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Lattice Boltzmann for Blood Flow: A Software Engineering Approach for a DataFlow SuperComputer

Nenad Korolija , nenadko@etf.rs Tijana Djukic , tijana@kg.ac.rs Nenad Filipovic , nfilipov@hsph.harvard.edu Veljko Milutinovic , vm@etf.rs. Lattice Boltzmann for Blood Flow: A Software Engineering Approach for a DataFlow SuperComputer. MyWork in a NutShell.

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Lattice Boltzmann for Blood Flow: A Software Engineering Approach for a DataFlow SuperComputer

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  1. NenadKorolija, nenadko@etf.rs TijanaDjukic, tijana@kg.ac.rsNenadFilipovic, nfilipov@hsph.harvard.edu VeljkoMilutinovic, vm@etf.rs Lattice Boltzmann for Blood Flow:A Software Engineering Approachfor a DataFlowSuperComputer

  2. MyWork in a NutShell • Introduction: Synergy of Physics and Logics • Problem: Moving LB to Maxeler • ExistingSolutions: None :) • Essence: Map+Opt(PACT) • Details: MyPhD • Analysis: BaU • Conclusions: 1000 (SPC)

  3. Cooperation between BioIRC, UniKG and School of Electrical Engineering, UniBG

  4. Lattice Boltzmann for Blood Flow:A Software Engineering Approach • Expensive • Quiet • Fast • Electrical • 20m cord • Environment-friendly • Big-pack • Wide-track • Easy handling • Reparation manual • Reparation kit • 5Y warranty • Service in your town • New-technology high-quality non-rusting heavy-duty precise-cutting recyclable blades streaming grass only to bag ...

  5. Lattice Boltzmann for Blood Flow:A Software Engineering Approach Expensive Quiet Electrical 20m cord Environment-friendly Big-pack Wide-track Easy handling Reparation manual Reparation kit 5Y warranty Service in your town New-technology high-quality non-rusting heavy-duty precise-cutting recyclable blades streaming grass only to bag ...

  6. Lattice Boltzmann for Blood Flow:A Software Engineering Approach

  7. Structure of the Existing C-Codefor a MultiCore Computer • LS1 LS2 LS3 LS4 LS5 • Statically: P / T = 100 / 400 = 25% => Only 100 lines to “kernelize” • Dynamically: P / T = 99%=> Potential speed-up factor is at most 100 LS – Looping structure LS1 and LS5 – Nested loops LS2, LS3, and LS4 – Simple loops P – lines to parallelize T – total number of lines

  8. What Looping Structures to “Kernelize” • All,because we like all datato reside on MAX3prior to the execution start MAX MAX MAX MAX MAX MAX CPU CPU CPU CPU CPU CPU

  9. What Looping StructuresBring what Benefits? • LS1 moderate • LS2, LS3, LS4negligible,but must “kernelize” • LS5 major FOR i = 1 2 3 4 5 … k … n DO FOR i = 1 2 3 4 5 … n DO T0 T1 T2 T3 T4 T0Tk T2k T3k OP1 OP1 OP2 OP2 OP3 OP3 OP4 OP4 OP5 OP5 OP6 OP6 . . . . . . OPkOPk Tk Tk+1 Tk+2 Tk T2k 1 result/clockMAX T3k T4k 1 result/k*clockCPU DFE doing k operations CPU doing only one

  10. Why “Kernelizing” the Looping Structures?Conditions for “Kernelizing” Revisited

  11. Programming: Iteration #1 What to do with LS1..5? • Direct MultiCore Data Choreography 1, 2, 3, 4, ... • Direct MultiCore Algorithm Execution ∑∑ + ∑ + ∑ + ∑ + ∑∑ • Direct MultiCoreComputational Precision:Double Precision Floating Point (64 bits)

  12. Programming: Iteration #1 Potentials of Direct “Kernelization” • Amdahl Low: limes(DFE Potential → ∞) = 100 • Reality Estimate: limes(work → 30.6.2013.)= N 1% 99% 1% 0% 1% x%

  13. Pipelining the Inner Loops inputs 0 Kernel(s) Stream Middle FunctionsKernels Manager Kernel j Kernel(s) Collide 320 0 112 i output

  14. The Kernel for LS1:Direct Migration • public class LS1Kernel extends Kernel { • public LS1Kernel(KernelParameters parameters) { • super(parameters); • // Input • HWVar f1new = io.scalarInput("f1new" ,hwFloat(8, 24)); • HWVar f5new = io.scalarInput("f5new" ,hwFloat(8, 24)); • HWVar f8new = io.scalarInput("f8new" ,hwFloat(8, 24)); • HWVar f1 = io.input("f1", hwFloat(8, 24)); // j • HWVar f2m = io.input("f2m", hwFloat(8, 24)); // j-1 • HWVar f3 = io.input("f3", hwFloat(8, 24)); // j • HWVar f4p = io.input("f4p", hwFloat(8, 24)); // j+1 • HWVar f5m = io.input("f5m", hwFloat(8, 24)); // j-1 • HWVar f6m = io.input("f6m", hwFloat(8, 24)); // j-1 • HWVar f7p = io.input("f7p", hwFloat(8, 24)); // j+1 • HWVar f8p = io.input("f8p", hwFloat(8, 24)); // j+1

