Reed's Ruminations: A Blog by Dan Reed

The Ghosts of Holiday Shopping: Past, Present and Future

N.B.  This article also appeared on the Communications of the ACM (CACM) blog. I will be off for the holidays.

In many parts of the western world, the frenzy of holiday shopping has reached its crescendo. In the U.S., it began with Black Friday, the day immediately after U.S. Thanksgiving. Not only is holiday shopping a major component of retail profits, financial analysts also use such consumer spending as a barometer of possible economic recovery. With apologies to Charles Dickens, it is instructive to consider the ghosts of shopping past, present and future, albeit with a bit of U.S.-centric perspective.

Past

Those of a certain age (read, older than 40) remember when catalog sales and retail stores defined the spectrum of shopping options, and the traditional media (newspapers, magazines, radio and television) carried the advertisements of sales and loss leaders. Other than a few national chains (e.g., Sears), retail stores were largely independently owned and operated, supported by a wholesale distribution network.

As a boy in a small town, I distinctly remember awaiting the annual delivery of the Sears Christmas mail order Wish Book, via which one's parents could purchase holiday gifts via mail order. Long before the ubiquitous UPS and FedEx trucks traveled the highways, multiple weeks could ensue from purchase to delivery.

From an information theoretic and network perspective, the system was much more loosely coupled. Consumers and retailers operated primarily with local availability and pricing data, coupled to the larger national and international economy via logistics and supply chain costs. The price of a toy could vary substantially across town and certainly across states and regions. Soon, all of that would change.

The dot.com bubble brought grand visions of reinventing retail commerce. Pure plays and a "get big fast" mentality prized growth over profits, with massive advertising campaigns for online orders. (Who can forget the pet food sock puppet and the Super Bowl advertisements?) Before the bubble collapsed, the frenzy stimulated a massive build out of dark fiber, which ultimately helped enable greater broadband penetration. From the rubble of the dot.com collapse, a set of viable business models did emerge, ones that leveraged the long tail and delivery of small, relatively high value items.

Present

Today, big box stores are common, supported by supply change management and point-of-sale data mining. Consumers commonly compare product characteristics and prices online and (increasingly) negotiate prices onsite, based on near real-time data from social networks and smartphones. In the electronic equivalent, we now have Cyber Black Friday and Cyber Monday, with many electronic retailers offering online promotions. Advertising is increasingly dominated by ad sales associated with web search engines, and ad placement can be the differentiator between profit and loss.

Although I felt some trepidation when I first entered a credit card number on a web site, particularly one I had found via a web search, I am now as likely to shop electronically as I am to frequent a physical store. Indeed, for many items, electronic shopping is my preferred method, as it saves my time. This is a profound sociological change, enabled by inexpensive computing, broadband networks, rapid transport, globalization and information flow. However, security and privacy remain important concerns, with both social and technological components.

Future

As Yogi Berra once remarked, "It's tough to make predictions, especially about the future." I suspect, however, that the distinction between the physical and virtual worlds will continue to blur as the cost of information transfer continues to decline. Business competition between electronic retailers and big box stores is increasing, with each competing on the other's territory. Augmented reality and intelligent devices will allow most customers to comparison shop and purchase simply by photographing objects. Finally, for information and entertainment, direct electronic delivery is likely to become increasingly predominant, with concomitant implications for physical manifestations of that information.

All of these are, of course, simply guesses. As they say in sports, even with rankings, the teams play the games to see what really happens. So it is with the future. We can predict, but reality is the ultimate validator.

Serial Dismay, Parallel Excitement

Multicolored lights are one of the iconic and ubiquitous manifestations of the end of year holidays. They also conjure festive memories of my childhood, albeit with one major exception. That rather less than pleasant memory involved much consternation, veiled (and not so veiled) mutterings and ritual questioning of all things derived from Maxwell and Edison.

I speak, without hesitation or ambiguity, regarding the curse (sometimes literal) of serially wired holiday lights. My father and mother kept a healthy supply of spare bulbs for laborious and sequential replacement and testing whenever a single bulb failed, and a strand of thirty bulbs went dark. I still remember my excitement and delight when we first purchased strands with parallel circuit wiring. What a difference it made, when a bulb failure dimmed only one light, rather than an entire strand. As a child, I quickly gained a visceral appreciation for Kirchhoff's laws, long before I ever knew the name Gustav Kirchhoff.

Systemic Resilience

If there is an Aesop-like moral in this tale from my childhood, it relates to systemic design for resilience rather than component resilience alone. Parallel circuit resilience trumps serial circuit resilience, and the extra cost is repaid in greater systemic reliability. Alas, I fear we have not learned this lesson in parallel computer system design and parallel programming models and applications.

