component frameworks:

PPL-Dept of Computer Science, UIUC

Component Frameworks:

Laxmikant (Sanjay) KaleParallel Programming Laboratory

Department of Computer Science

University of Illinois at Urbana-Champaign

http://charm.cs.uiuc.edu


Motivation

• Parallel Computing in Science and Engineering– Competitive advantage

– Pain in the neck

– Necessary evil

• It is not so difficult– But tedious, and error-prone

– New issues: race conditions, load imbalances, modularity in presence of concurrency,..

– Just have to bite the bullet, right?


But wait…

• Parallel computation structures– The set of the parallel applications is diverse and

complex

– Yet, the underlying parallel data structures and communication structures are small in number

• Structured and unstructured grids, trees (AMR,..), particles, interactions between these, space-time

• One should be able to reuse those– Avoid doing the same parallel programming again and

again

– Domain specific frameworks


A Unique Twist

• Many apps require dynamic load balancing– Reuse load re-balancing strategies

• It should be possible to separate load balancing code from application code

• This strategy is embodied in Charm++– Express the program as a collection of interacting

entities (objects).

– Let the system control mapping to processors


Multi-partition decomposition

• Idea: divide the computation into a large number of pieces– Independent of number of processors

– typically larger than number of processors

– Let the system map entities to processors


Object-based Parallelization

User View

System implementation

User is only concerned with interaction between objects


Charm Component Frameworks

Object based decomposition

Reuse of Specialized

Parallel Strucutres

Component Frameworks

Load balancing

Auto. Checkpointing

Flexible use of clusters

Out-of-core execn.


Goals for Our Frameworks

• Ease of use:– C++ and Fortran versions

– Retain “look-and-feel” of sequential programs

– Provide commonly needed features

– Application-driven development

– Portability

• Performance:– Low overhead

– Dynamic load balancing via Charm++

– Cache performance


Current Set of Component Frameworks

• FEM / unstructured meshes:– “Mature”, with several applications already

• Multiblock: multiple structured grids– New, but very promising

• AMR:– Oct and Quad-trees


Charm++

Converse

Load database + balancer

MPI-on-Charm Irecv+

AutomaticConversion from

MPI

FEM Structured

Cross module interpolation

Migration path

Frameworkpath

Using the Load Balancing Framework


begin time loop

compute forces

update node positions

end time loop

begin time loop

compute forces

communicate shared nodes


end time loopSerial Code

for entire meshFramework Code for mesh partition

Finite Element Framework Goals

• Hide parallel implementation in the runtime system

• Allow adaptive parallel computation and dynamic automatic load balancing

• Leave physics and numerics to user

• Present clean, “almost serial” interface:


FEM Framework: Responsibilities

Charm++(Dynamic Load Balancing, Communication)

FEM Framework(Update of Nodal properties, Reductions over nodes or partitions)

FEM Application(Initialize, Registration of Nodal Attributes, Loops Over Elements, Finalize)

METIS I/O

Partitioner Combiner


Structure of an FEM Program

• Serial init() and finalize() subroutines– Do serial I/O, read serial mesh and call FEM_Set_Mesh

• Parallel driver() main routine:– One driver per partitioned mesh chunk

– Runs in a thread: time-loop looks like serial version

– Does computation and call FEM_Update_Field

• Framework handles partitioning, parallelization, and communication


Structure of an FEM Application

init()

Update Update Update

finalize()

driver driver driver

Shared Nodes Shared Nodes


Framework Calls

• FEM_Set_Mesh – Called from initialization to set the serial mesh

– Framework partitions mesh into chunks

• FEM_Create_Field– Registers a node data field with the framework, supports user data types

• FEM_Update_Field– Updates node data field across all processors

– Handles all parallel communication

• Other parallel calls (Reductions, etc.)


