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Parallel Computing
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
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Algorithm 806: SPRNG: a scalable library for pseudorandom number generation
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ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Clustering on a Hypercube Multicomputer
IEEE Transactions on Parallel and Distributed Systems
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Monitoring Large Systems Via Statistical Sampling
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Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
PerfExplorer: A Performance Data Mining Framework For Large-Scale Parallel Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
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Proceedings of the twenty-second annual symposium on Computational geometry
PNMPI tools: a whole lot greater than the sum of their parts
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Scalable load-balance measurement for SPMD codes
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Discovering and Exploiting Program Phases
IEEE Micro
Parallel Hierarchical Clustering on Market Basket Data
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Major Computer Science Challenges At Exascale
International Journal of High Performance Computing Applications
Scalable Identification of Load Imbalance in Parallel Executions Using Call Path Profiles
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Scalable fine-grained call path tracing
Proceedings of the international conference on Supercomputing
Large scale debugging of parallel tasks with AutomaDeD
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Elastic and scalable tracing and accurate replay of non-deterministic events
Proceedings of the 27th international ACM conference on International conference on supercomputing
An early prototype of an autonomic performance environment for exascale
Proceedings of the 3rd International Workshop on Runtime and Operating Systems for Supercomputers
Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Existing supercomputers have hundreds of thousands of processor cores, and future systems may have hundreds of millions. Developers need detailed performance measurements to tune their applications and to exploit these systems fully. However, extreme scales pose unique challenges for performance-tuning tools, which can generate significant volumes of I/O. Compute-to-I/O ratios have increased drastically as systems have grown, and the I/O systems of large machines can handle the peak load from only a small fraction of cores. Tool developers need efficient techniques to analyze and to reduce performance data from large numbers of cores. We introduce CAPEK, a novel parallel clustering algorithm that enables in-situ analysis of performance data at run time. Our algorithm scales sub-linearly to 131,072 processes, running in less than one second even at that scale, which is fast enough for on-line use in production runs. The CAPEK implementation is fully generic and can be used for many types of analysis. We demonstrate its application to statistical trace sampling. Specifically, we use our algorithm to compute efficiently stratified sampling strategies for traces at run time. We show that such stratification can result in data-volume reduction of up to four orders of magnitude on current large-scale systems, with potential for greater reductions for future extreme-scale systems.