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Efficient Supercomputing with Green Destiny
gManZboy writes: "Is it an oxymoron to have an efficient supercomputer? Wu-Chun Feng (Los Alamos National Laboratory) doesn't believe so - Green Destiny and its children are Transmeta-based supercomputers that Wu thinks are fast enough, at a fraction of the heat/energy/cost, according to ACM Queue." 240 processors running under 5.2kW (or less!) is nothing to sneeze at. The article offers up this question: might there be other metrics that might be important to supercomputing, rather than relying solely on processing speed?
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The MHZ war has been going on for soooo long that everyone just excepted that faster MHZ related to faster machines. Well, 64Bit computers are placing chip manufactures in a position where they have to market on a platform that declares that MHZ doesn't really matter.
I think the question is a bit naive though as everyone knows a hundred software tools to rate performance of CPUs rather than just relying on MHZ.
Nick Powers
Encryption: I may not agree with what you say, but I will defend your right to encrypt it...
The other point is: how expensive it is to support a cluster ? Not only the energy consumption, but also the infraestructure. It is pretty darn difficult to keep a thousand processors cold. You may need a special building, special power supply for it, etc.
A final point: as far as I know, the rule of thumb is that the floating point performance with these energy efficient processors is of the same order of magnitude as regular processor, may be a factor 2 difference.
You do the math ... :-)
Why are supercomputers primarily benchmarked by their speed? The answer comes when you consider that almost all labour-saving devices are measured in the work they perform in a given period of time.
Time is the only truly finite resource from a human perspective. As technology has progressed, distances have been conquered, vast energies harnessed, but old Father Time is still inescapable.
As a result, we place great value on just how much time is taken to accomplish anything.
You're comparing apples to oranges, not to mention that your info's a little off...
1) A Nehemiah core C3 runs really close to the same performance of a comparably clocked Celeron, with the same general power consumption of a Samuel2 core (For those that don't know, part of how VIA's chip originally got it's low power is that the FPU was underclocked by a factor of 1/2). It's a nice chip overall, but it's not really intended (nor are they USING it that way) for scientific or gaming applications even though you can use it for that. The C3's winning usages is in something like a media PC, workgroup servers, and embedded systems where you need low power consumption, relatively low cost, and relatively high performance compared to other x86 embedded solutions.
2) The Crusoe and similar chips are very fast executing VLIW CPUs (very much like the Itanium...) that have code morphing that translates x86-32 instructions into comparable sets of instructions for the VLIW CPU- in fact it's very good at doing this sort of thing. The reason it's less desirable with a desktop or gaming application is that you're exceeding the VLIW code cache regularly, meaning you have to keep recompiling the x86 instructions into the native VLIW ones. For a scientific application, the same task gets executed time and time again and usually ends up with most, if not all, of the code in the pre-morphed code cache. At that point, you're now in the high-performance domain with very little power consumption. The Crusoe in this application would consume less power than the G5 and run just as fast. (Check the article that you're commenting on...)
Do some thinking outside of the box here, what's good or great on a desktop machine isn't always the optimal choice for supercomputing clusters or HA clusters. Depends on a bunch of factors, including what you're going to be running on the systems in question and what kind of environmental conditions you're going to be facing.
I am not merely a "consumer" or a "taxpayer". I am a Citizen of the State of Texas
No, supercomputers that can do a lot of image processing cannot waste power simply because it might be available.
Modest supercomputers are used in the military on airframes. Power consumption is important for at least two reasons. First is the wattage and power draw. Second, and more subtle, it that the cooling requirements while flying at high altitude become more important than simple fan noise. Pentiums burn up no matter what you do. PowerPCs@10Watts with conduction cooling will survive.
Especially when simulating nuclear weapons.
-Shane
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Making a case for Efficient Supercomputing
From Power
Vol. 1, No. 7 - October 2003
by Wu-Chun Feng, Los Alamos National Laboratory It's time for the computing community to use alternative metrics for evaluating performance.Motivation
A supercomputer evokes images of big iron and speed; it is the Formula 1 racecar of computing. As we venture forth into the new millennium, however, I argue that efficiency, reliability, and availability will become the dominant issues by the end of this decade, not only for supercomputing, but also for computing in general.
Over the past few decades, the supercomputing industry has focused on and continues to focus on performance in terms of speed and horsepower, as evidenced by the annual Gordon Bell Awards for performance at Supercomputing (SC). Such a view is akin to deciding to purchase an automobile based primarily on its top speed and horsepower. Although this narrow view is useful in the context of achieving performance at any cost, it is not necessarily the view that one should use to purchase a vehicle. The frugal consumer might consider fuel efficiency, reliability, and acquisition cost. Translation: Buy a Honda Civic, not a Formula 1 racecar. The outdoor adventurer would likely consider off-road prowess (or off-road efficiency). Translation: Buy a Ford Explorer sport-utility vehicle, not a Formula 1 racecar. Correspondingly, I believe that the supercomputing (or more generally, computing) community ought to have alternative metrics to evaluate supercomputersspecifically metrics that relate to efficiency, reliability, and availability, such as the total cost of ownership (TCO), performance/power ratio, performance/space ratio, failure rate, and uptime.
Motivation
In 1991, a Cray C90 vector supercomputer occupied about 600 square feet (sf) and required 500 kilowatts (kW) of power. The ASCI Q supercomputer at Los Alamos National Laboratory will ultimately occupy more than 21,000 sf and require 3,000 kW. Although the performance between these two systems has increased by nearly a factor of 2,000, the performance per watt has increased only 300-fold, and the performance per square foot has increased by a paltry factor of 65. This latter number implies that supercomputers are making less efficient use of the space that they occupy, which often results in the design and construction of new machine rooms, as shown in figure 1, and in some cases, requires the construction of entirely new buildings. The primary reason for this less efficient use of space is the exponentially increasing power requirements of compute nodes, a phenomenon I refer to as Moore's law for power consumption (see figure 2)that is, the power consumption of compute nodes doubles every 18 months. This is a corollary to Moore's law, which states that the number of transistors per square inch on a processor doubles every 18 months [1]. When nodes consume and dissipate more power, they must be spaced out and aggressively cooled.
Figure 1
Without the exotic housing facilities in figure 1, traditional (inefficient) supercomputers would be so unreliable (due to overheating) that they would never be available for use by the application scientist. In fact, unpublished empirical data from two leading vendors corroborates that the failure rate of a compute node doubles with every 10-degree C (18-degree F) increase in temperature, as per Arrenhius' equation when applied to microelectronics; and temperature is proportional to power consumption.
We can then extend this argument to the more general computing community. For example, for e-businesses such as Amazon.com that use multiple compute systems to process online orders, the cost of downtime resulting from the unreliability and unavailability of computer systems can be astronomical, as shown in table 1millions of dollars per hour for brokerages an
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