1. Impact of the Cardiac Heart Flow Alpha Project Kathy Yelick EECS Department U.C. Berkeley

2. Outline Vision of a Digital Human Applications of the IBM –The Heart Model –The Cochlea Model –Others Overview of the Immersed Boundary Method The Alpha project –Solvers –Automatic tuning (FFT vs. MG) –Heart model Short term future directions

3. Simulation: The Third Pillar of Science Traditional scientific and engineering paradigm: 1)Do theory or paper design. 2)Perform experiments or build system. Limitations: –Too difficult -- build large wind tunnels. –Too expensive -- build a throw-away passenger jet. –Too slow -- wait for climate or galactic evolution. –Too dangerous -- drug design. Computational science paradigm: 3)Use high performance computer systems to simulate the phenomenon.

4. Economics of Large Scale Simulation Automotive design: –Crash and aerodynamics simulation (500+ CPUs). –Savings: approx. $1 billion per company per year. Semiconductor industry: –Device simulation and logic validation (500+ CPUs). –Savings: approx. $1 billion per company per year. Airlines: –Logistics optimization on parallel system. –Savings: approx. $100 million per airline per year. Securities industry: –Home mortgage investment and risk analysis. –Savings: approx. $15 billion per year. What about health care, which is 20% of GNP? Source: David Bailey, LBNL

5. From Visible Human to Digital Human Source: John Sullivan et al, WPI Source: www.madsci.org Building 3D Models from images

6. Heart Simulation Calculation Developed by Peskin and McQueen at NYU –Done on a Cray C90: 1 heart-beat in 100 hours –Used for evaluating artificial heart valves –Scalable parallel version done here Implemented in a high performance Java dialect –Model also used for: Inner ear Blood clotting Embryo growth Insect flight Paper making

7. Simulation of a Heart

8. Simulation and Medicine Imagine a “digital body double” –3D image-based medical record –Includes diagnostic, pathologic, and other information Used for: –Diagnosis –Less invasive surgery-by-robot –Experimental treatments Where are we today?

9. Digital Human Roadmap 1995200020052010 1 organ 1 model scalable implementations 1 organ multiple models multiple organs 3D model construction better algorithms organ system coupled models 100x effective performance improved programmability

10. Last Year

11. Project Summary Provide easy-to-use, high performance tool for simulation of fluid flow in biological systems. –Using the Immersed Boundary Method Enable simulations on large-scale parallel machines. –Distributed memory machine including SMP clusters Using Titanium, ADR, and KeLP with AMR Specific demonstration problem: Simulation of the heart model on Blue Horizon.

12. Outline Short term goals and plans Technical status of project –Immersed Boundary Method –Software Tools –Solvers Next Steps

13. Short Term Goals for October 2001 IB Method written in Titanium (IBT) IBT Simulation on distributed memory Heart model input and visualization support in IBT Titanium running on Blue Horizon IBT users on BH and other SPs ?Performance tuning of code to exceed T90 performance ?Replace solver with (adaptive) multigrid

14. IB Method Users Peskin and McQueen at NYU –Heart model, including valve design At Washington –Insect flight Fauchy et al at Tulane –Small animal swimming Peter Kramer at RPI –Brownian motion in the IBM John Stocky at Simon Fraser –Paper making Others –parachutes, flags, flagellates, robot insects

15. Building a User Community Many users of the IB Method Lots of concern over lack of distributed memory implementation Once IBT is more robust and efficient (May ’01), advertise to users Identify 1 or 2 early adopters Longer term: workshop or short course

16. Long Term Software Release Model Titanium –Working with UPC and possibly others on common runtime layer –Compiler is relatively stable but needs ongoing support IB Method –Release Titanium source code –Parameterized “black box” for IB Method with possible cross-language support Visualization software is tied to SGI

17. Immersed Boundary Method Developed at NYU by Peskin & McQueen to model biological systems where elastic fibers are immersed in an incompressible fluid. –Fibers (e.g., heart muscles) modeled by list of fiber points –Fluid space modeled by a regular lattice

18. Immersed Boundary Method Structure Fiber activation & force calculation Interpolate Velocity Navier-Stokes Solver Spread Force 4 steps in each timestep Fiber Points Interaction Fluid Lattice

