Reed's Ruminations: A Blog by Dan Reed

Reflections on SC08

Ok, I admit it, what I said in my previous post was wrong. There was singing at SC08. The conference included both a music room where attendees could perform and a music booth where one could lip sync to classic hits. Beyond singing, the conference broke all previous attendance records, with roughly 11,000 attendees, though I doubt singing had anything to do with that!

Clouds and Accelerators

"Cloud" was undoubtedly the buzz word of the conference. Like the word Grid in the past, cloud is now a tabula rasa on which research groups and companies are projecting their own definitions and spins. Somewhere, there's a Dennis Milleresque cultural reference lurking that invokes either Joni Mitchell

I've looked at clouds from both sides now,
From up and down, and still somehow
It's cloud illusions i recall.
I really don't know clouds at all.

or The Rolling Stones

I said, Hey! You! Get off of my cloud
Hey! You! Get off of my cloud
Hey! You! Get off of my cloud
Don't hang around 'cause two's a crowd
On my cloud, baby

In either case, I'm too tired to emit such a pithy aphorism.

On the hardware front, accelerators, notably GPUs, and solid state storage (SSDs) dominated the exhibit floor. NVIDIA was highly visible, and vendors large and small were demonstrating software tools for accelerator programming and for SSDs.

Microsoft News

Microsoft broke into the top ten of the Top500 list of the world's fastest machines, based on execution of the high-performance Linpack (HPL) benchmark atop Windows HPC Server 2008. Like all Top500 runs, this required long hours by a dedicated team of people who pushed the hardware and themselves to the absolute limit. Everyone who has done this, and I remember it well from my NCSA days, knows that this is a caffeine and adrenalin-fueled, sleep deprivation process, wherever you happen to be.

I was also pleased that HPCWire awarded its Editor's Choice Award for best industry/government collaboration to the Microsoft/Intel Universal Parallel Programming Research Center (UPCRC) program, which involves the University of Illinois at Urbana-Champaign and UC-Berkeley. Andrew Chien (Intel) and I are responsible for coordinating this program across the two companies and two universities.

Top500 Perils

First, one of the increasing challenges for HPL and the Top500 is the time required to complete the benchmark run. Given the scale of today's systems, regardless of hardware/software stack, the mean time before failure (MTBF) of these systems is roughly equal to the time to complete the HPL run. This alone makes benchmarking a rather stressful business.

Beyond that, at the time of benchmark runs, the hardware is normally very new, and component infant mortality is still common. Finally, one generally has only a single window to secure the highest position on the list, because new and even larger systems appear regularly. If you miss your target of opportunity for the June or November ranking, your system will slip several positions on the list.

Maybe I am unable to generate a pity aphorism for clouds, but I will close with an allusion to Conrad's Heart of Darkness. Considering the challenges of multicore, exascale, multidisciplinary application software and reliability, one is inclined to remark, "The horror, the horror." We have serious work ahead.

Beyond The Azure Blue

From the first day I arrived at Microsoft, my academic colleagues have been asking me about Microsoft's strategy for cloud computing and when (or if) there would be public announcements. Those questions rose to a crescendo as academic groups prepared responses to the NSF eXtreme Digital (XD) TeraGrid solicitation. All I could say was that we were working on a plan, and it would become clear soon.

I don't normally pitch Microsoft products in the blog, preferring to discuss science policy, technology research and development and global competitiveness. However, something big just happened at Microsoft, something I think will affect all of us. Moreover, as I write this, the Pacific Northwest sky is clear and azure blue, and that doesn't happen often this time of year. An omen, perhaps?

Microsoft Azure Cloud Services

At our Professional Developers Conference (PDC), Microsoft announced Azure, our cloud computing platform, with on-demand compute and storage to host, scale and manage Internet or cloud applications. The press release has additional business perspective and a link to the presentation. Azure is one element of the vision Ray Ozzie (See "Mind to Mind: Building Innovation") described in his 2005 Internet Services Disruption memorandum.

The simplest description of Azure is that the initial release allows you to develop hosted Windows applications using .NET Services, though future releases will support unmanaged code and open source tools as well (Eclipse, Ruby, PHP, and Python). Within Azure, a fabric controller manages application instances and access to storage via SQL Data Services (SDS), and it hosts applications atop virtualized multicore hardware. Finally, Microsoft's Live Services offerings will be layered atop the Azure framework.

