How Charm works its magic

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How Charm works its magic

Laxmikant Kale

http://charm.cs.uiuc.eduParallel Programming Laboratory

Dept. of Computer Science

University of Illinois at Urbana Champaign

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Parallel Programming Environment• Charm++ and AMPI

– Embody the idea of processor virtualization

• Processor Virtualization– Divide the computation into a large number of pieces

• Independent of number of processors• Typically larger than number of processors

– Let the system map objects to processors

User View

System implementation

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Charm++ and AMPI

• Charm++– Parallel C++

– “Arrays” of Objects

– Automatic load balancing

– Prioritization

– Mature System

– Available on all parallel machines we know

• Several applications:– Mol. Dynamics

– QM/MM

– Cosmology

– Materials/processes

– Operations Research

• AMPI = MPI + virtualization – A migration path for MPI codes

– Automatic dynamic load balancing for MPI applications

– Uses Charm++ object arrays and migratable threads

– Bindings for C, C++, Fortran90

• Porting MPI applications– Minimal modifications needed

– Automated via AMPizer

• AMPI progress– Ease of use: automatic packing

– Asynchronous communication

• Split-phase interfaces

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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

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AMPI:

7 MPI processes

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AMPI:

Real Processors

7 MPI “processes”

Implemented as virtual processors (user-level migratable threads)

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Benefits of Virtualization

• Software Engineering– Number of virtual processors can be

independently controlled– Separate VPs for different modules

• Message Driven Execution– Adaptive overlap of communication– Modularity– Predictability:

• Automatic Out-of-core– Asynchronous reductions

• Dynamic mapping– Heterogeneous clusters:

• Vacate, adjust to speed, share– Automatic checkpointing– Change the set of processors used

• Principle of Persistence– Enables Runtime

Optimizations

– Automatic Dynamic Load Balancing

– Communication Optimizations

– Other Runtime Optimizations

More: http://charm.cs.uiuc.edu

We will illustrate:

-- An application breakthrough

-- Cluster Performance Optimization

-- A Communication Optimization

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Technology Demonstration

• Recent breakthrough in Molecular Dynamics performance– NAMD, implemented using Charm++– Demonstrates power of techniques, applicable to CSAR

• Collection of charged atoms, with bonds– Thousands of atoms (10,000 - 500,000)– 1 femtosecond time-step, millions needed!

• At each time-step– Bond forces– Non-bonded: electrostatic and van der Waal’s

• Short-distance: every timestep• Long-distance: every 4 timesteps using PME (3D FFT)• Multiple Time Stepping

– Calculate velocities and advance positionsCollaboration with K. Schulten, R. Skeel, and coworkers

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700 VPs

192 + 144 VPs

30,000 VPs

Virtualized Approach to Parallelization using Charm++

These 30,000+ Virtual Processors (VPs) are mapped to real processors by Charm runtime system

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Asynchronous reductions, and message-driven execution in Charm allow applications to tolerate random variations

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Performance: NAMD on Lemieux

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500

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Cutoff

PME

MTS

ATPase: 320,000+ atoms including water

To be Published in SC2002: Gordon Bell

Award Finalist

15.6 ms,

0.8 TF

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Component Frameworks

Motivation• Reduce tedium of parallel

programming for commonly used paradigms & parallel data structures

• Encapsulate parallel data structures and algorithms

• Provide easy to use interface, – Sequential programming

style preserved• Use adaptive load balancing

framework• Used to build parallel

components

Frameworks• Unstructured Grids

– Generalized ghost regions– Used in

• RocFrac version,• RocFlu• Outside CSAR

– Fast Collision Detection

• Multiblock Framework– Structured Grids– Automates communication

• AMR– Common for both above

• Particles– Multiphase flows– MD, Tree codes

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Component Frameworks

Objective:• For commonly used

structures, application scientists– Shouldn’t have to deal

with parallel implementation issues

– Should be able to reuse code

Components and Challenges• Unstructured meshes:

Unmesh– Dynamic refinement support

for FEM– Solver interfaces– Multigrid support

• Structured meshes: Mblock– Multigrid support– Study applications

• Particles• Adaptive mesh refinement:

– Shrinking and growing trees– Applicable to the three

above

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Charm/AMPI

MPI/lower layers

D

Unmesh MBlock Particles

AMR support

Application

Orchestration / Intergration Support

Application Components

Parallel Standard Libraries

Solvers

Data transfer

CA B

Framework Components

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Outline• There is magic:

– Overview of charm capabilities

– Virtualization paper

– Summary of charm features:

• Converse– Machine model

– Scheduler and General Msgs

– Communication

– Threads

• Proxies and generated code

• Object Groups (BOCs)

• Support for migration– Seed balancing

– Migration support w reduction

• How migration is used– Vacating workstations and

adjusting to speed

– Adaptive Scheduler

• Principle of persistence:– Measurement based load

balancing

• Centralized strategies, refinement, commlb

• Distributed strategies (nbr)

– Collective communication opts

• Delegation

• Converse client-server interface

• Libraries: – liveviz, fft, ..

