges and the straitened economics of research funding have slowed progress. For detailed background on the technical challenges, I heartily recommend reading the 2008-2009 DARPA exascale hardware and software studies, chaired by Peter Kogge and Vivek Sarkar, respectively. Although some of the details have changed in the interim, the key findings are still relevant. (In full disclosure, I was one of several co-authors of the exascale software study.)
Among a plethora of design challenges highlighted by these two reports, three are especially relevant when considered with respect to petascale systems:
Substantially reduced memory per floating point operation (i.e., reduced memory per processor core due to energy constraints)
Dramatically higher energy efficiency per floating point operation with minimal data movement, given the high time and energy cost of off-chip data accesses.
Frequent component failures, given the sheer number of chips required to reach the exascale performance target
Just a Few Orders of Magnitude
All currently envisioned exascale systems would require parallelism at unprecedented scale, and barring new, energy efficient memory technologies; they would be memory starved relative to current systems, even under a 20 MW system design point; and multilevel fault tolerance would be required to achieve acceptable systemic mean time to failure (MTBF). Extraordinary parallelism, unprecedented data locality and adaptive resilience: these are daunting architecture, system software and application challenges for exascale computing.
If we have learned anything in sixty years of software and hardware development, it is that orders of magnitude matter, whether in latencies and access times, bandwidths and capacities, software scale and complexity, or level of parallelism. From file system metadata bottlenecks when opening thousands of files to application performance losses from operating system jitter due to daemon activity, every order of magnitude brings new challenges. Only the naïve or inexperienced believe one can scale any computer system design by factors of ten without exposing unexpected issues.
Knowns and Unknowns
What can we expect at exascale? As always, there are the known knowns, the known unknowns and the unknown unknowns, to use a Rumsfeld phrase. The knowns, of both kinds, include the ever-present issues of scale and locality. Will variants of current scheduling and resource management techniques be effective and usable by application developers? Will the complexity of multilevel memory management, heterogeneous multicore and dark silicon shrink the cadre of ultra-high performance application developers even further, perhaps below a technically and politically viable threshold?
The unknowns are more deep and subtle. How can energy optimization be elevated to parity with performance optimization, both statically and dynamically. The dynamic aspect is crucial, as the hysteresis of thermal dissipation in dark silicon affects chip lifetimes. Equally importantly, how can energy usage be related to code in ways that highlight optimization choices? This is the energy analog of performance measurement and guidance for optimized code, where measurements must be related to the original code in ways that are meaningful and amenable to change.
Finally, there are important open questions about the future of operating system structures themselves. The fundamental lesson of cloud computing – the nearest equivalent in scale – is the importance of weak consistency and loose coordination. Given projected exascale communication costs (energy and time), and frequent component failures, might federated rather than synchronized operation be preferable? Is it time to revisit some of our most cherished HPC assumptions and imagine operating system structures and programming models not based on Linux variants, MPI and OpenMP?
Evolution or Revolution
The exascale hardware and software challenges are real. Do we pursue incremental extensions of current practices or step back and explore more radical and fundamental options? Each has different advantages and disadvantages, which suggests we should probably pursue both, recognizing the costs. To be sustainable, an exascale research and development program must lead to cost effective and usable systems that are an integral part of the mainstream of semiconductor and software industries.
N.B.: An abbreviated version of this perspective is scheduled to appear in the Iowa City Press-Citizen. On February 15, 2013, I will be participating in a televised and webcast discussion of personalized medicine as part of the University of Iowa'sWorldCanvass series.
DNA (deoxyribonucleic acid) – it is literally the stuff of life. Three billion instances of four nucleotides (abbreviated GATC) (in the haploid genome) define our humanity, and slight variations across those three billion instances are responsible for all our differences, including our susceptibility and predisposition to diseases. Thus, understanding how DNA regulates biological processes is key to the mechanics of life and to treating disease at its most fundamental levels.
In 2003, after multiple years of painstaking work, two groups, one public and one private, each succeeded in sequencing the DNA of one individual – a human genome – at a cost of roughly three billion dollars. This technological tour de force required collaborations among research laboratories across the country, vast arrays of robotic machines to identify DNA snippets and massive amounts of computing power to assemble the snippets into a complete genome sequence via a technique known as shotgun sequencing.
In the intervening ten years, the cost to sequence a genome has dropped below ten thousand dollars. In other words, for the price of a minivan, you could have your family's DNA sequenced today. More importantly, technological advances will soon push that price below $1000, with $100 sequencing soon to follow. Very soon, having your DNA sequenced is likely to cost less than what most of us spend on gas for our cars each month.
