Revenge of the SMP?

By Michael Feldman

April 27, 2007

Lately it seems like I've been talking with people who see the multicore phenomenon as something of a cluster-buster. One of those people is Mike Hoskins, CTO of Pervasive Software, a company that develops database software technologies. Hoskins' reading of the tea leaves suggests that the trajectory of multicore processors is on a collision course with cluster computing. Essentially, the rationale is that as cores multiply on the chip, it makes more sense to build and program scaled-up SMP machines than scaled-out clusters.

Hoskins hopes this is the case. In general, his world of data-intensive computing has never been comfortable with the cluster and grid model. The technology heritage in this arena is mostly C and Java apps running on mainframes or big servers. Clusters and MPI programming are seen as fringe technologies. The clusters themselves are hard to deploy and administrate, while the programming model is primitive and not well-supported for commercial application development.

For Hoskins, the path of least resistance to bring data-intensive and compute-intensive computing into the Java universe is through SMP architectures. This week's feature article on Pervasive's Java framework looks at how cluster and multicore technologies are viewed from someone outside the traditional HPC community.

Hoskins tells a convincing story. Although the average multicore processor today is a dual-core chip, soon that will be quad-core. If we just follow a Moore's Law curve, a standard general-purpose processor will have 16 cores by the end of the decade. If you put four of those processors in an SMP box, you essentially have a machine that matches or exceeds the performance of most workgroup and departmental clusters today.

Since the workgroup and departmental systems are the fastest growing segment in HPC, a switch to SMP boxes would change the profile of the market fairly quickly. If multicore SMP systems cannibalize the low end of the cluster market, it will force clusters into the higher-end (but lower volume) capacity computing space.

It's no coincidence that vendors like Azul and Sun, who are pushing the multicore envelope more than most, are also big proponents of scaled up SMP boxes. Azul's 48-core Vega 2 chip is being used in their 768-way Compute Appliance, while Sun's 8-core, 32-thread UltraSPARC processor populates their T1000 and T2000 servers. And just last week, Sun announced first silicon for their new 16-core Rock processor. Since quad-core currently represents the upper end of x86 processors, more general-purpose, scaled-up machines are still on the drawing board. But SGI's f1240 server already offers a 48-core x86 SMP, which can be expanded up to 96 cores.

Beyond 2010, we can extrapolate core doublings into a manycore future, eventually squeezing capacity clusters up against supercomputing capability systems, until … poof, they disappear, never to be heard from again.

Or maybe not. Just as scaling nodes in a cluster has its problems, so does scaling cores and processors in a machine.

The biggest impediment to scale-up is the memory wall. Since SMP systems, by definition, share a common memory space, the data bandwidth into each processor, and then each core, is limited by memory system performance. As more cores compete for memory, each one has proportionally less bandwidth available to it. Memory technology isn't standing still, but RAM has only been doubling in speed every 10 years, well behind the 18-month Moore's Law doubling rate that is driving the multicore phenomenon. Technologies on the horizon to speed up memory access include 3D chip stacking (IBM), on-chip photonics (Intel) and proximity communication (Sun Microsystems). Whether any of these proves to be a practical solutions remains to be seen. But in the short term, the memory wall will act as a barrier to unconstrained SMP scale-up.

In addition, as you add more cores and processors to a system, system architects add additional RAM to keep computational performance balanced with memory capacity. But once you get up into terabytes of RAM, you have to start worrying about the likelihood of hard errors occurring with some frequency. Technologies such as memory scrubbing can deal with this, but the system cost is increased.

But the really big unknown is future HPC application demand for more performance. If applications that now run on low-end clusters don't change appreciably, the equivalent code will run on SMP workstations in a few years. But if those applications are limited by performance, they're likely to migrate to more powerful clusters as the nodes and interconnects ramp up in power.

Certainly in the bigger problems sets in HPC, like climate modeling or other types of large-scale simulations, the demand for more performance is almost insatiable. As you increase the time scales or resolutions of many models, the workloads scale relatively easily. But for commercial HPC applications, it's a mixed bag. Some problems are domain limited, for example, the genomic analysis of a bacterial pathogen. These types of applications don't scale. But many types of engineering simulations can scale as easily as climate models.

One thing did become clear to me in talking to Hoskins: There are users out there who would love to move into the high performance computing world, but are unwilling to migrate to cluster or grid computing because of the difficulty of the software model and the complexity of the system. For these people, multicore SMP systems are the answer.

——

As always, comments about HPCwire are welcomed and encouraged. Write to me, Michael Feldman, at [email protected].

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