19. Challenges to Parallelization Irregular fiber lists need to interact with regular fluid lattice. –Trade-off between load balancing of fibers and minimizing communication Efficient “scatter-gather” across processors Need a scalable elliptic solver –Plan to uses multigrid –Eventually add Adaptive Mesh Refinement New algorithms under development by Colella’s group

20. Tools used for Implementation Titanium supports –Classes, linked data structures, overloading –Distributed data structures (global address space) –Useful for planned adaptive hierarchical structures ADR provides –Help with analysis and organization of output –Especially for hierarchical data KeLP provides –Alternative programming model for solvers ADR and KeLP are not critical for first-year

21. Titanium Status Titanium runs on uniprocessors, SMPs, and distributed memory with a single programming model It has run on Blue Horizon –Issues related to communication balance –Revamped backends are more organized, but BH backend not working right now Need to replace personnel

22. Solver Status Current solver is based on 3D FFT Multigrid might be more scalable Multigrid with adaptive meshes might be more so Balls and Colella algorithm could also be used KeLP implementations of solvers included Note: McQueen is looking into solver issues for numerical reasons unrelated to scaling Not critical for first year goals

23. IB Titanium Status IB (Generic) rewritten in Titanium. Running since October Contractile torus –runs on Berkeley NOW and SGI Origin Needed for heart: –Input file format –Performance tuning Uniprocessor (C code used temporarily in 2 kernels) Communication

24. Immersed Boundary on Titanium Performance Breakdown (torus simulation):

25. Immersed Boundary on Titanium

26. Next Steps Improve performance of IBT Generate heart input for IBT Recover Titanium on BH Identify early user(s) of IBT Improve NS solver Add functionality –Bending angles, anchorage points, source & sinks) to the software package.

27. Yelick(UCB), Peskin (NYU), Colella (LBNL), Baden (UCSD), Saltz (Maryland) Adaptive Computations for Fluids in Biological Systems Immersed Boundary Method Applications Human Heart (NYU) Embryo Growth (UCB) Blood Clotting (Utah) Robot Insect Flight (NYU) Pulp Fibers (Waterloo) Generic Immersed Boundary Method (Titanium) Heart (Titanium) Insect Wings Flagellate Swimming … Spectral (Titanium) Multigrid (KeLP) AMR Application Models Extensible Simulation Solvers

28. General Questions - How has your project addressed the goals of the PACI program (providing access to tradition HPC, providing early access to experimental systems, fostering interdisciplinary research, contributing to intellectual development, broadening the base)? - What infrastructure products (e.g., software, algorithms, etc.) have you produced? - Where have you deployed them (on NPACI systems, other systems)? - What have you done to communicate the availability of this infrastructure? - What training have you done? - What kind/size of community is using your infrastructure? - How have you integrated your work with EOT activities? - What scientific accomplishments - or other measurable impacts not covered by answers to previous questions - have resulted from its use? - What are the emerging trends/technologies that NPACI should build on/leverage? - How can we increase the impact of NPACI development to date? - How can we increase the community that uses the infrastructure you've developed?

29. Greg’s Slides

30. Scallop A latency tolerant elliptical solver library Implemented in KeLP, with a simple interface Still under development

31. Elliptical solvers A finite-difference based solvers –Good for regular, block-structured domains Method of Local Corrections –Local solutions corrected by a coarse solution –Good accuracy, well-conditioned solutions Limited communication –Once to generate coarse grid values –Once to correct local solutions

32. KeLP implementation Advantages –abstractions available in C++ –built in domain calculus –communication management –numerical kernels written in Fortran Simple interface –callable from other languages –no KeLP required in user code

33. A Finite Difference Domain Decomposition Method Using Local Corrections for the Poisson Equation Greg Balls University of California, Berkeley

34. The Poisson Equation We are interested in the solution to A particular solution to this equation is

35. Infinite Domain Boundary Conditions We can write our infinite domain boundary condition as These boundary conditions specify a unique solution.