The standard domain decomposition models commonly used to solve the partial differential equations that underlie so much of computational science and that are embodied in parallel MPI implementations of the solvers, implicitly presume that all of the parallel processes (tasks) and the supporting hardware operate without error or failure. Periodic checkpointing is our primary concession to failure and the vagaries of job scheduling.

This is analogous to trusting that the series circuit holiday lights will not fail until we turn them off each evening. It can be effective for small numbers of lights (processes) but increasingly problematic for larger systems. All this suggests we need to rethink our models of hardware and software resilience, and embrace redundancy as the necessary cost of systemic reliability, particularly in the trans-petascale and exascale regimes. It may also mean that we can never use all of the hardware for non-redundant computation. After all, would one rather fail quickly at exascale rates or compute reliably at sustained trans-petascale rates?

Changing the Game

Cloud services now operate on the largest computing systems we have ever built on this planet, with service reliability expectations far higher than what we demand from scientific applications. Thus, I also believe there are lessons from cloud computing that are potentially applicable to computational science applications. Arguably the most important is exploring how to exploit eventual consistency rather than sequential consistency.

To understand this potential shift in perspective, I heartily recommend Werner Vogels' analysis of the power of eventual consistency for large-scale web services at Amazon. Eric Brewer's thoughts on the CAP theorem, drawn from his Inktomi experiences, have also shaped theoretical and empirical assessments of large-scale system reliability. For those not familiar with the CAP theorem, it postulates that one can choose any two of Consistency, Availability or Partition tolerance. More generally, it offers a framework to reason about conflicting objectives.

Lest everyone shudder in numerical and scientific horror at this prospect, remember that parallel, numerical applications are themselves samples drawn from the space of "possible answers." They are not "the answer," and they may not even be an unbiased sample. (See On Getting the "Right" Answer.) Are these weak consistency approaches transferrable to scientific applications? I do not know. I am only sure that we need to find out – quickly. If successful, this would be a profound change, with deep implications for computational science algorithms, software and application reliability.

The World Is Small

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

As I write this, I am flying to a business meeting in Asia. In turn, this caused to me reflect on the changing nature of information flow, global business and 24x7 accessibility.

There was a time, not that many years ago, when international travel meant one was largely inaccessible to colleagues at home, wherever home might be. Yes, you could send a telegram or place a telephone call, but both were expensive and often required foresight and planning. Similarly, news from home came via the International Herald-Tribune, radio and newswires, or film that had been airlifted to a television network.

Slowly, we began entering the Internet age. I distinctly remember attending an international research conference on high-performance computing where all the attendees stood in line for email access on the single, Internet-connected terminal. The international bandwidth was so low that I could type faster than the characters could be echoed. Needless to say, one only scanned messages for deadline-driven events.

Today, when my plane lands, I will turn on my smart phone, connect to the local GSM network and download queued messages. By the time I am ensconced in a taxi to my hotel, I will be responding to electronic queries. At the international hotel, I will connect my laptop to the local wireless network and work as productively as I would in my office or at home. Other than the delay due to time zone separation, most of my colleagues will not realize that I am traveling, and the inevitable late night wakefulness due to jet lag will ameliorate even that delay.

Inexpensive, global communication now allows distributed, international groups to work collaboratively and productively, with little regard to location. Computing research – in networks and protocols, servers and storage, devices and user interfaces – made all of this possible. The world may or may not be as flat as Thomas Friedman suggested, but it is certainly small.

Now if I could just catch up on my email deluge.

Language Shapes Behavior: Our Poor Cousin, Data

The Sapir-Whorf Hypothesis holds that language and culture shape behavior and thought. The idea has a long, rich and sometimes controversial history, though recent cognitive linguistic research, together with brain imaging, has lent it new credence. Lately, I have been reflecting on the nature of linguistic relativity in scientific research, particularly for HPC and cloud computing. In particular, how has our focus on floating point operations per second (FLOPS) shaped discourse, science politics and outcomes?

Look, It's Terascale!

If I were to remark, "That is a terascale system," you would likely interpret my comment as an assessment of the system's computing performance, more specifically its floating point capabilities. For the cognoscenti, the sentence likely engenders visions of multicore systems with out of order instruction issue and deep pipelining, small clusters with fast interconnects and (perhaps) some accelerators such as GPUs. You might even reflect on the historical evolution of high-performance computing, when a terascale system was ranked first on the Top500 list about a decade ago.

