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Personal Supercomputing and data analysis.
What experience has anyone had with desktop supercomputers employing, for example, the NVIDIA Tesla computing cards? Does this type of hardware configuration work optimally for data mining and statistical analyses in cases where data is not always visualized? What about simulations (MCMC, etc..)? Are there significant improvements over 8 core (i.e. Xeon boards) with high RAM configurations? When discussing, please describe your hardware configuration as much as possible and your reference application, data size/structure, etc.
Tags: large dataset, nvidia, supercomputer, supercomputing, tesla
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Permalink Reply by Kevin Urban on -
My company has created a SQL coprocessor for the analytics market which is used in our DBx appliance. We considered using GPUs for this (which are good with floating point calculations) but we found that GPUs are good at doing only certain tasks, mainly revolving around graphics based calculations. As we were interested in text and integer based SQL operators (group-by, join-by, select project, regular expressions), we decided on using FPGA technology, which is in essence a programmable ASIC. Our results indicate that our coprocessor is about 10x the performance of quad-core CPUs for SQL based operators, and that octal-core CPUs provide only marginal improvements (programming models still aren't taking advantage of core parallelism). We have shown that Monte Carlo simulations are 16x faster than quad-core processors (8x faster the GPUs) running double precision. You can read more about it from our website (which will be updated on Monday).
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