Dendritic Growth

• Studies evolution of solidification microstructures using a phase-field model computed on an adaptive finite element grid

• Adaptive refinement and coarsening of grid involves re-partitioning


Crack Propagation

• Explicit FEM code

• Zero-volume Cohesive Elements inserted near the crack

• As the crack propagates, more cohesive elements added near the crack, which leads to severe load imbalance

Decomposition into 16 chunks (left) and 128 chunks, 8 for each PE (right). The middle area contains cohesive elements. Pictures: S. Breitenfeld, and P. Geubelle

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Iteration Number

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ber

of It

erat

ions

Per

sec

ond


Crack Propagation

Decomposition into 16 chunks (left) and 128 chunks, 8 for each PE (right). The middle area contains cohesive elements. Both decompositions obtained using Metis. Pictures: S. Breitenfeld, and P. Geubelle


“Overhead” of Multipartitioning

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Number of Chunks Per Processor

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Load balancer in action

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Iteration Number

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se

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dAutomatic Load Balancing in Crack Propagation

1. ElementsAdded 3. Chunks

Migrated

2. Load Balancer Invoked


Scalability of FEM Framework

Speedup of Crack Propagation

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Number of Processors

Spee

dup

Actual Speedup

Ideal Speedup


Scalability of FEM Framework

1.E-3

1.E-2

1.E-1

1.E+0

1.E+1

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Processors

Tim

e/S

tep

(s)

3.1M elements

1.5M Nodes

ASCI Red

1 processor time : 8.24 secs

1024 processors time:7.13 msecs


Parallel Collision Detection

• Detect collisions (intersections) between objects scattered across processors

Approach, based on Charm++ ArraysOverlay regular, sparse 3D grid of voxels (boxes)

Send objects to all voxels they touch

Collide voxels independently and collect results

Leave collision response to user code


Collision Detection Speed• O(n) serial performance

Good speedups to 1000s of processors

ASCI Red, 65,000 polygons per processor scaling problem

(to 100 million polygons)

Single Linux PC

2us per polygon serial performance


FEM: Future Plans

• Better support for implicit computations– Interface to Solvers: e.g. ESI (PETSC), ScaLAPACK

or POOMA’s Linear Solvers

• Better discontinuous Galerkin method support• Fully distributed startup• Fully distributed insertion

– Eliminate serial bottleneck in insertion phase• Abstraction to allow multiple active meshes

– Needed for multigrid methods


Multiblock framework

• For collection of structured grids– Older versions:

• (Gengbin Zheng, 1999-2000)

– Recent completely new version:

• Motivated by ROCFLO

– Like FEM:

• User writes driver subroutines that deal with the life-cycle of a single chunk of the grid

• Ghost arrays managed by the framework– Based on registration of data by the user program

• Support for “connecting up” multiple blocks– makemblock processes geometry info


Multiblock Constituents


Terminology


Mutiblock structure

• Steps:– Feed geometry information to makemblock

• Input: top level blocks, number of partitions desired

• Output: block file containing list of partitions, and communication structure

– Run parallel application

• Reads the block file

• Initialization of data

• Manual and info:• http://charm.cs.uiuc.edu/ppl_research/mblock/


Multiblock code example: main loop

do tStep=1,nSteps

call MBLK_Apply_bc_All(grid, size, err) call MBLK_Update_field(fid,ghostWidth,grid,err)

do k=sk,ek do j=sj,ej do i=si,ei ! Only relax along I and J directions-- not K newGrid(i,j,k)=cenWeight*grid(i,j,k) & &+neighWeight*(grid(i+1,j,k)+grid(i,j+1,k) &

&+grid(i-1,j,k)+grid(i,j-1,k)) end do end do end do


Multiblock Driver

subroutine driver()implicit noneinclude 'mblockf.h’… call MBLK_Get_myblock(blockNo,err) call MBLK_Get_blocksize(size,err)...call MBLK_Create_field(& &size,1, MBLK_DOUBLE,1,& &offsetof(grid(1,1,1),grid(si,sj,sk)),& &offsetof(grid(1,1,1),grid(2,1,1)),fid,err)

! Register boundary condition functions call MBLK_Register_bc(0,ghostWidth, BC_imposed, err) … Time Loopend


Multiblock: Future work

• Support other stencils– Currently diagonal elements are not used

• Applications– We need volunteers!