  15. The Kernel for LS5: Direct Migration • // Do the summations needed to evaluate the density and components of velocity • HWVarro = f0 + f1 + f2 + f3 + f4 + f5 + f6 + f7 + f8; • HWVarrovx = f1 - f3 + f5 - f6 - f7 + f8; • HWVarrovy = f2 - f4 + f5 + f6 - f7 - f8; • HWVarvx = rovx/ro; • HWVarvy = rovy/ro; • // Also load the velocity magnitude into plotvar - this is what we will • // display using OpenGL later • HWVar v2x = vx * vx; • HWVar v2y = vy * vy; • HWVarplotvar = KernelMath.sqrt(v2x + v2y); • HWVarv_sq_term = 1.5f*(v2x + v2y); • // Evaluate the local equilibrium f values in all directions • HWVarvxmvy = vx - vy; • HWVarvxpvy = vx + vy; • HWVarrortau = ro * rtau; • HWVar rortaufaceq2 = rortau * faceq2; • HWVar rortaufaceq3 = rortau * faceq3; • HWVar vxpvyp3 = 3.f*vxpvy; • HWVar vxmvyp3 = 3.f*vxmvy; • HWVar vxp3 = 3.f*vx; • HWVar vyp3 = 3.f*vy; • HWVar v2xp45 = 4.5f*v2x; • HWVar v2yp45 = 4.5f*v2y; • HWVarmv_sq_term = 1.f - v_sq_term; • HWVar mv_sq_termpv2xp45 = mv_sq_term + v2xp45; • HWVar mv_sq_termpv2yp45 = mv_sq_term + v2yp45; • HWVar vxpvyp45vxpvy = 4.5f*vxpvy*vxpvy; • HWVar vxmvyp45vxmvy = 4.5f*vxmvy*vxmvy; • HWVar mv_sq_termpvxpvyp45vxpvy = mv_sq_term + vxpvyp45vxpvy; • HWVar mv_sq_termpvxmvyp45vxmvy = mv_sq_term - vxmvyp45vxmvy; • HWVar f0eq = rortau * faceq1 * mv_sq_term; • HWVar f1eq = rortaufaceq2 * (mv_sq_termpv2xp45 + vxp3); • HWVar f2eq = rortaufaceq2 * (mv_sq_termpv2yp45 + vyp3); • HWVar f3eq = rortaufaceq2 * (mv_sq_termpv2xp45 - vxp3); • HWVar f4eq = rortaufaceq2 * (mv_sq_termpv2yp45 - vyp3); • HWVar f5eq = rortaufaceq3 * (mv_sq_termpvxpvyp45vxpvy + vxpvyp3); • HWVar f6eq = rortaufaceq3 * (mv_sq_termpvxmvyp45vxmvy - vxmvyp3); • HWVar f7eq = rortaufaceq3 * (mv_sq_termpvxpvyp45vxpvy - vxpvyp3); • HWVar f8eq = rortaufaceq3 * (mv_sq_termpvxmvyp45vxmvy + vxmvyp3);

  16. Programming: Iteration #2 Ideas for Additional Speedup (a) • Better Data Choreography • 5x x 5x • Estimate: 1.2 X Speed-up (as seen from the drawing above)

  17. Programming: Iteration #3 Ideas for Additional Speedup (b) • Algorithmic Changes:∑∑ + ∑ + ∑ + ∑ + ∑∑ → ∑∑ + ∑ + ∑∑ • Explanation: As seen from the previous drawing,LS2 and LS3 can be integrated with LS1 • Estimate: 1.6

  18. Programming: Iteration #4 Ideas for Additional Speedup (c) • Precision Changes:LUT (Double-precision floating point, 64) = 500LUT (Maxeler-precision floating point, 24) = 24 • Explanation:With less precision,hardware complexity can be reduced by a factor of about 20.Increasing number of iterations 4 timesbrings approximately similar precision, much faster. • Estimate: Factor = (500/24)/4 ≈ 5 • This is the only action,before which an topic expert has to be consulted!

  19. Lattice Boltzman http://www.youtube.com/watch?v=vXpCC3q0tXQ

  20. Results: SPTC≈1000x“Maxeler’s technology enables organizations to speed up processing times by 20-50x,with over 90% reduction in energy usage and over 95% reduction in data centre space”. • Speedup factor: 1.2 x 1.6 x 5 x N ≈ 10N- Precisely 30.6.2013. • Power reduction factor(i7/MAX3) =17.6 / (MAX2 / MAX3) ≈ 10- Precisely: the WallCordmethod • Transistor count reduction factor = i7 / MAX3- Precisely: about 20 • Cost reduction factor: x- Precisely: depends on production volumes

  21. Q&A: nenadko@etf.rs 10km/h ! 30km/h !!! Hawaii Tahiti

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