A first evaluation of dynamic configuration of load-balancers for AMR simulations of flows

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1 A first evaluation of dynamic configuration of load-balancers for AMR simulations of flows Henrik Johansson, Johan Steensland and Jaideep Ray Uppsala University, Uppsala and Sandia National Laboratories, Livermore July 18 th, 2007

Johansson, Johan Steensland and Jaideep Ray Uppsala

2 Aim of the project To dynamically choose & configure load-balancers for component-based time-dependent block-structured AMR simulations The load-balancer algorithm depends on the mesh geometry The configuration parameters for a given algorithm depends on frequency of communication versus amount of communication (transport v/s reactive compute, degree of implicitness of the time-integrator, elliptic nature of the problem etc) The mathematical nature of the problem and geometry of the mesh change with time as the simulation proceeds, and the loadbalancer is invoked repeated (dynamic)

versus amount of communication (transport v/s reactive compute, degree of implicitness of the time-integrator, elliptic nature of the problem etc)

3 What does this require? A component-based time-dependent AMR simulation suffering from partitioning problems A stable of partitioners that can potentially solve the problem, provided the right partitioner could be identified A control system that can do the trick. This would contain: A control law, mapping application state to the correct partitioner configurations A feedback system, for stability A software architecture general enough to accommodate various simulations (i.e. various AMR grid packages) various load-balancer libraries Various control laws, since definitions of application states for unstructured meshes and block-structured ones are different as configuration tuples for different partitioners

partitioner could be identified A control system that can do the trick.

4 What do we have today? Jaideep s component based simulations of reactive and shockdriven flows Steensland s Nature+Fable stable of configurable loadbabancers The beginnings of a control law A 4-tuple characterization of a mesh (Μ) for partitioning purposes (Steensland & Ray, 2003) The 6-tuple characterization of Nature+Fable (N+F) partitioners (Λ) A database of characterization of N+F partitioners on multiple timesteps dumped from 4 different applications (not mine) i.e. a database of ( Π, P ) where Π = Μ U Λ P = 3-tuple {imbalance, compute time, communication time}

beginnings of a control law A 4-tuple characterization of a mesh (Μ) for partitioning purposes (Steensland & Ray, 2003) The 6-tuple

5 First crack at the control system.. Goal: What would the software architecture of a control system look like? Could it be demonstrated that a control system would improve things somewhat e.g. vis-à-vis default partitioner settings? Not to be addressed: Feedback loop design The optimal Π Further restrictions Make do with mesh traces rather than actual simulations Each simulation takes time! Make do with a coarse ( Π, P ) database We aren t looking for the optimal Π.

Not to be addressed: Feedback loop design The optimal Π Further restrictions Make do with mesh traces rather than

6 Methodology For a given mesh from a timestep from a simulation Identify the mesh characteristics tuple Μ k From the ( Π, P ) database find all entries Π l, l ε L, where Μ l is close to Μ k Predict the performance P k, using Π k = Μ k U Λ l and interpolating the table values Pick the best Λ l, Λ l, max as the partitioner configuration Metric of success P k, max obtained from running the partitioner with Λ l, max should be better than that obtained from default parameter settings Note: P k, max need not be optimal the database is coarse P k, max need should beat default settings every time To be realistic, Λ l, max should change every time the partitioner is invoked

configuration Metric of success P k, max obtained from running the partitioner with Λ l, max should be better than that obtained from default parameter settings Note:

7 Workflow

8 Architecture An Init component to read in a mesh and characterize it A Communication and Transformation (CoT) component that converts the mesh from the AMR framework form to an intermediate form used by the partitioner A Core component that chooses and configures the partitioner and produces the partitions The partitions are handed back to CoT to be translated back into a form understood by the AMR framework The stable of partitioners i.e. Nature+Fable The performance database i.e. the table of ( Π, P )

and configures the partitioner and produces the partitions The partitions are handed back to CoT to be translated back into a

9 CCA Components

10 Preliminary results, focussynch1_25

11 Preliminary Results, focussynch1_25

12 Conclusions A first cut, lots more to be done The database ought to be more resolved The control system is about as good as the default values because the database is simply not detailed enough But this takes time, Π is high-dimensional We could cluster values taken from the database and try a better way of predicting performance P k Check whether the database is general enough The database was generated using a training set of nonreacting flows; use it with a reacting flows simulation This is being done

values taken from the database and try a better way of predicting performance P k Check whether the database is general enough

The scalability impact of a component-based software engineering framework on a growing SAMR toolkit: a case study

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