A half-decade after Herb Sutter wrote that the “free lunch” of Moore’s Law is over, he’s back with his prophet’s wisdom about where we’re going in his January Dr. Dobbs article, “Welcome to the Jungle”. I’ll give you a moment to decide whether to get the Guns N’ Roses song out of your head or use it as a backdrop for this juicy quotation:

If hardware designers merely use Moore’s Law to deliver more big fat cores, on-device hardware parallelism will stay in double digits for the next decade, which is very roughly when Moore’s Law is due to sputter, give or take about a half decade. If hardware follows Niagara’s and MIC’s lead to go back to simpler cores, we’ll see a one-time jump and then stay in triple digits. If we all learn to leverage GPUs, we already have 1,500-way parallelism in modern graphics cards (I’ll say “cores” for convenience, though that word means something a little different on GPUs) and likely reach five digits in the decade timeframe.

But all of that is eclipsed by the scalability of the cloud, whose growth line is already steeper than Moore’s Law because we’re better at quickly deploying and using cost-effective networked machines than we’ve been at quickly jam-packing and harnessing cost-effective transistors. It’s hard to get data on the current largest cloud deployments because many projects are private, but the largest documented public cloud apps (which don’t use GPUs) are already harnessing over 30,000 cores for a single computation. I wouldn’t be surprised if some projects are exceeding 100,000 cores today. And that’s general-purpose cores; if you add GPU-capable nodes to the mix, add two more zeroes.

The big takeaway for software engineers like me is that we’d best be learning how to develop solutions using the emerging APIs so that we can harness all of those extra orders of magnitude of scalability. That involves figuring out how to . . .

• Deal with the processor axis’ lower section [of Sutter's chart] by supporting compute cores with different performance (big/fast, slow/small).
• Deal with the processor axis’ upper section by supporting language subsets, to allow for cores with different capabilities including that not all fully support mainstream language features.
• Deal with the memory axis for computation, by providing distributed algorithms that can scale not just locally but also across a compute cloud.
• Deal with the memory axis for data, by providing distributed data containers, which can be spread across many nodes.
• Enable a unified programming model that can handle the entire [memory/locality/processor] chart with the same source code.

Perhaps our most difficult mental adjustment, however, will be to learn to think of the cloud as part of the mainstream machine — to view all these local and non-local cores as being equally part of the target machine that executes our application, where the network is just another bus that connects us to more cores. That is, in a few years we will write code for mainstream machines assuming that they have million-way parallelism, of which only thousand-way parallelism is guaranteed to always be available (when out of WiFi range). . . .

If you haven’t done so already, now is the time to take a hard look at the design of your applications, determine what existing features — or better still, what potential and currently unimaginable demanding new features — are CPU-sensitive now or are likely to become so soon, and identify how those places could benefit from local and distributed parallelism. Now is also the time for you and your team to grok the requirements, pitfalls, styles, and idioms of hetero-parallel (e.g., GPGPU) and cloud programming (e.g., Amazon Web Services, Microsoft Azure, Google App Engine).

p.s. — I can’t believe that it’s been almost four years since I took a course with Herb out in Washington. That was some hard-core learnin’.

(From How to Think about Parallel Programming: Not!)

• When optimizing code for GPU, FPGA, or CPU, definitely focus on pipelining and overall throughput, not just local optimizations.
• There’s a trade-off between “faster” and “sooner.” It’s not always worth saving a few seconds (or even a few minutes) if the kernels take hours or days to compile. (Then again, sometimes it is.)
• Try to reduce dependence on the language/compiler “stack” that removes inefficiencies. The optimizer does good work, but you can do things to help it. Think about the hardware or architecture format. It’s not a sin to reduce the amount of abstraction in the service of performance. Pay attention to things that affect processor pipelining and cache movement.
• BTW, some languages and technologies exist to provide higher level programming that’s close to the hardware, but they’re proprietary, secret, or still in R&D.
• Use algorithmic optimization techniques. Step back and find the shortest-time computation.
• Avoid using if statements. The goto construct is considered harmful, but if is basically the same thing. Instead think about state machines and polymorphism. There’s no branch-prediction penalty to pay, since the system “just is” in the state it’s supposed to be in. The logic is clearer, because there are no switches, making it easier to test, too.
• Don’t always assume that floating-point values are necessary. Integers can often be creatively used and are far faster for math than double-precision numbers.
• Of course, there’s a compromise between speedy/efficient and readable/maintainable.
• Aim to structure programs as “symbolic intent.” Mathematical descriptions are bad ways of expressing programs. Think about functional programming models instead of procedural.

