1. Stream Processing of X-ray Microdiffraction Data on Multicores Yuzhen Xie, University of Western Ontario (UWO) joint work with Alain Biem, IBM Research Michael A. Bauer, UWO Stewart McIntyre, UWO Nobumichi Tamura, Lawrence Berkeley National Lab AMMCS, July 2011

2. Motivation Efficiently use of multi-core processors to process large blocks of synchrotron XRD data generated at high rates (1 to 10 images per second of each 4MB) Develop high-performance kernels to achieve near real- time data analysis for synchrotron experiments, the goal of the Active Network Interchange for Scientific Experimentation (ANISE) project

3. Synchrotron X-ray White-beam Microdiffraction Incident X-ray (5 – 30 KeV) CCD Camera Sample Diffracted beams Dectris Pilatus 1M CCD at ALS (2010): sub-second readout An image showing the Laue microdiffration pattern of a unit-cell in a crystal sample

4. Process of Laue Patterns for Micro-texture Analysis Background fit and removal (optional)

5. Example of Crystallographic Orientation and Strain Maps (courtesy: Jing Chao and Marina Fuller, UWO) Strain map, average strain: 9.92 x 10 -3 Result by XMAS (X-ray Microdiffraction Analysis Software), Advanced Light Source Orientation map

6. Reference Software Packages XMAS (X-ray Microdiffraction Analysis Software), Advanced Light Source 3D X-ray Microdiffraction Analysis Software Package in IDL, Advanced Photon Source A prototype of C code for a selection of features in Laue pattern analysis, Science Studio and ANISE projects, UWO Best sequential processing time: 25 to 50 seconds per image

7. 7 7 Stream Processing Illustration Continuous IngestionContinuous Analysis

8. 8 IBM Streams Programming Model Streams Processing Language (SPADE) Input OutputProcess Platform optimized compilation

9. Laue XRD Processing System on Streams Processing Elements (mainly User-defined Operators (UDOPs)) Preprocessing -Formatting -Parsing -XRD image data Background Removal Blob Searching Peak Fitting Indexing -Blobs search -Scheduling for parallel peak fitting XRD Image Stream Filters available -Parabolic -2D Bruckner -2D Mean Filter - Lorentz - Gaussian - Pearson VII Split operator Functions Available Strain Bundle Sorting

10. Key Implementation Techniques Efficient Source operator for parsing image files: block reading and type casting Fine-grained pipelining and cache-efficient background filters Memory-efficient parallel peak fitting Organize common parameter values as a stream for shared-use in indexing and strain analysis

11. A Fine-pipelined Background Filter based on Parabolic Method

13. A Pilatus TIFF Image before and after Background Removal

14. Memory-efficient Parallel Peak Fitting

15. Data Management: the Key Issue Blob center b: data set R b (d b x d b ) is needed for fitting a peak with center at p. Peak center p: data set R p is needed for integrated intensity computation. Assume p is not far from b. Define R to be the square region (2d b x 2d b ) with center at b. Attach a data set R to a blob tuple rather than passing the whole image to each peaking fitting element. Determine R b and R p by coordinate mapping in R. Small data size, good locality, no memory contention, …, and hence efficiency.

16. A SPADE Code Snippet for Blob Searching and Parallel Peak Fitting ## Parse an image stream engStream(height: Integer, width: Integer, emax: …, evalues: DoubleList) := Source()[“file://c4-3_001.spe”,udfbinformat=“speParser”, blocksize=65536*15]{} ## Search blobs and generate blob stream stream blobStream( groupid: Integer, blobid: Integer, …, lroi: DoubleList) := Udop(engStream)[“blobSearch”]{np=“NUM_PF”} ## Split blobs to subgroups for_begin @c 0 to NUM_PF-1 stream subBlobStream@c(groupid: Integer, blobid: Integer, …, lroi: DoubleList) for_end := Split(blobStream)[groupid]{} ## Parallel peak fitting for subgroups of bobs and bundle all peaks together bundle peakBundle := () for_begin @c 0 to NUM_PF-1 stream subPeakFitStream@c(numblobs: Integer, x: Integer, …, inten: Double) := Udop(subBlobStream@c)[“peakFitting”]{} peakBundle += subPeakFitStream@c for_end

17. Organize Common Parameter Values as one Stream for Shared- use in Indexing and Strain Refinement of all XRD Images k in q3q3 q1q1 q2q2 33 11 22 k out q 22 Known crystal structure and energy range (5-30 keV) List of peak positions on the CCD Find triplets  1,  2,  3 (thus q 1,q 2,q 3 ) matching calculated and measured values within a given angular tolerance Calculated q hkl list of reflections Experimental q i list of reflections Choose triplets indexing the largest number of reflections within a given angular tolerance. Look for “missing” reflections. Beam direction k in, Detector position and dimensions Strain refinement

18. Streams Live Graph: One Pipeline with 4 Processing Elements for Parallel Peak Fitting 2.5 seconds per image (2084*2084) on an Intel Core2 Quad CPU Q9550 (2.83 GHz, 8 GB RAM and 6 MB L2 cache) Image Sourcing Blob Search & Scheduling Blob Search & Scheduling Parallel Peak Fitting Parallel Peak Fitting Indexing Parameter Sourcing Parameter Sourcing Strain

19. Super-linear speedup obtained on an Intel Core2 Quad CPU Q9550 Streams Live Graph: 4 Pipelines to Process 4 Images Concurrently in Streaming Mode

20. Conclusion We present the first stream processing application in the field of synchrotron XRD data analysis. We show that stream processing is an effective model for efficiently using multicore processors for XRD image data analysis. Our system provides a high-performance processing kernel to achieve near real-time data analysis of image data from synchrotron experiments. Our work-in-progress include: evaluation, optimization, configuration and deployment of this kernel to large systems with many cores to process large set of XRD images in parallel and streaming mode. Thank You!