From an engineering point of view, it is obvious that one would want to save power to attain maximum efficiency in any largely-deployed infrastructure, as we mentioned earlier. But the metrics of tradeoffs in power vs. performance differ vastly depending on the application. In other technology areas, similar differences exist. For example, with automobiles, one metric is to shoot for maximum speed, as with a Formula One race car, where one gets only a little over one kilometer per liter in fuel efficiency. On the other hand, there are fuel-efficiency competitions where one attempts to maximize the distance that can be traveled with a liter of fuel; the current world record is 5134 km, which is nearly four orders of magnitude different than the race car example. Even though combustion technology is recognized as being fairly mature, we seldom observe the exponential growth that we see in the IT industry.
Still, the technological advancements in fuel efficiency improvement in the “standard” automotive industry is in the low percentage points, and even disruptive technologies such as fuel cell or battery-based EVs (Electrical Vehicles) will not improve the efficiency by an order of magnitude. With the IT industry, however, we all know that with Moore’s Law performance has been increasing exponentially since the 1960s and is expected to continue until at least 2015. However, some of the problematic phenomena that drive up power consumption also follow this exponential curve. For example, static leakage current is directly related to the number of transistors, which gave rise to the exponential performance increase in the first place.
While low power consumption may seem to be an obvious engineering ideal for computing systems, especially in HPC, achieving it requires designers to make various tradeoffs that have their own pros and cons.
Pros:
- Higher density — With lower thermal density, an HPC architecture can be more densely packed. This is very important. As Table 1 shows, the absolute space occupancy is starting to limit the machine size, not just in terms of the physical real estate needed, but also, for example, in terms of cable length. In fact, if we were to build a Petascale machine now using Earth Simulator technology, not only would it require a 100MW scale electrical power plant, but it would occupy over 30,000 square meters of floor space (approx 330,000 square feet, or the size of a small football stadium). The weight of its cabling, amounting to approximately 400,000 kilometers or about 250,000 miles, would be a whopping 15,000 tons (several times more than the steel reinforcements that would be used in such a stadium)!
- Reduced Cooling Infrastructure — Cooling requirements of large machines may add anywhere from 25 to 50 % to the consumed power of the machine. Moreover, most machines are designed to operate at their maximum performance level at some point, resulting in maximum thermal heat dissipation. The cost of the initial infrastructure, which would include maximum cooling capacity, could be millions of dollars of equipment and construction, not to mention substantial space consumption.
- Improved error rate / MTBF — Higher cabinet temperatures will result in shorter mean time between failures (MTBF) for various parts of the machine, primarily disk storage, capacitors, and silicone. Some studies have shown that a ten degree increase in the operational temperature of a typical hard disk will reduce its lifetime to 1/10th of its typical rating.