You can read the white paper for details on the Azure design and usage approach. In addition, the software development kit (SDK) is available for download. In addition to the Azure SDK itself, there are SDKs for Visual Studio, .NET and SDS Services. Finally, there are Java and Ruby SDKs for .NET Services as well. This is a Community Technology Preview (CTP), meaning Microsoft welcomes feedback on these early capabilities and will continue to expand the capabilities of Azure over the coming months.

Science and Technology Implications

Earlier in the year, I wrote on both my blog and in HPCWire ("Dan's Cloudy Crystal Ball") about the possibility of outsourcing research computing services and infrastructure to the cloud. I noted then that the explosive growth of computing as an enabler of scientific discovery had strained university capabilities and Federal research budgets. Given our current economic crisis, university operating budgets and Federal research expenditures will be under even greater strain and there will be increased scrutiny on the need for each investment.

In a world of (at best) modest research budget increases, we must ask hard questions about the best use of limited funds. Cloud computing offers a potential mechanism to increase the efficiency of current research, ensure continuity of critical data and enable new kinds of research not now feasible.

In this model, researchers focus on the higher levels of the software stack -- applications and innovation, not low-level infrastructure. University and Federal research agency administrators, in turn, procure services from the providers based on capabilities and pricing. Finally, the cloud service providers deliver economies of scale and capabilities driven by a large market base and energy efficient infrastructure. Remember, computing infrastructure exists to enable discovery, not as monuments to technological prowess.

In addition to efficiency, the scalability of cloud services and infrastructure opens new research possibilities. Not only is it possible federate multidisciplinary research data at far larger scales than possible in a university environment (think tens to hundreds of petabytes of low latency storage), we can escape the pernicious cycle of transitory research infrastructure.

How often have we created data repositories as part of research projects, only to find few mechanisms to ensure their long-term sustainability and access by the broader research community? How often have we faced a miasma of distributed data sources with unknown provenance and non-compatible metadata, each supported pro bono on a best effort basis? (See my recent comments on digital document preservation.) Instead, imagine multidisciplinary data fusion and mining, where students can pose queries against integrated but diverse data sources using robust tools?

Finally, by leveraging "pay as you go" models, we can trade time and scale on a continuous basis. Imagine applying 50,000 processors for one hour at the same cost as 50 processors for one thousand hours. In the cloud, the integral under the curve is the same and the costs are comparable, but the research effects are qualitatively different.

The Standard Questions

The standard questions always arise about new approaches to computing. Cloud services and data storage inevitably raise the standard ones.

• Is it reliable and will my data persist?
• Is it safe, private and secure?
• Will I be captured and become captive?
• What does it cost and what if I can't continue paying?

We tend to forget that there are complementary issues about local infrastructure because we have already internalized and accepted the implications and risks. Moreover, local failures are rarely publicized.

• What happens if my disks crash?
• What if I can't pay for backups or maintenance or physical plant or …?
• What if my network is penetrated?

These are the standard cost/benefit/risk tradeoffs. One must make them based on statistics, economics and practical constraints. Remember that we debated the same issues when we shifted research computing from vendor-backed HPC designs to predominantly commodity components.

Let's Reason Together

I welcome discussion of how we can exploit cloud services and infrastructure effectively – all cloud infrastructure, not just Microsoft's Azure. To do this, the cloud service providers, hardware vendors, universities and Federal government must work together to outline an agenda, conduct experiments at scale and speak with a united voice on the opportunities.

It's a sunny day, but my head is in the clouds.

Showing Up and Two Corollaries

"Eighty to ninety percent of life is showing up." The line has been variously attributed to Yogi Berra, Woody Allen or even an anonymous wag. It's wise, though obvious advice – showing up and doing the expected generally allows one to avoid a host of problems. Appearing for jury duty avoids one being held in contempt of court, and you can't fly if you don't show up at the airport on time.  I was reflecting on the implications of "showing up" while at a recent meeting in Italy.

Show Up and See What Happens

My friend, Dave Turek, IBM's Vice President for Deep Computing, once explained IBM's open source and Linux strategy by saying that IBM had a deeply considered, two phase strategy for Linux and clusters for HPC, "Show up and see what happens." As he once remarked at an NCSA Private Sector Partners (PSP) meeting, "We've showed up. Now, we are waiting to see what happens."