– We will write demo apps ourselves


Adaptive Mesh Refinement

• Used in various engineering applications where there are regions of greater interest– e.g.http://www.damtp.cam.ac.uk/user/sdh20/amr/amr.html

– Global Atmospheric modeling

– Numerical Cosmology

– Hyperbolic partial differential equations (M.J. Berger and J. Oliger)

• Problems with uniformly refined meshes for above– Grid is too fine grained thus wasting resources

– Grid is too coarse thus the results are not accurate

http://www.damtp.cam.ac.uk/user/sdh20/amr/amr.html




AMR Library

• Implements the distributed grid which can be dynamically adapted at runtime

• Uses the arbitrary bit indexing of arrays• Requires synchronization only before refinement

or coarsening• Interoperability because of Charm++• Uses the dynamic load balancing capability of the

chare arrays


Indexing of array elements

• Case of 2D mesh (4x4)

0,0,0

0,0,2 0,1,2 1,0,2 1,1,2

0,0,4 0,1,4 1,0,4 1,1,4 0,2,4 0,3,4 1,2,4 1,3,4

Node or root

Leaf

Virtual Leaf

Question: Who are my neighbors


Indexing of array elements (contd.)

• Mathematicaly: (for 2D)

if parent is x,y using n bits then,

child1 – 2x , 2y using n+2 bits

child2 – 2x ,2y+1 using n+2 bits

child3 – 2x+1, 2y using n+2 bits

child4 – 2x+1,2y+1 using n+2 bits


Pictorially

0,0,4


Communication with Nbors

• In dimension x the two nbors can be obtained by

- nbor --- x-1 where x is not equal to 0

+ nbor --- x+1 where x is not equal to 2n

• In dimension y the two nbors can be obtained by

- nbor --- y-1 where y is not equal to 0

+ nbor--- y+1 where y is not equal to 2n


Case 1

Nbors of 1,1,2

Y dimension : -nbor 1,0,2

0,0,4

0,0,0

0,0,2 0,1,2 1,0,2 1,1,2

0,1,4 1,0,4 1,1,4 0,2,4 0,3,4 1,2,4 1,3,4

Case 2Nbors of 1,1,2X dimension : -nbor 0,1,2

Case 3

Nbors of 1,3,4

X dimension : +nbor 2,3,4

Nbor of 1,2,4

X Dimension : +nbor 2,2,4


Communication (contd.)

• Assumption : The level of refinement of adjacent cells differs at maximum by one (requirement of the indexing scheme used)

• Indexing scheme is similar for 1D and 3D cases


AMR Interface

• Library Tasks- Creation of Tree

- Creation of Data at cells- Communication between cells- Calling the appropriate user routines in each iteration- Refining – Refine on Criteria (specified by user)

• User Tasks- Writing the user data structure to be kept by each cell- Fragmenting + Combining of data for the Neighbors- Fragmenting of the data of the cell for refine- Writing the sequential computation code at each cell


Some Related Work

• PARAMESH Peter MacNeice et al. http://sdcd.gsfc.nasa.gov/RIB/repositories/inhouse_gsfc/Users_manual/amr.htm

- This library is implemented in Fortran 90

- Supported on CrayT3E and SGI’s

• Parallel Algorithms for Adaptive Mesh Refinement, Mark T. Jones and Paul E. Plassmann, SIAM J. on Scientific Computing, 18,(1997) pp. 686-708. (Also MSC Preprint p 421-0394. )

http://www-unix.mcs.anl.gov/sumaa3d/Papers/papers.html

• DAGH-Dynamic Adaptive Grid Hierarchies– By Manish Parashar & James C. Browne– In C++ using MPI

http://sdcd.gsfc.nasa.gov/RIB/repositories/inhouse_gsfc/Users_manual/amr.htm











Future work

• Specialized version for structured grids– Integration with multiblock

• Fortran interface– Current version is C++ only

• unlike FEM and Multiblock frameworks, which support Fortran 90

– Relatively easy to do


Summary

• Frameworks are ripe for use– Well tested in some cases

• Questions and answers:– MPI libraries?

– Performance issues?

• Future plans:– Provide all features of Charm++


AMPI: Goals

• Runtime adaptivity for MPI programs– Based on multi-domain decomposition and dynamic

load balancing features of Charm++

– Minimal changes to the original MPI code

– Full MPI 1.1 standard compliance

– Additional support for coupled codes

– Automatic conversion of existing MPI programs

Original MPI Code AMPI Code

AMPI Runtime

AMPIzer


Charm++

• Parallel C++ with Data Driven Objects• Object Arrays/ Object Collections• Object Groups:

– Global object with a “representative” on each PE

• Asynchronous method invocation• Prioritized scheduling• Mature, robust, portable• http://charm.cs.uiuc.edu


