1 / 25

Optimized Parallel Distribution Load Flow Solver on Commodity Multi-core CPU

Optimized Parallel Distribution Load Flow Solver on Commodity Multi-core CPU. Tao Cui (Presenter) Franz Franchetti Dept. of ECE. Carnegie Mellon University Pittsburgh PA tcui@ece.cmu.edu. This work is supported by NSF 0931978 & 1116802. Smart Grids. Image by Dr. M.Sanchez. Smart Grids.

khoi
Download Presentation

Optimized Parallel Distribution Load Flow Solver on Commodity Multi-core CPU

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Optimized Parallel Distribution Load Flow Solver on Commodity Multi-core CPU Tao Cui (Presenter) Franz Franchetti Dept. of ECE. Carnegie Mellon University Pittsburgh PA tcui@ece.cmu.edu This work is supported by NSF 0931978 & 1116802

  2. Smart Grids Image by Dr. M.Sanchez

  3. Smart Grids • New players in the grid • Challenges • Undispatchable, large variances, great impact on grid • Large population exhibits stochastic properties Images from wikipedia Source: Pantos 2011 Source: LBNL-3884e Source: ORNL/TM2004/291

  4. Motivation • Conventional Distribution System • Passively receiving power • Few realtime monitoring or controls • Challenges in Distribution System • Solar, wind, stochastic • Large variance and impact • Smart Distribution System • New Sensors: Smart Meters • High Performance Deskside Supercomputer • A Computational Tool for Probabilistic Grid Monitoring Image from: Wikipedia ~Tflop/s $1000 1kW power Image from: Dell

  5. Outline • Motivation • Distribution System Load Flow Analysis • Code Optimization • Real Time Implementation • Conclusion

  6. Core: Distribution Load Flow • Distribution System: • Radial , high R/X ratio, varying Z, unbalanced • NOT suitable for transmission load flow • Forward / Backward Sweep (FBS) • Implicit Z-matrix, detail model, convergence • Generalized Component Model[Kersting2006] • One Terminal Node Model: Constant PQ: • Two Terminal Link Model: IEEE 37 NodeTest Feeder: Based on an actual feeder in California Source: IEEE PES Distribution System Analysis Subcommittee Backward: Forward:

  7. Core: Distribution Load Flow • Forward / Backward Sweep [Kersting2006] • Branch current based • Input: substation voltage, load; output: all node voltages • Steps: • 1: Initial current = 0, Initial voltage V = V0; • 2: Compute node current In using Node model; • 3: Backward: Compute branch current Ib using Link model & KCL; • 4: Forward: Update Vk+1 = Vk based on Ib over Link model; • 5: Check convergence (|dS|<Error Limit) stop or go to step 2. Forward Backward IEEE 4 Node Test Feeder Example

  8. Core: Distribution Load Flow • 3-Phase Voltage on IEEE 37 Nodes Test Feeder Phase C Phase A Phase B Nominal 1.1 0.90 ANSI C84.1: Nominal: 115, Range A:110~126V Range B:107~127V

  9. Parallel High Performance Power Flow Solver Our Approach Random Variable Sampling • Random Number Generator • Basic Uniform RNG + Transformation for different PDFs • Parallel strategy for multi-thread implementation • Optimized Parallel Distribution Load Flow Solver • Code optimizations • Highly parallel implementation for Monte Carlo applications • Density Estimation & Visualization • Kernel density estimation

  10. Outline • Motivation • Distribution System Load Flow Analysis • Code Optimization • Real Time Implementation • Conclusion

  11. Optimization: Data Structures • Data Structure Optimization • Baseline: C++ object oriented, a tree object • Translate to array access, exploit spatial/temporal locality • Other techniques: unroll innermost loops, scalar replacement, pre-compute as much as possible (C++) (C Array) GridLab-D: the Smart Grid Simulator www.gridlabd.org, opensource since 2009

  12. Optimization: Pattern Based Syntehsis • Algorithm-level Optimization • Pattern based matrix-vector multiplication For A,B,c,d matrices: • Multi-grounded Cable: diagonal matrix • Ignore shunt & coupling: c = 0, d = I, A = I Reduce unnecessary operations Reduce unnecessary storage for better memory access Similar to [Belgin2009] (C Pattern) switch (mat_type){ casereal_diag_equal_mat: output[0] = *constant * input[0]; ... output[5] = *constant * input[5]; break; caseimag_diag_equal_mat: output[0] = -*constant * input[3]; output[1] = -*constant * input[4]; output[2] = -*constant * input[5]; output[3] = *constant * input[0]; output[4] = *constant * input[1]; output[5] = *constant * input[2]; break; ... } … case 1: case 2: case N: code 1 code 2 code N

