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PhysX, GPUs, and the future of supercomputing

What if I told you that most of what the tech press thinks they know about …

A grand unified theory of the HPC market

A glance at the recent evolution of the Top 500 Supercomputers list
nicely illustrates the fact that the supercomputing market is becoming
increasingly dominated by systems built from common off-the-shelf
(COTS) components and connected together in a cluster configuration;
the only specialty hardware in many of these clusters is the system
interconnect. The rise of the COTS cluster has made owning a
supercomputer much less costly, and as supercomputer ownership gets
cheaper it gets more widespread. But as supercomputers get more
widespread, they get cheaper, and thus the virtuous circle called
"economies of scale" works its magic and causes supercomputers to

If you're a silicon vendor who wants a spot in the growing number of HPC clusters, the price of entry is a COTS part where the emphasis is on "C" for commodity.

So let's say that you're a company with a great design for a multicore coprocessor chip that would offer the HPC market remarkable speedups on data-parallel workloads vs. today's clusters of general-purpose x86 processors. The problem you face is that you can't just produce a chip specifically for the HPC market, because the chip has to be available in enough volume to keep the price down, and the HPC market just isn't big enough for that yet. The HPC market is growing, though, and you want to be in on it because nothing helps stock prices like the prospect of growth (conversely, nothing depresses a stock price like the perception that you've run out of room to grow).

If you're IBM or Ageia, then the way to get your multicore coprocessor chip into COTS clusters is clear: sell the chip in the gaming market first, because the gaming market has a unique combination of high volume and an insatiable appetite for parallelism. Then, when the chip is selling widely enough to be profitable (fabs are expensive, and you have to keep them busy or you lose money) then you can break into the small but profitable and growing market for HPC clusters. This is why IBM got Sony to help them foot the bill of designing and fabbing Cell, and it's why Ageia wants gamers to buy a physics acceleration board.

From the opposite end of the spectrum, if you're NVIDIA or AMD/ATI and you're already selling a large number of parallel coprocessors in the gaming market, then you have the hardest part—the challenge of ramping up volume—licked. Now all you need in order to get into HPC clusters is a developer toolset and a PR campaign.

Challenges for the current players

Ultimately, IBM and Ageia still face that volume-related pricing hurdle. They're still hoping that gamers will latch onto their chips and pull them far enough up the volume curve to make their HPC play really profitable. Because its Cell processor occupies the prime gaming real-estate of Sony's PlayStation 3 console, IBM is much further up this hill than Ageia is. The latter company is still struggling to gain traction in a gaming market that's still eyeing with skepticism the prospect of spending a few hundred dollars and a PCI slot on a physics accelerator.

NVIDA and AMD/ATI, on the other hand, face a different set of challenges altogether. For all their talk of stream processing and generalized data-parallel computation, the G8800 and the R600 are still graphics processing units, with plenty of graphics-specific logic and with microarchitectures that are designed for ultra-fast real-time 3D rendering. Not only are all of a GPU's architectural decisions made in favor of fast 3D rendering, but power efficiency hasn't really entered the GPU picture yet. The vast majority of these chips are sold to gamers who don't factor their electric bill into their hardware purchases; the GPU market is about raw performance, and is relatively insensitive to performance/watt considerations.

Because NVIDIA and AMD/ATI still live and die by 3D gaming benchmarks, and because their sales volume is tied to a customer base that doesn't care about power consumption, neither NVIDIA nor ATI can afford to sacrifice any 3D rendering performance in favor of improving their performance-per-watt ratio on more general kinds of data-parallel workloads. Unfortunately, HPC cluster buyers who have to power a room full of systems do care about power consumption, so per-chip performance/watt matters for them in a way that it does not for the Opposable Thumbs crowd.

Aside from performance/watt considerations, GPUs face another major challenge in the HPC cluster market: a rapid pace of microarchitectural evolution that can spoil investments in hand-optimization. Like GPUs' large die sizes and voracious appetites for wattage, the high degree to which GPU microarchitectures can change from one generation to the next is a direct result of the fact that their sales are tied to their dominance in 3D gaming benchmarks. A GPU maker will change almost anything about a design if it means a boost in frames-per-second on top games. These changes mean that if you get really invested in low-level hand optimizations, then you'd better plan to stick with that exact same GPU for the life of your cluster. Of course, companies like Peakstream and Rapidmind have middleware layers aimed at alleviating the effects of this kind of GPU product churn, so for HPC customers that use such tools maintaining software compatibility across multiple hardware upgrade cycles will be less of a challenge.

To finish off where we started—with the PhysX PPU—Ageia faces a steep uphill battle in the HPC coprocessor game. Not only does PhysX not have volume at the moment, but if its architecture is as I've described it, then it also faces direct competition from both established vendors like IBM (and probably Intel, before long) and other startups like Clearspeed. In this kind of competitive environment, Ageia had better post some killer performance/watt numbers if their product is going to have any sort of chance against GPUs on the one hand and other massively multicore coprocessors on the other.

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