If you want to know more, you should definitely watch the half-hour interview. And if your reaction was more along the lines of “Yes, yes; that’s all true, and it’s how I design my image processing code,” then I definitely hope you’ll consider applying for the GPU/multicore engineering position we have open.

This is another one of those posts. Every year between Christmas and New Years Day, I try to use the quiet week to get stuff done and tie up loose ends. Last year, I cleared out a bunch of notes. This year, I’m looking at presentations and slides from the QCon SF 2011 conference (wrap-up). Its focus on software architecture and project management is about 75% of my job, so many of the presentations seemed tailor-made for me. Here’s some of what I learned.

Erik Doernenburg. “Software Quality: You Know It When You See It” has a really good slide deck that got me thinking about some projects I might want to set up. It’s full of practical, usable suggestions:

• View the code at the 1,000 view, rather than ground-level or 30,000 feet.
• Look at the test-to-code ratio, not just code coverage.
• Graph the change of metrics between versions and revisions, compare across different parts of the code, and look at them relative to industry standards.
• Measure the “toxicity” of code by rolling up various quality metrics about a bunch of modules into stacked bar charts.

We should pose these questions during design and code reviews:

• Is the software/change of value to its users?
• How appropriate is the design?
• How easy is the code/design to understand and extend?
• How maintainable is the software?

It was full of some really great links to things like Metrics tree maps (a.k.a., pretty heatmaps for source code) as well as a few tools: SourceMonitor, iPlasma, and using Moose to visualize quality.

Joshua Kerievsky. “Refactoring to Patterns” — some notes:

• Refactoring is like algebra’s equivalence-preserving manipulations. “Design patterns are the word problems of the programming world; refactoring is its algebra.”
• Understanding the refactoring thought process is more important than remembering individual techniques or tool support.
• Code smells have multiple refactoring options and often benefit from composite refactorings.
• Look for automatable refactorings first. Consider changing the client of smelly code before the smelly code itself.

Guilherme Silveira. “How To Stop Writing Next Year’s Unsustainable Piece Of Code” was pithy and thought-provoking.

• There is no value for architecture or design without implementation. That’s just interpretation of the software.
• “New language. New mindset. new idiomatic usage. Same mistakes.”
• Complexity and composition are natural and good, but if they’re invisible, they’re evil.
• Start with a mess and refactor right away. Starting “right” is hard (and misguided thinking). Refactor for better, not just prettier.
• Make complexity easier to understand and see.
• Hiding complexity in concision hurts testability, since no one knows the complexity is there. Furthermore, if it’s hard to test, it’s also hard to use correctly.
• “Model rules. Do not model models.”

Michael Feathers. “Software Naturalism: Embracing The Real Behind The Ideal” is a presentation that I would like to see/hear, since the slides seemed full of information but weren’t self-explanatory. Here are two things I could glean: 80% of software defects in large projects were in 20% of the files. In general, the more churn in a file, the more complex it tends to be.

Panel: “Objects on Trial” was perhaps the most unusual presentation, since it was a mock-trial. I use objects all the time . . . some of them are good . . . some demonstrably so. Even so, I never latched onto the idea of object-oriented (OO) design versus objects as types. The four panelists, in one way or another, basically said, “That’s the problem.”

One of the panelists drew an extended analogy between the space program and OO. The space shuttle (which we all love) was fixated on reuse but basically was a waste of heavy lifting; people don’t reuse the right stuff. In software, object reuse is largely accomplished by cut-and-paste copying of boilerplate code that does close to what you want. Of course, the panelist acknowledged that we do reuse the ideas in OO via design patterns, and no one seems to have much of a problem with that. Ironically, having a rich pattern language means that software engineers are in a better place than ever before to use objects correctly.

A key problem with our approach to objects is that we’ve failed (generally in software engineering) to handle complexity well, which was supposed to be the point of OO design. A conflation of beauty and OO design makes things worse. Internally, software is ugly, and beauty shouldn’t be a goal. Making a fetish of beauty makes code inflexible because people don’t want to extend the beautiful thing that works.

For other panelists, objects weren’t the problem at all. For them it’s static typing in “OO languages,” such as C++, Java, and C#. We’re at a place now where all of the good things about OO have been lost in an attempt to make OO languages as fast as C. This runs counter to the goal of having “ordinary,” understandable code. Generic programming using strongly typed (possibly template heavy) languages just makes everything complicated.