36. The Discretized Problem We would like an approximate solution

37. Solving the Discretized Problem We could calculate the convolution integral at each point Multigrid provides a faster method

38. A Standard Finite Difference Discretization With a discretization of the Laplacian, e.g. We solve the discretized equation

39. A Finite Difference Approach for the Infinite Domain Problem A discrete solution can be found in three steps: 1.Solve a multigrid problem with homogeneous Dirichlet boundary conditions. 2.Do a potential calculation to set accurate inhomogeneous Dirichlet boundary conditions. 3.Solve a second multigrid problem with these boundary conditions.

40. A Finite Difference Approach for the Infinite Domain Problem The first multigrid solution:

41. A Finite Difference Approach for the Infinite Domain Problem The potential calculation:

42. A Finite Difference Approach for the Infinite Domain Problem The second multigrid solution:

43. Domain Decomposition We would like to solve this problem in parallel, calculating  h such that A basic domain decomposition strategy: Do until converged - Break into pieces. Solve on each piece. Compute coupling.

44. Domain Decomposition Options Point relaxation –Too much communication and too much computation. Multigrid –Less computation, but still too much communication. Finite element domain decomposition –Less communication, but still iterative.

45. The Importance of Communication Current parallel machines can do many floating point operations in the time that it takes to send a message. This imbalance will get worse.

46. Fast Particle Methods Methods such as FMM and MLC need no iteration. They take advantage of the fact that the local and far-field solutions are only weakly coupled.

47. A Method of Local Corrections for Finite Difference Calculations The basic strategy: –Break into pieces. –Solve on each piece. –Compute coupling through a single coarse solution. –Compute the corrected solution on each piece.

48. The Initial Solution An infinite domain solution is found on each piece, l The effects of all other pieces are ignored.

49. A Coarse Grid Charge A coarse grid charge is computed for each piece.

50. The Global Coarse Solution All the individual coarse grid charges are combined on a global coarse grid. A global coarse solution is found.

51. Setting Accurate Boundary Conditions The interpolation stencil only interpolates far-field information.

52. Setting Accurate Boundary Conditions The coarse stencil information is interpolated to a corresponding fine grid stencil to O(H 4 ). Local information is added from nearby fine grids.

53. The Corrected Solution Once the boundary conditions have been set for each piece, we solve one last time with multigrid: The full solution is then

54. How Accuracy Is Maintained Local error is only O(h 2 ). Error in the global coarse solution is O(H 4 ). The coarse solution is accurate to O(H 4 ) because of the error of the L 9 discretization.

55. Scaling for Accuracy and Performance We can scale the coarse and fine grids as The coarse grid solve represents much less work than the work done on the fine grids.

56. The Titanium Programming Language Titanium is a new language designed for scientific computing on parallel architectures. –SPMD parallelism. –A dialect of Java, compiled to native code. –Language support for scientific computing.

57. The Benefits of Titanium An object-oriented language with built- in support for fast, multi-dimensional arrays. Language support for –Tuples (Points). –Rectangular regions (RectDomains). –Expressing array bounds as RectDomains and indexing arrays by Points. A global address space

58. Accuracy of the Method Grid SizeNpNp NrNr Max ErrorL 2 ErrorMax Convergenc e L 2 Convergenc e 257248.61e-82.18e-8 257288.51e-82.13e-8 257488.25e-82.02e-8 2574167.23e-81.77e-8 513242.02e-85.32e-92.222.06 513282.01e-85.26e-92.232.06 513481.96e-85.05e-92.322.18 5134161.67e-84.12e-92.422.30

59. Error on a Large, High-Wavenumber Problem

60. Scalability of the Method Results from the SDSC IBM SP-2:

61. Scalability of the Method Results from the NERSC Cray T3E:

62. Future Work Extension to three dimensions. Implementation of different boundary conditions. Use in other solvers such as: –Euler. –Navier-Stokes.

63. Conclusions The method is second-order accurate. The method does not iterate between the local fine representations and the global coarse grid. The need for communication is kept to a minimum. The method is scalable.

64. Comparison to the Serial Method Extra computational costs: –The time spent on the coarse grid solution can be kept to less than 10% of time spent on the local fine grids. –The final multigrid solution adds 40% more fine grid work. Communication costs: –Experimentally, less than 1% of the total time