If You Doubt the Utility of GPUs for HPC, Read this
By Josh Simons on Nov 16, 2008
As you may know, the Tokyo Institute of Technology is the home of TSUBAME, the largest supercomputer in Asia. It is an InfiniBand cluster of 648 Sun Fire x4600 compute nodes, many with installed Clearspeed accelerator cards.
The desire is to continue to scale TSUBAME into a petascale computing resource over time. However, power is a huge problem at the site. The machine is responsible for roughly 10% of the overall power consumption of the Institute and therefore they cannot expect their power budget to grow over time. The primary question, then, is how to add significant compute capacity to the machine while working within a constant power budget.
It was clear from their analysis that conventional CPUs would not allow them to reach their performance goals while also satisfying the no-growth power constraint. GPUs--graphical processing units like those made by nVidia--looked appealing in that they claim extremely high floating point capabilities and deliver this performance at a much better performance/watt ratio that conventional CPUs. The question, though, is whether GPUs can be used to significantly accelerate important classes of HPC computations or whether they are perhaps too specialized to be considered for inclusion in a general-purpose compute resource like TSUBAME. Professor Matsuoka's talk focused on this question.
The talk approached the question by presenting performance speed-up results for a selection of important HPC applications or computations based on algorithmic work done by Prof. Matsuoka and other researchers at the Institute. These studies were done in part because GPU vendors do a very poor job of describing exactly what GPUs are good for and what problems are perhaps not handled well by GPUs. By assessing the capabilities over a range of problem areas, it was hoped that conclusions could be drawn about the general utility of the GPU approach for HPC.
The first problem examined was a 3D protein docking analysis that performs an all-to-all analysis of 1K proteins to 1K proteins. Based on their estimates, a single protein-protein interaction analysis requires about 200 TeraOps while the full 1000x1000 problem requires about 200 ExaOps. In order to maximally exploit GPUs for this problem, a new 3D FFT algorithm was developed that in the end delivered excellent performance and a 4x better performance/watt over IBM's BG/L system, which itself is much more efficient than a more conventional cluster approach.
In addition, other algorithmic work delivered speedups of 45X over single conventional CPUs for CFD, which is typically limited by available bandwidth. Likewise, a computation involving phase separation liquid delivered a speedup of 160X over a conventional processor.
Having looked at single node performance and compared it to a single-node GPU approach and found that GPUs do appear to able to deliver interesting performance and performance/watt for an array of useful problem types so long as new algorithms can be created to exploit the specific capabilities of these GPUs, the next question was whether these results could be extended to multi-GPU and cluster environments.
To test this, the team worked with the RIKEN Himeno CFD benchmark, which is considered the worst memory bandwidth-limited code one will ever see. It is actually worse than any real application one would ever encounter. If this could be parallelized and used with GPUs to advantage, then other less difficult codes should also benefit from the GPU approach.
To test this, the code was parallelized to run using multiple GPUs per node and with MPI as the communication mechanism between nodes. Results showed about a 50X performance improvement over a conventional CPU cluster on a small-sized problem.
A multi-GPU parallel sparse solver was also created which showed a 25X-35X improvement over conventional CPUs. This was accomplished using double precision implemented using mixed-precision techniques.
While all of these results seemed promising, could such a GPU approach be deployed at scale in a very large cluster rather than just within a single node or across a modest-sized cluster? The Institute decided to find out by teaming with nVidia and Sun to enhance TSUBAME by adding Tesla GPUs to some (most) nodes.
Installing the Tesla cards into the system went very smoothly and resulted in three classes of nodes: those with both Clearspeed and Tesla installed, those with only Tesla installed, and those Opteron nodes with neither kind of accelerator installed.
Could this funky array of heterogeneous nodes be harnessed to deliver an interesting LINPACK number? It turns out that it could, with much work and in spite of the fact that there was limited bandwidth in the upper links of the InfiniBand fabric and that they had limited PCIx/PCIe bandwidth available in the nodes (I believe due to the number and types of slots available in the x4600 and the number of required devices in some of the TSUBAME compute nodes.)
As a result of the LINPACK work (which could have used more time--it was deadline-limited) the addition of GPU capability in TSUBAME allowed its LINPACK number to be raised from 67.7 TFLOPs, which was reported in June, to a new high of 77.48 TFLOPs which shows an impressive increase.
With the Tesla cards installed, TSUBAME can now be viewed as a 900 TFLOPs (single precision) or 170 TFLOPs (double precision) machine. A machine that has either 10K cores or 300K SIMD cores if one counts the components embedded within each installed GPU.
The conclusion is pretty clearly that GPUs can be used to significant advantage on an interesting range of HPC problem types, though it is worth noting that it also appears that significantly clever, new algorithms may also need to be developed to map these problems efficiently onto GPU compute resources.