In many ways, the dramatic reductions in DNA sequencing cost are due to advances in some of the same technologies that have given us powerful, yet inexpensive mobile telephones and other electronic devices. Automated DNA sequencers rely on robotics for sample management, advanced computing for coordination and data management, and miniaturization and nanotechnology for biological process and sample analysis.
Beyond the potential for scientific insight, these dramatic declines in DNA sequence costs have been in part due to perceived business and healthcare opportunities. Many companies, including ones created by faculty and students at the University of , see personalized medicine as a new frontier, much in the way that advanced imaging – x-ray computerized tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) – transformed assessment and diagnosis in the 1970s and 1980s. To spur research and innovation, the X Prize Foundation has offered 10 million dollars to the first group to sequence 100 human genomes highly accurately at a cost of $1,000 or less.
Toward Personalized Medicine
What are the implications of inexpensive DNA sequencing for each of us? We can read the letters in each of our personal books of life. However, we do not yet understand fully how those letters collectively define the operating manual for our cells and our bodies, but biomedical research is bringing us closer to commonplace medical treatments.
Today, if you visit your primary care physician, he or she compares your current health to that of a typical human of your age and gender. Therein is the problem. There is no mass production of typical humans; each of us is custom made and slightly different, unique among the roughly seven billion people on this planet. We celebrate those differences, for they define our humanity. In that sense, every child's mother is right when she calls her child special and precious, for we are, in so many wondrous ways.
Biologically, DNA variations and the genes expressed lead to our differing appearance, behavior, physiology and metabolic processes. When combined with our varied lifestyles, environments, exercise patterns and food preferences, it is no surprise that we have different physical reactions to the same drugs and medical treatments. None of us is typical, yet today's medicine treats us as if we are.
When you visit your physician in a few years, to what might he or she compare your current health? Ideally, it would be you at your very best, perhaps at age 25 when you were in peak physical and mental condition, at your optimum weight, and living a healthy lifestyle. More to the point, your physician would then tailor your treatment based on a deep understanding of your unique genetic characteristics, your current condition, physical environment, and your body's particular reactions to those treatments. This is the promise of personalized medicine – earlier and more effective treatment tailored specifically for you.
Our DNA is the personal operating manual that directs our cells and physiology. Understanding that is essential to personalized medicine, but it is not enough. We also need inexpensive and routine diagnostics that can compare the "current you" and the "healthiest possible you" to determine what is wrong.
All of this is analogous to how we now diagnose automobile problems. In addition to inspecting the vehicle, all mechanics read the data captured by the vehicle's onboard monitoring electronics. That data include the vehicle's history of operation and all deviations from the factory-defined standard. While you drive, the vehicle continually monitors itself, raising alarms if there are problems.
Just as the best car repair is the one you never need, the best health care is treatment you never need because you are well. The next best case is early intervention that alerts you and your caregivers before serious issues develop. Late intervention when you are very sick is both damaging to you and expensive for all of us.
As with DNA sequencing, new technologies are bringing the medical diagnostics version of personal monitoring ever closer, allowing each of us to track our physiology and lifestyle. There are already smartphone apps that can measure heart rate and lung function, wearable devices that monitor exercise and sleep functions, and wireless meters for glucose monitoring. Some individuals in the quantified self community are now measuring their bodies in ways that were heretofore only possible in a research environment. Via microfluidics, nanotechnology, robotics, advanced computing and other technologies, the Star Trektricorder is on the horizon.
Societal Implications
Like all new technologies, genetic medicine brings a new set of societal questions. If DNA sequencing uncovers an untreatable genetic defect, do you want to know? It is not a hypothetical question; we are already facing this ethical dilemma for selected diseases. Because you are genetically similar to your siblings, what are the implications for them if you fit a particular disease profile? What is the appropriate ethical and economic balance between personalized health care treatment and cost, particularly if you choose a lifestyle that worsens your health, given a genetic predisposition to a disease? How do we protect individual privacy in a world of "big data" and inexpensive health monitoring devices?
As a comprehensive research university that combines the sciences, engineering and medicine with the liberal arts and humanities, the University of Iowa (UI) brings insights and expertise to all aspects of genetics-based personalized medicine. The UI is a major participant in scientific and biomedical research, as well as the transfer of research ideas into practice via new companies created by its faculty, research staff and students. It is also engaged actively in helping shape the ethical, social, legal and economic frameworks that will govern this transformation.
This exciting new world of personalized medicine is ripe with the promise of improved health for our citizens, by helping our children remain healthy, by allowing our seniors to live independently for longer periods, and by ensuring our citizens in rural areas can monitor their health in detail.
I believe the future is bright. Via personalized medicine, we can improve the quality of life and reduce health care costs for everyone, while respecting and protecting our individuality.
Remember, we are all special. Our DNA tells us so.
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