For me, it’s moot. C++ is what I use, and I don’t have a large proprietary object system that I can tap into for reuse. I’m in the camp that uses C++ objects to generate new types for data hiding and aggregation, as well as (to a lesser extent) reuse. But some of these types are generic, template classes that are hard to understand. I plead “no contest.”

• Ultramarathon — The always hilarious author of The Oatmeal describes his long-ass run.
• Inbox 10,000 — E-mail sucks . . . and is mostly worthless. Am I right?
• The Joy of Cycling — A poster.
• Getting Bin Laden — What happened that night in Abbottabad
• The unavoidable power of compromise — The issue of raising the debt ceiling may no longer be a hot topic, but Scott Berkun’s take on the value of compromise is evergreen.
• The Growing User and the Perennial Beginner — A mental model for anyone who creates software interfaces for people of different competency levels.
• Yeah. We know. — You hate needles. We do, too.
• A pair of articles from Victoria Cumbow. In “It’s Political, and It’s Personal” she hopes you’ll encourage President Obama to attend the UN summit on noncommunicable diseases; and “A Gentle Reminder” reminds us why we need to advocate for better education and attitudes for health care professionals. (This kind of experience is why I stopped answering the phone when my insurance company’s nurses called. Bad me.)
• O Is For Outrage — Go sign the petition to President Obama, please.
• Kelly Kunik says, “I’m Not Afraid Of My Pump Being Hacked, Because Like You, I Am The Only User On My Personal Pump Network.”
• Five Ways To Solve The Diabetes Crisis With Data-Driven Design — Just FYI, Fast Company, “Solve” might be over-promising just a wee bit.
• Semifinalists in “Data Design Diabetes Challenge” tackle a major health issue in ingenious ways — The iMedicalApps people have the tone a little better.
• It’s Broken — By “It” many of us mean “the entire system around software patents.”

First off, I’m not surprised. Like any device that transmits and receives wirelessly, the signals from pumps and CGMs are interceptable. Furthermore, like any other device that communicates with limited access control—you just need to know (or sniff out or be able to guess) the six or seven digit code that’s used to connect with another device—they’re essentially open. From there it’s all just figuring out the protocols and the format of the data as it’s passed around. As someone who spent about ten years working with and occasionally reverse-engineering formats, I can tell you, it’s all just a matter of trial and error and careful observation. (If I were a hacker, my handle would be “gluX0se.”)

So, in a world where relatively few people have these medical devices—unlike, say, mobile phones or bluetooth devices—the device manufacturers essentially did the easy thing, which was to assume we use our medical devices in a trustable world where people don’t mess with medical devices. (BTW, who knew there was a free Vulnerability Management for Dummies e-book?)

There’s been a lot of unease in the community about the way that the information was presented to the press and the way that some outlets sensationalized it (e.g., “Black Hat: Lethal Hack and wireless attack on insulin pumps to kill people”). It’s hard not to agree with a lot of the criticism there. But I can’t criticize looking for security holes in medical devices. Nor can I fault the impulse to hack into own’s own medical device—even one that keeps people alive—or to help other people hack their devices. Not all hacking is scary villainy, but this incident certainly exposes some problems.

Using the AP to share this information leaves a bad taste in my mouth, but presenting the findings at the Black Hat Conference seems like the most appropriate way to publicly disclose this research. (And it is, in my mind, legitimate personal security research that should be shared openly.) I would have preferred that Radcliffe work more closely with the device manufacturers leading up to the announcement. (I’m assuming that he did not.)

On the other hand, just presenting the findings to the device manufacturers—as some would have liked—violates the hacker ethos, both the black hat and white hat versions. Part of hacking—the part that I can get down with—is when motivated hobbyists exploit technology to solve a problem (real or imagined). I have thought many times how great it would be to sniff the unprotected data that’s transmitted by my pump/CGM and skip the middleman of uploading data to a web site. I’ve even gone so far as to seek out the information that Radcliffe presented, but it wasn’t available at the time.

Device manufacturers limit our access to our own medical data and tightly control the way that we can interact with our devices. It’s understandable given the limitations put on them by the FDA, their own desire to help (not harm) customers/patients, and their lawyers’ desire to limit risk exposure. It does mean, though, that the enormous potential for third-party, patient-focused tools goes untapped. Those tools could benefit so much from being able to present data the way that their users want to see them: A dashboard light in a car, a desktop computer widget that display CGM values, a mobile app that records all of the data for later use, a device that calls parents of children with diabetes when something happens, an awesome mood ring displaying BG, etc.

