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SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 24th international conference on Machine learning
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Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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Proceedings of the VLDB Endowment
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Distributed and Parallel Databases
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NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
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OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
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This paper describes the BID Data Suite, a collection of hardware, software and design patterns that enable fast, large-scale data mining at very low cost. By co-designing all of these elements we achieve single-machine performance levels that equal or exceed reported cluster implementations for common benchmark problems. A key design criterion is rapid exploration of models, hence the system is interactive and primarily single-user. The elements of the suite are: (i) the data engine, a hardware design pattern that balances storage, CPU and GPU acceleration for typical data mining workloads, (ii) BIDMat, an interactive matrix library that integrates CPU and GPU acceleration and novel computational kernels (iii), BIDMach, a machine learning system that includes very efficient model optimizers, (iv) Butterfly mixing, a communication strategy that hides the latency of frequent model updates needed by fast optimizers and (v) Design patterns to improve performance of iterative update algorithms. We present several benchmark problems to show how the above elements combine to yield multiple orders-of-magnitude improvements for each problem.