HPCwire: Mission Possible -- Greening the HPC Data Center

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August 31, 2009

Mission Possible -- Greening the HPC Data Center

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The Cray XT5 Jaguar supercomputer at Oak Ridge National Laboratory (ORNL) is big in every respect. It's big physically – the machine's 284 cabinets sprawl across 5,700 square feet, a space slightly larger than a college basketball court. And it's powerful – with its 182,000 processing cores, 362 terabytes of memory and a 10 petabyte file system, Jaguar is rated at 1.64 petaflops; making it the fastest machine in the world today for open science.
Cray XT5 Jaguar Supercomputer at ORNL

These impressive computational capabilities also make for a supercomputer that is ravenously hungry for power. Jaguar consumes up to seven megawatts, enough to power a town or small city. About half the power is used to operate the system, the other half to cool it. Right now power bills at supercomputing centers around the world are running into the tens of million dollars annually. But as planned upgrades move these centers into the exascale range, potential spending on power goes off the chart.

You might think that power and cooling problems of this sort is the exclusive domain of the big high performance computing (HPC) machines at major government scientific laboratories. That's not the case. Smaller clusters operating in the terabyte range are just as prone to power consumption problems. This makes the greening of the data center a daunting challenge that must be addressed.

It's no wonder that Sumit Gupta, senior manager of the Tesla™ GPU computing business unit at NVIDIA® reports: "Power consumption and the need for more energy efficient computing systems is top of mind for most of our customers."

In fact, he emphasizes that the reason for the current intense interest in green computing is that data centers supporting scientific computation and enterprise HPC are in a power crisis. Gupta estimates that between 40% and 60% of the energy costs in these data centers is attributable to cooling (see chart below).
Breakout of HPC data center energy costs

The fact is that traditional architecture for supercomputing just does not scale well. Back in the early 1980s, the first gigaflop machine, a Cray X-MP, required a modest 60 kW. By the mid-1990s, a one teraflop system required 850 kW. Today's petaflop supercomputers with their megawatt appetites will look positively dainty compared to the 25 MW or more that the next decade's exascale machines will need if current architectural trends continue unabated.

Supercomputing is a highly skewed field. Of the top 500 supercomputers, only a few, such as the machines at ORNL and Los Alamos National Laboratory, operate in the petaflop range. The vast majority of HPC clusters are all cruising along at less than 30 teraflops. But, pound for pound, their power challenges are just as daunting. For example, a small research cluster with 32 CPU servers valued at about $120,000 requires 21 kW of power for the servers alone. The annual cost for power and cooling runs almost $40,000 annually. The bottom line: In three years, you will have spent as much on these operating costs as you initially paid for the servers themselves.

Gupta notes that the new data center economics are having an impact on the academic community. Professors writing grants for additional HPC horsepower for their investigations are increasingly being asked by the university computing facilities to include power and cooling costs in their grant request. In the past, IT picked up the tab as an overhead expense.

Compounding the power/cooling problem is the fact that the computational capabilities of traditional HPC CPUs – including the latest multi-core CPUs – have not kept pace with the demand for computing performance on the part of researchers in science and industry. The more multi-core CPUs are added to a system, the greater the power and cooling requirements. Given this situation, being green is not only not easy, it's almost impossible. Fortunately, recent advances in computer architecture point to a way out of this dilemma.

Going Green with GPUs

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