Scalable and Power-Efficient Data Mining Kernels

Transcription

1 Scalable and Power-Efficient Data Mining Kernels Alok Choudhary, John G. Searle Professor Dept. of Electrical Engineering and Computer Science and Professor, Kellogg School of Management Director of the Center for Ultra-Scale Computing and Security Northwestern University

2 Challenges in Climate Data Analysis: Massive Data Climate data is huge and increasing in size Current Climate Model Intercomparison Project (CMIP5) expected to produce 2.5 PB of data 1 Climate data predicted to reach 350 PB by How do we store it? How do we efficiently access it? How do we analyze it? 1 J. T. Overpeck, G. A. Meehl, S. Bony, and D. R. Easterling, Climate data challenges in the 21st century," Science, vol. 331, no. 6018, pp , 2011.

3 Challenge: Climate Data is Inherently Complex Multivariate data Spatio-temporal nature Long range dependencies Uncertainty Periodic patterns Non-stationary behavior Nonlinear processes Temporally and geographically local phenomena

4 A HPC Library of Data Analysis Kernels Performance typically dominated by a few kernels Common sub-processes Library of highly optimized, scalable kernels Flexibility to define custom analytics pipelines High scalability Integrate into a software framework (e.g. R)

5 HPC Technologies Enable Systems-Scale Science on Massive Data High end architectures Multiple CPUs, multi-core, and GPUs Technologies like MPI, OpenMP, and CUDA Approximate analysis Reduces power and time costs at expense of accuracy In situ analytics Analyze data as created to reduce storage burdens Parallel I/O and file systems to efficiently store data

6 Scalability Results on Representative Kernels Illustrative example: K-means clustering on distributed memory platform (MPI) with thousands of cores gives linear-speedups.

7 Harnessing heterogeneous architectures (GPU) Hardware Platform CPU: Intel Quad Core 2.4 GHz, 4GB main memory GPU: Tesla C2050, 448 cores, 1.15 GHz, 144 GB/s memory bandwidth, 3GB DRAM K-means clustering More than 360X speedup over the CPU implementation 4 million data points, 48 dimensions, and 32 clusters Large Scale Correlation Analysis More than 60X speedup overt the CPU implementation All-pair correlation calculation of 20,000 vectors of length 1000

8 Power-Aware Algorithms for GPU Architecture K-Means Reduced bit representation Tradeoff between energy savings, speed-up, and accuracy 12-bit representation gives 47% energy savings, ~ 0.02% error rate

9 Power-Aware Algorithms for GPU Architecture Large Scale Correlation Analysis Reduced bit representation Tradeoff between energy savings, speed-up, and accuracy

10 Case Study: Analysis of Decadal Trends in Climate Identified nontrivial events and trends in climate network Computational bottleneck All-pairs correlation calculation

11 Analysis of Decadal Trends in Climate: Methods Data processing to reduce seasonality Division of data into overlapping decadal time windows Analysis of dependencies using Pearson correlation Analysis of climate network evolution using stable clusters Characterization of the climate networks through clustering Construction of decadal climate networks by applying correlation threshold

12 Analysis of Decadal Trends in Climate: Results Reanalysis data Monthly mean surface air temperature, Evidence of large-scale modulation of planetary-scale climatic pattern Stable teleconnection between Nino-3 region and Indian Ocean Coincident with El Niño event of 1972 Realignment of Sahel region to northern Africa Indirect evidence of desertification Full results at:

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