Data driven execution

Scheduler Scheduler

Message Q Message Q


Load Balancing Framework

• Based on object migration and measurement of load information

• Partition problem more finely than the number of available processors

• Partitions implemented as objects (or threads) and mapped to available processors by LB framework

• Runtime system measures actual computation times of every partition, as well as communication patterns

• Variety of “plug-in” LB strategies available


Load Balancing Framework


Building on Object-based Parallelism

• Application induced load imbalances• Environment induced performance issues:

– Dealing with extraneous loads on shared m/cs

– Vacating workstations

– Automatic checkpointing

– Automatic prefetching for out-of-core execution

– Heterogeneous clusters

• Reuse: object based components• But: Must use Charm++!


AMPI: Goals• Runtime adaptivity for MPI programs

– Based on multi-domain decomposition and dynamic load balancing features of Charm++

– Minimal changes to the original MPI code

– Full MPI 1.1 standard compliance

– Additional support for coupled codes

– Automatic conversion of existing MPI programs

Original MPI Code AMPI Code

AMPI Runtime

AMPIzer


Adaptive MPI

• A bridge between legacy MPI codes and dynamic load balancing capabilities of Charm++

• AMPI = MPI + dynamic load balancing

• Based on Charm++ object arrays and Converse’s migratable threads

• Minimal modification needed to convert existing MPI programs (to be automated in future)

• Bindings for C, C++, and Fortran90

• Currently supports most of the MPI 1.1 standard


AMPI Features

• Over 70+ common MPI routines– C, C++, and Fortran 90 bindings

– Tested on IBM SP, SGI Origin 2000, Linux clusters

• Automatic conversion: AMPIzer– Based on Polaris front-end

– Source-to-source translator for converting MPI programs to AMPI

– Generates supporting code for migration

Very low “overhead” compared with native MPI

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AMPI Extensions

• Integration of multiple MPI-based modules– Example: integrated rocket simulation

• ROCFLO, ROCSOLID, ROCBURN, ROCFACE• Each module gets its own MPI_COMM_WORLD

– All COMM_WORLDs form MPI_COMM_UNIVERSE• Point-to-point communication among different

MPI_COMM_WORLDs using the same AMPI functions• Communication across modules also considered for balancing load

• Automatic checkpoint-and-restart– On different number of processors

– Number of virtual processors remain the same, but can be mapped to different number of physical processors


Charm++

Converse


Application Areas and Collaborations

• Molecular Dynamics: – Simulation of biomolecules

– Material properties and electronic structures

• CSE applications: – Rocket Simulation

– Industrial process simulation

– Cosmology visualizer

• Combinatorial Search:– State space search, game tree search, optimization


Molecular Dynamics

• Collection of [charged] atoms, with bonds• Newtonian mechanics• At each time-step

– Calculate forces on each atom

• Bonds:

• Non-bonded: electrostatic and van der Waal’s

– Calculate velocities and advance positions

• 1 femtosecond time-step, millions needed!• Thousands of atoms (1,000 - 100,000)


BC1 complex: 200k atoms


Performance Data: SC2000

Speedup on ASCI Red: BC1 (200k atoms)

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Processors

Spe

edup


Charm++

Converse

Load database + balancer

MPI-on-Charm Irecv+

AutomaticConversion from

MPI

FEM Structured

Cross module interpolation

Migration path

Frameworkpath

Component Frameworks:Using the Load Balancing Framework


Finite Element Framework Goals

• Hide parallel implementation in the runtime system• Allow adaptive parallel computation and dynamic

automatic load balancing• Leave physics and numerics to user• Present clean, “almost serial” interface:

begin time loop

compute forces


end time loop

begin time loop

compute forces

communicate shared nodes


end time loop

Serial Codefor entire mesh

Framework Code for mesh partition


FEM Framework: Responsibilities

Charm++(Dynamic Load Balancing, Communication)

FEM Framework(Update of Nodal properties, Reductions over nodes or partitions)

FEM Application(Initialize, Registration of Nodal Attributes, Loops Over Elements, Finalize)

METIS I/O

Partitioner Combiner


Structure of an FEM Application

init()

Update Update Update

finalize()

driver driver driver

Shared Nodes Shared Nodes


Dendritic Growth

• Studies evolution of solidification microstructures using a phase-field model computed on an adaptive finite element grid