  13. Data Parallelism (SIMD) • SIMD parallelization • SIMD: Single Instruction Multiple Data SSE: Streaming SIMD Extensions • 128bit, 4 floats in one register • eg. 4 “fadd” at cost of 1 “addps” AVX: Advanced Vector eXtensions (256bit, 8 floats), Larrabee (512bit, 16 floats) • Vectorized solver on SIMD level for MCS: • Assumptions & Limitations: converge at same step 4-way SSE example (SIMD) vector registerxmm1 xmm0 vector operationaddps xmm0, xmm1 xmm0

  14. Variant Synthesis with SPIRAL • Symbolic process[Puschel2005]: • pattern based matrix vector product code … case 1: case 2: case N: SPL Compiler

  15. Multithreading • Multithreading, Run across All CPUs • Vectorized load flow solver in each thread • Each thread pinned to a physical core exclusively • Fully utilize computation power of Multi-core CPUs • Double buffer (automatic load balancing for MCS application) (Multi-Core)

  16. Performance Results: Across Sizes • Performance of Optimized Code, Mass Amount Load Flow Pseudo flop/s: >60 % peak Flop/s: 50% Peak

  17. Details: Performance Gains >50x >20x

  18. Performance Results: Across Machines

  19. Accuracy • Convergence of Monte Carlo • Very crude. MCG59+ICDF, 50 trials with “time(NULL)” seeds Out: Voltage on Node 738 In: Active Power P~ u=0,std=100kw on Phase A of Node 738,711,741

  20. Outline • Motivation • Distribution System Load Flow Analysis • Code Optimization • Real Time Implementation • Conclusion

  21. System Implementation • Distribution System Probabilistic Monitoring System (DSPMS) • System Structure: • MCS solver running on Multi-core Desktop Server (Code optimization) • Results published via ECE Web Server (TCP/IP socket) • Web based dynamic User Interface by client side scripts (JavaScript) • Smart meters in campus building (MODBUS/TCP)

  22. System Implementation • Distribution System Probabilistic Monitoring System (DSPMS) • Web Server and User Interface • Link: www.ece.cmu.edu/~tcui/test/DistSim/DSPMS.htm

  23. Conclusion • Smart Distribution Network: Impact of renewable and stochastic • Commodity HPC & code optimization: Millions of cases /sec on $1K class machine • Distribution System Probabilistic Monitor: A prove of concept real time application source: LBNL-3884e

  24. References • [LBNL-3884E]. Mills, A. Implications of Wide-Area Geographic Diversity for Short-Term Variability of Solar Power, LBNL-3884E. Lawrence Berkeley National Laboratory, Berkeley, • [ORNL/TM2004/291]. B. Kirby, "Frequency Regulation Basics and Trends," ORNL/TM 2004/291, Oak Ridge National Laboratory, December 2004. • [Pantos2011]. Miloš Pantoš Stochastic optimal charging of electric-drive vehicles with renewable energy  Energy, Volume 36, Issue 11, November 2011 • [Ghosh97]. A.K. Ghosh, D. L. Lubkeman, M. J. Downey, R. H. Jones “Distribution Circuit State Estimation Using a Probabilistic Approach,” IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 45-51, 1997 • [Belgin2009]. M. Belgin, G. Back, and C. J. Ribbens, “Pattern-based sparse matrix representation for memory-efficient smvm kernels,” in Proceedings of the 23rd international conference on Supercomputing, ser. ICS ’09. New York, NY, USA: ACM, 2009, pp. 100–109. • [Puschel2005]. M. Puschel, J. M. F. Moura, J. Johnson, D. Padua, M. Veloso, B. Singer, J. Xiong, F. Franchetti, A. Gacic, Y. Voronenko, K. Chen, R. W. Johnson, and N. Rizzolo, “SPIRAL: Code generation for DSP transforms,” Proceedings of the IEEE, special issue on “Program Generation, Optimization, and Adaptation”, vol. 93, no. 2, pp. 232– 275, 2005. • [Kersting2006]. W. Kersting, Distribution system modeling and analysis. CRC, 2006

  25. The End Thank You! Q&A

More Related