I suspect (and once again I’m assuming here) that Radcliffe was intrigued by the rather obvious possibilities of unprotected communication, and that’s getting lost in the whole “malicious people ruining diabetics’ lives” reporting. I fear the notoriety this incident is garnering is going to scare manufacturers into closing exploitable security holes without providing a secure, replacement method for getting at all of that data. And that’s a shame.

Since last Saturday I’ve learned a lot. I’ve picked up the syntax of Objective-C, which is causing this C++ programmer to “think differently.” I like what I’ve seen from it so far, but we’ll see what I think after building something real. I’ve made a couple “Hello, World!” applications, just enough to get a few basic skills using Xcode and Interface Builder.

Now the hard work begins.

I’ve made a list of requirements for the first couple of (internal) versions of the app; so I know what needs I plan to satisfy. I’ve picked an external library to plot the CGM data. And I’ve started working on the functional design, sketching a few different views that people will use to interact with their data. (I usually hate graphical user interface design, but something about the UIKit components seem to be amplifying my scanty abilities with interaction design.) I still have to figure out the data model — that is to say, the architecture — but I think that should follow from the views I create, which of course is supposed to visualize items in the pump/CGM data model.

Tomorrow I’ll try to put a few components together. Stay tuned!

I’ve also been thinking thinking about the apps I have now that I find useful for diabetes. To be perfectly honest, none of them are the typical journaling apps. I downloaded a few, used a couple, but stuck with none. Journaling just takes too much time and/or sustained attention. You either stick with it and get a lot of use out of it, or you have a life. I’m trying not to make that same mistake with my own app. Let the machines collect the data, I say. We’ll use our brains to make decisions based on their hard work. Thinking, that’s our proper role.

Anyway, what apps do I have on my iPod Touch (a.k.a., cheapo iPhone) that help me with my diabetes?

• Tap & Track: Calorie Tracker ($3.99) — It has a ridiculous amount of foods in its onboard database, including loads of restaurants and name-brand packaged foods. No network connection required. Easily access things you eat regularly. Get details about your own recipes. A bargain at twice the price.
• Twitter (Free) — Gotta keep in touch with my diabetes online community.
• Things ($9.99) — This GTD app is a bit pricey, but it’s the best I’ve found. Among other things, I use it to keep track of my diabetes and health-related projects and tasks. I can set it to remind me to get my A1c drawn months in the future, when it’s actually relevant. It reminded me to “write that weblog entry about iPod apps” before DSMA. Oh, and since I made a recurring task to take my vitamins, I’ve gotten much better at doing that. I like checking things off a To-Do list. :^)
• Due ($2.99) — I try to bolus for breakie 15 minutes before I eat. This little reminder app has reminded me more than once that it’s finally time to stop working and go eat that cereal.
• Reeder ($2.99) — This RSS aggregator hooks up to Google Reader and helps me keep up to date with all y’all’s weblogs. (Thanks to Pearlsa for recommending this.)
• iBooks (Free) — I keep my running plan that I built on a running web site in a PDF here, along with a few exercisey things that I scanned from magazines. (What I really want is a tear-sheet app, but until then . . .)

So what am I missing? What do you use? Please leave a comment!

It wasn’t an awful experience. It did help keep me posting photographs and whatnot, but it doesn’t seem that I set aside enough time to write some of the longer articles that I hoped to create. In fact, I often found myself remembering right at the end of the day that I needed to write something. Now that the month is over, I’ll keep going with the occasional posting — with more substantial content, I hope.

See you back here real soon.

. . . I guess there’s no time like the present to write a bit more and say that I got an iPod Touch for an early Christmas present. (Thanks, Mom and Miles!) I like it very much as a media device — navigating playlists and moving around the interface is just much more awesome than my old 6th generation “classic” iPod — and I’m starting to look around at some of the apps for it.

Let’s see here, there’s Twitter and Facebook and the New York Times reader and Netflix and Skype and the ESPN app which I’m sure will be useful once baseball season starts again. (It has rugby, but no Australian scores. Go Rabbitohs!) And I’ve just started looking for things that could help me out with diabetes and exercise.

Here’s where I need your advice.

What are your favorite iPhone / iPod Touch apps? What do you use to help you manage your diabetes? What should I download just for fun or because no properly decked out Apple gizmo is complete without it?