At NCSA, we partnered with IBM in 2001 to deploy two of the first large-scale commodity clusters for open scientific use: two 1 teraflop systems based on Intel Pentium III and Itanium processors. At the time, this was a radical, almost heretical idea – deploying commodity PC clusters as production HPC platforms. Of course, such commodity clusters now dominate the Top500 list.

In a reprise of this experience, Microsoft and NCSA recently partnered to deploy Windows HPC Cluster 2008 on the latest incarnation of commodity cluster hardware. (The customer story has the technical details). I don't generally evangelize for Microsoft products in this blog, but I was very impressed that Windows HPC Cluster achieved substantially higher performance on the same hardware than did Linux. Microsoft, in the form of Kyril Faenov's HPC team, has definitely "showed up" in this space in a big way, and I think there are great opportunities to offer not only Windows compute clusters but also backend acceleration for desktop applications. Of course, all of this is ultimately connected to the ferment in cloud computing.

Avoid the Obviously Wrong

At the recent Cetraro meeting on High-Performance Computing and Grids, Miron Livny extended the "show up and see what happens" maxim by offering a corollary, "Show up and avoid doing something stupid." His observation was that evolutionarily, human success was defined by avoiding being trampled by a woolly mammoth, eaten by a hungry Bengal tiger or falling into a crevasse.

The computing implication of Livny's corollary is that one should do reasonable things when presented with opportunities. In terms of research infrastructure, this means avoiding our academic tendency to delight in second system syndrome – building complex systems that embody all of our personally favorite features without determining if they are either needed or useful.

At Cetraro, we debated the impact of the multicore revolution, the similarities and differences between Grids and clouds, and the commonalities between future exascale systems and the architecture of megascale data centers. (By the way, if you have not read the Department of Energy's exascale computing study, I highly recommend it.)

There are deep technical challenges in all of these areas. However, we must avoid being trampled by the woolly mammoths; this domain is fraught with academic, government and industrial politics. I believe we need a wider dynamic range (time horizon, risk/reward and fiscal scale) of research and development projects if we are to solve these problems.

I have made this point many times, most recently as part of the PCAST report on the U.S. NITRD program. I am scheduled to testify about this again to the House Science and Technology Committee on July 31. I will report on the hearing in August.

Do Simple Things Quickly

At the same Cetraro meeting, I opined that there was a second corollary, "Do the obvious, simple things quickly." I think this is the key lesson to be drawn from web2.0 mashups, and the rapid evolution of commercial clouds. The simplicity of the APIs and hosted infrastructure encourages external groups to innovate rapidly. We have seen the clear evidence of this in the explosive growth in social networking sites and in the hosted services that have appeared.

By contrast, I think this is one of the places we have struggled with academic Grids. The software has often been too complex, and this complexity has been engendered by the distributed nature of the participating organizations, requiring "glue code" to integrate disparate policies and infrastructure across virtual organizations. In contrast, mashups and cloud services can be deployed quickly (by academic standards) using very simple APIs and service level agreements (SLAs). It will be interesting to see how the Grid/Cloud mashup evolves.

Salishan, Exascale and Heterogeneous Multicore

Update: The proccedings from the meeting are now online.

Every year, the three U.S. Department of Energy (DOE) weapons laboratories (LANL, LLNL and SNL) organize a workshop on the state of high-performance computing and computational science. By long tradition, the meeting is held at the Salishan lodge on the Oregon coast. The attendees are drawn from the three weapons laboratories, the DOE Office of Science laboratories, other computing-intensive elements of the U.S. government (e.g., the NSA and DoD HPC Modernization Program), the NSF supercomputing centers, key academic researchers and industry high-performance computing leaders. I've been attending for many years, both as the former NCSA director and as a federal science policy wonk.

Parallelism, Multicore and Academic Partnership

Today, Microsoft and Intel jointly announced the creation of two Universal Parallel Computing Research Centers (UPCRC): one at the University of California at Berkeley (UC-Berkeley) and a second at the University of Illinois at Urbana-Champaign (UIUC). The two university press releases (Illinois and UC-Berkeley) are also posted. As Tony Hey said during the announcement, "Driven by the unprecedented capability of multicore processors, we're in the midst of a revolution in the computing industry, which profoundly affects the way we develop software."