Cluster Hardware Torture Tests
A plethora of benchmark programs is available. The best benchmark is to run the code that will be used in production, just as it is good to run production code during run-in. This is not always possible, so a standard set of benchmarks is a decent alternative. Also, standard benchmarks establish a relative performance value between systems, which is good information. We do not expect a dramatic performance difference in commodity chipsets and CPUs. Performance differences exist, however, when different chipsets and motherboard combinations are involved, which was the case in this testing trail.
We also wrote a wrapper to a number of standard benchmarking tools and packaged it into a tool called HEPIX-Comp (High Energy Physics—Compute). It is a convenience tool, not a benchmark program itself. It allows a simple make server or make network to measure different aspects of a system. For example, HEPIX-Comp is a wrapper for the following tools (among others): Bonnie++, IOZone, Netpipe, Linpack, NFS Connectathon package and streams.
Understanding the character of the code that runs on the system is paramount to evaluating with standard benchmarking. For example, if you are network-constrained, a fast front-side bus is less important than network bandwidth or latency. These are good benchmarks that measure different aspects of a system. Streams, for example, measure the I/O memory subsystem throughput, which is an important measure for systems with hierarchical memory architectures. Bonnie++ measures different types of read/write combinations for I/O performance.
Many vendors report performance that gives the best possible picture. For example, sequential writes as an I/O performance measure is pretty rosy compared to random, small writes, which are closer to reality for us. Having a standardized test suite run under the Linux installation that is used in production establishes a baseline measurement. If the system is tuned for one benchmark, it might perform the benchmark well at the expense of another system performance factor. For example, systems tuned for large block sequential writes hurt small random writes. A baseline benchmark suite at least shows an apples-to-apples comparison, although not the potentially best performance. So, this is by no means a perfect system, but rather one more data point in an evaluation that characterizes system performance.
All the data was collected and placed on internal Web pages created for the evaluation and shared among the group. We met once a week and reported on the progress of the testing. After our engineering tests were complete, we chose a system.
Non-engineering factors (contractual agreements, warranties and terms) are critical to the success of bringing in new systems for production work. The warranty terms and length affects the long-term cost of system support. Another consideration is the financial health of the vendor company. A warranty does little good if the vendor is not around to honor it.
Also crucial is the acceptance criteria, although seldom talked about until too late. These criteria determine the point in the deployment when the vendor is finished and the organization is willing to accept the systems. This point should be made in writing in your purchase order. If the vendor drops the system off at the curb and later, during the rollout period, some hardware-related problem surfaces, you need to be within your rights to ask the vendor to fix the system problem or remove the system. On the vendor side, a clear separation between what constitutes a hardware or software problem needs to be made. Often a vendor has to work with the client to determine the nature of the problem, so that costs need to be built in to the price of the system.
The success of the method outlined in this article is apparent in how much easier, and therefore cheaper, it is to run the systems we chose after doing this extensive evaluation. We have other systems that we purchased without doing the qualification outlined here. We have had fewer problems after the better evaluation, and we are able to get more work done in other areas, such as tool writing and infrastructure development. And, we are less frustrated, as are our researchers, with good hardware in production.
John Goebel works at the Stanford Linear Accelerator Center (SLAC) in Menlo Park, California. He is part of the SLAC Computing Services (High-Performance Group), supporting a high-energy physics project for a worldwide research community.
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