• Adaptive refinement and coarsening of grid involves re-partitioning


Crack Propagation

Decomposition into 16 chunks (left) and 128 chunks, 8 for each PE (right). The middle area contains cohesive elements. Both decompositions obtained using Metis. Pictures: S. Breitenfeld, and P. Geubelle


“Overhead” of Multipartitioning

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 2 4 8 16 32 64 128 256 512 1024 2048

Number of Chunks Per Processor

Tim

e (

Se

co

nd

s) p

er

Ite

rati

on


Load balancer in action

0

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501 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91

Iteration Number

Nu

mb

er

of

Ite

rati

on

s P

er

se

con

dAutomatic Load Balancing in Crack Propagation

1. ElementsAdded 3. Chunks

Migrated

2. Load Balancer Invoked


Parallel Collision Detection

• Detect collisions (intersections) between objects scattered across processors

Approach, based on Charm++ ArraysOverlay regular, sparse 3D grid of voxels (boxes)

Send objects to all voxels they touch

Collide voxels independently and collect results

Leave collision response to user code


Collision Detection Speed

• O(n) serial performance

Good speedups to 1000s of processors

ASCI Red, 65,000 polygons per processor scaling problem

(to 100 million polygons)

Single Linux PC

2us per polygon serial performance


Rocket Simulation

• Our Approach:– Multi-partition decomposition

– Data-driven objects (Charm++)

– Automatic load balancing framework

• AMPI: Migration path for existing MPI+Fortran90 codes– ROCFLO, ROCSOLID, and

ROCFACE

"Overhead" of multipartition decomposition

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Number of partitions


Timeshared parallel machines

• How to use parallel machines effectively?• Need resource management

– Shrink and expand individual jobs to available sets of processors

– Example: Machine with 100 processors

• Job1 arrives, can use 20-150 processors

• Assign 100 processors to it

• Job2 arrives, can use 30-70 processors, – and will pay more if we meet its deadline

• We can do this with migratable objects!


Faucets: Multiple Parallel Machines

• Faucet submits a request, with a QoS contract:– CPU seconds, min-max cpus, deadline, interacive?

• Parallel machines submit bids:– A job for 100 cpu hours may get a lower price bid if:

• It has less tight deadline,

• more flexible PE range

– A job that requires 15 cpu minutes and a deadline of 1 minute

• Will generate a variety of bids

• A machine with idle time on its hand: low bid


Faucets QoS and Architecture

•User specifies desired job parameters such as:

•min PE, max PE, estimated CPU-seconds, priority, etc.

•User does not specify machine. .

•Planned: Integration with Globus

Central Server

Faucet Client

Web Browser

Workstation Cluster

Workstation Cluster

Workstation Cluster


How to make all of this work?

• The key: fine-grained resource management model– Work units are objects and threads

• rather than processes

– Data units are object data, thread stacks, ..

• Rather than pages

– Work/Data units can be migrated automatically

• during a run


Time-Shared Parallel Machines


Appspector: Web-based Monitoring and Steering of Parallel Programs

• Parallel Jobs submitted via a server– Server maintains database of running programs

– Charm++ client-server interface

• Allows one to inject messages into a running application

• From any web browser:– You can attach to a job (if authenticated)

– Monitor performance

– Monitor behavior

– Interact and steer job (send commands)


BioCoRE

•Project Based•Workbench for Modeling•Conferences/Chat Rooms•Lab Notebook•Joint Document Preparation

Goal: Provide a web-based way to virtually bring scientists together.

http://www.ks.uiuc.edu/Research/biocore/


Some New Projects

• Load Balancing for really large machines:– 30k-128k processors

• Million-processor Petaflops class machines– Emulation for software development

– Simulation for Performance Prediction

• Operations Research– Combinatorial optiization

• Parallel Discrete Event Simulation


Summary

• Exciting times for parallel computing ahead• We are preparing an object based infrastructure

– To exploit future apps on future machines

• Charm++, AMPI, automatic load balancing• Application-oriented research that produces enabling

CS technology• Rich set of collaborations• More information: http://charm.cs.uiuc.edu

component frameworks:

Documents

load balancing code

load imbalances

dynamic load balancingreuse

parallel applications

serial mesh

rebalancing strategiesit

timeloop loo

mesh partitionfem framework