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1/21 Lattice Boltzmann for Blood Flow: A Software Engineering Approach for a DataFlow SuperComputer Nenad Korolija, Tijana Djukic,

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Presentation on theme: "1/21 Lattice Boltzmann for Blood Flow: A Software Engineering Approach for a DataFlow SuperComputer Nenad Korolija, Tijana Djukic,"— Presentation transcript:

1 1/21 Lattice Boltzmann for Blood Flow: A Software Engineering Approach for a DataFlow SuperComputer Nenad Korolija, Tijana Djukic, Nenad Filipovic, Veljko Milutinovic,

2 MyWork in a NutShell 1.Introduction: Synergy of Physics and Logics 2.Problem: Moving LB to Maxeler 3.ExistingSolutions: None :) 4.Essence: Map+Opt(PACT) 5.Details: MyPhD 6.Analysis: BaU 7.Conclusions: 1000 (SPC) 2/21

3 Cooperation between BioIRC, UniKG and School of Electrical Engineering, UniBG 3/21

4 4/21 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 5/21 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 6/21

7 7/21 Structure of the Existing C-Code for 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 8/21 What Looping Structures to “Kernelize” All, because we like all data to reside on MAX3 prior to the execution start MAX CPU MAX CPU MAX CPU MAX CPU MAX CPU MAX CPU

9 9/21 What Looping Structures Bring what Benefits? LS1 moderate LS2, LS3, LS4 negligible, but must “kernelize” LS5 major FOR i = … k … n DO FOR i = … n DO T 0 T 1 T 2 T 3 T 4 T 0 T k T 2k T 3k OP1 OP2 OP3 OP4 OP5 OP6. OPk T k T k+1 T k+2 T k T 2k 1 result/clock MAX T 3k T 4k 1 result/k*clock CPU DFE doing k operations CPU doing only one

10 10/21 Why “Kernelizing” the Looping Structures? Conditions for “Kernelizing” Revisited Why?LS1LS2/3/4LS5 1.BigData O(n 2 ) 2.WORM+++ 3.Tolerance to latency+++ 4.Over 95% of run time in loops++ 5.Reusability of the data++ 6.Skills++++

11 11/21 Programming: Iteration #1 What to do with LS1..5? Direct MultiCore Data Choreography 1, 2, 3, 4,... Direct MultiCore Algorithm Execution ∑∑ + ∑ + ∑ + ∑ + ∑∑ Direct MultiCore Computational Precision: Double Precision Floating Point (64 bits)

12 12/21 Programming: Iteration #1 Potentials of Direct “Kernelization” Amdahl Low: limes(DFE Potential → ∞) = 100 Reality Estimate: limes(work → ) = N 99%1%1% 0% 1% x%x%

13 13/21 Pipelining the Inner Loops j i inputs output Kernel Kernel(s) Stream Middle Functions Kernels Kernel(s) Collide Manager

14 14/21 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 15/21 The Kernel for LS5: Direct Migration // Do the summations needed to evaluate the density and components of velocity HWVar ro = f0 + f1 + f2 + f3 + f4 + f5 + f6 + f7 + f8; HWVar rovx = f1 - f3 + f5 - f6 - f7 + f8; HWVar rovy = f2 - f4 + f5 + f6 - f7 - f8; HWVar vx = rovx/ro; HWVar vy = 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; HWVar plotvar = KernelMath.sqrt(v2x + v2y); HWVar v_sq_term = 1.5f*(v2x + v2y); // Evaluate the local equilibrium f values in all directions HWVar vxmvy = vx - vy; HWVar vxpvy = vx + vy; HWVar rortau = 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; HWVar mv_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 16/21 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 17/21 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 18/21 Programming: Iteration #4 Ideas for Additional Speedup (c) Precision Changes: LUT (Double-precision floating point, 64) = 500 LUT (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 times brings 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 19/21 Lattice Boltzman

20 20/21 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 Power reduction factor(i7/MAX3) = 17.6 / (MAX2 / MAX3) ≈ 10 - Precisely: the WallCord method Transistor count reduction factor = i7 / MAX3 - Precisely: about 20 Cost reduction factor: x - Precisely: depends on production volumes

21 Q&A: Hawaii Tahiti 10km/h ! 30km/h !!! 21/21


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