Given my remark, you would be unlikely to eye your desktop computer with its terascale (terabyte) commodity disk drive. Yet, based on its storage capacity, that desktop is just as surely a terascale platform as is the compute cluster. Ah, you say, I have mixed capacity (bytes) with capability (operations). Terabyte per second storage systems are rare, you say. Absolutely true, yet as this bit of third rate linguistic legerdemain illustrates, language and, perhaps more importantly, connotation really does trump denotation in our discourse.

Whether Petascale and Exascale?

Today, the world's fastest computing platforms have broken the one petaflop mark on the high-performance Linpack (HPL) benchmark, and planning groups across the United States, Europe and Asia are debating political and technical approaches for the construction of exascale computing systems. I cannot help but wonder, though, why we in technical computing talk so little about petascale and exascale data platforms? Why is there no international initiative to build a low latency, high bandwidth (many terabytes/second to even a petabyte/second) data analysis engine with multiple exabytes of storage capacity?

I suspect the reason lies in our background and linguistic heritage. Most of us in high-performance computing came from mathematical backgrounds, where success was defined by a proof and an equation and their embodiment in a computational model, rather than by insight gleaned from large-scale data analysis. I believe it is time we found a lingua franca that bridges FLOPS and bytes and reclaim the full connotation of exascale.

I Want My Exabytes

It is worth remembering that the explosive growth of observational data is itself largely a product of inexpensive CMOS sensors, based on the same semiconductor technologies that begot microprocessors. The examples of high resolution instruments are legion, from the Large Hadron Collider (LHC) and its international hierarchy of data archives to the proposed Square Kilometer Array (SKA) radio telescope, which may produce as much as an exabyte of data every few days. This data tsunami is not limited to the physical sciences; the biological and social sciences are being inundated as well.

One of the major lessons from web search and cloud data centers is the power of truly massive scale, near real-time data analysis. When anyone with a cheap cell phone and a web browser can extract data and insights from a non-trivial fraction of the human knowledge base, behavior and culture are transformed. I would like to believe that we can bring the same data-driven analytics to scientific research as are routinely exploited to find a good lasagna recipe. We need a balanced exascale initiative, in every sense of the word, lest we be a technical confirmation of the Sapir-Whorf hypothesis.

Twitter Is Three

Yesterday (March 21, 2009), Twitter, the microblogging service, turned three years old. I've been twittering for two years now, watching the evolution of social networking and the nature of the participants. (Yeah, I had a Blackberry back in 2000-2001, long mobile email became an international addiction, but then I'm a gadget geek.) Twitter is now growing exponentially according to Nielsen Wire, with unique visitors up over 1000 percent since one year ago.

Twitter would not have been possible in the U.S. a decade ago, as we lacked the ubiquitous smartphones and broadband coverage needed for mobile access. However, if one recalls the popularity of the short message service (SMS) in Europe and Asia, Twitter could easily have appeared as a value-added service long ago. After all, GSM has been around since the 1980s. It remains an open question, however, if Twitter can be a profitable business.

Extended Friends

Like its big brother, Facebook, Twitter has gone mainstream, with attributions and social discussions in the popular press. Indeed, some have argued that Twitter is the new Facebook, favored by the digerati. All of this reminds me of Yogi Berra's famous remark about a restaurant, "Nobody goes there anymore; it's too crowded."

In both the Facebook and Twitter worlds, it does seem crowded. I am now being "friended" by some people I barely know, and by others I do not know at all. (I am sure many of you have had the same experience.) I am not complaining; rather I have realized that friend in this context is a very elastic thing, ranging from long-time personal friend to an extended network of business acquaintances. Who would have thought one would need information visualization tools like Friend Wheel to track one's "friends"?

Interacting Networks

Like web search engines and the social insights one can glean from tracking queries (e.g., Google's tracking of flu trends), analyzing and visualizing tweets can illuminate social dynamics and behavior. Watching Twittervision, Twitter StreamGraphs or TwittEarth can at times be fascinating but can also overwhelm one with the minutia of daily life. (A trip to the gas station is not an event of historic proportions.) Such is the nature of a global social dynamic.

I also find it interesting that these extended social networking sites are themselves increasingly interconnected. For example, my Twitter updates appear on my blog and my Facebook page. Conversely, excerpts and links from my blog posts appear on Twitter via TwitterFeed and are also replicated on Facebook, LinkedIn and FriendFeed. In between, there's microblogging (a tumblelog) with Tumblr – hpcdan.tumblr.com).

Perhaps in the limiting case, all of my social networks can simply chat about me among themselves. They are probably more interesting than I am.