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Converse• Converse is a layer on which Charm++ is built

– Provides machine-dependent code

– Provides “utilities” needed by the RTS of many parallel programming languages

– Used for implementing many mini-languages

• Main Components of Converse:– Machine model


– Communication

– Threads

• Machine model:– Collections of nodes, each node is a collection processes.

– Processes on a node can share memory

– Macros for supporting node-level (shared) and processor level globals

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Data driven execution

Scheduler Scheduler

Message Q Message Q

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Converse Scheduler• The core of converse is message-driven execution

– But scheduled entities are not just “messgese” from remote processors

• Genalized notion of messages: any schedulable entity• From scheduler’s point of view: a block of memory,

– First few bytes encode a handler function (as an index into a table)

– Scheduler, in each iteration:• Polls network, enqueuing messages in a fifo• Selects a message from either the fifo or local-queue• Executes handler of of selected message

– This may result in enqueuing of message in the local-queue– Local queue is prioritized lifo/fifo– Priorities may be integers (smaller: higher) or bitvectors

(lexicographic)

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Converse: communication and threads• Communication support:

– “send” a converse message to a remote processor

– Message must have handler-index encoded at the beginning

– Variety of send-variations (sync/async, memory deallocation) and broadcasts supproted

• Threads: bigger topic– User level threads

– Migratable threads

– Scheduled via converse scheduler

– Suspend and awaken : low level thread package

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Communication ArchitectureCommunication Architecture

Communication API (Send/Recv)

Net MPI Shmem

UDP(machine-eth.c)

TCP(machine-tcp.c)

Myrinet(machine-gm.c)

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Parallel Program StartupParallel Program Startup

• Net version - nodelist

Charmrun node compute node

Rsh/ssh(IP, port)

my node

(IP, port)

Broadcast all nodes

(IP, port)

compute node

Rsh/ssh(IP, port)

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Converse InitializationConverse Initialization• ConverseInit

– Global variables initialization;

– Start worker threads;

• ConverseRunPE for each Charm PE– Per thread initialization;

– Loop into scheduler: CsdScheduler()

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Message formatsMessage formats

• Net version#define CMK_MSG_HEADER_BASIC { CmiUInt2 d0,d1,d2,d3,d4,d5,hdl,d7; }

Dgram Header length handlerxhandler

• MPI version#define CMK_MSG_HEADER_BASIC { CmiUInt2 rank, root, hdl,xhdl,info,d3; }

infohandlerxhandlerrank root d3

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SMP support• MPI-smp as an example

– Create threads: CmiStartThreads

– Worker threads work cycle

• See code in mahcine-smp.c

– Communication thread work cycle

• See code in machine-smp.c

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– Communication

– Threads





adjusting to speed



balancing




• Delegation



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Charm++ translator

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Need for Proxies• Consider:

– Object x of class A wants to invoke method f of obj y of class B.

– x and y are on different processors

– what should the syntax be?

• y->f( …)? : doesn’t work because y is not a local pointer

• Needed:– Instead of “y” we must use an ID that is valid across processors

– Method Invocation should use this ID

– Some part of the system must pack the parameters and send them

– Some part of the system on the remote processor must invoke the right method on the right object with the parameters supplied

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Charm++ solution: proxy classes• Classes with remotely invokeable methods

– inherit from “chare” class (system defined)

– entry methods can only have one parameter: a subclass of message

• For each chare class D – which has methods that we want to remotely invoke

– The system will automatically generate a proxy class Cproxy_D

– Proxy objects know where the real object is

– Methods invoked on this class simply put the data in an “envelope” and send it out to the destination

• Each chare object has a proxy– CProxy_D thisProxy; // thisProxy inherited from “CBase_D”

– Also you can get a proxy for a chare when you create it:

• CProxy_D myNewChare = CProxy_D::ckNew(arg);

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Generation of proxy classes• How does charm generate the proxy classes?

– Needs help from the programmer

– name classes and methods that can be remotely invoked

– declare this in a special “charm interface” file (pgm.ci)

– Include the generated code in your program

pgm.ci

mainmodule PiMod {

mainchare main {

entry main();

entry results(int pc);

};

chare piPart {

entry piPart(void);

};

Generates

PiMod.def.h

PiMod.def.h

pgm.h

#include “PiMod.decl.h”

..

Pgm.c

…

#include “PiMod.def.h”

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Object Groups• A group of objects (chares)

– with exactly one representative on each processor

– A single proxy for the group as a whole

– invoke methods in a branch (asynchronously), all branches (broadcast), or in the local branch

– creation:

• agroup = Cproxy_C::ckNew(msg)

– remote invocation:

• p.methodName(msg); // p.methodName(msg, peNum);

• p.ckLocalBranch()->f(….);

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Information sharing abstractions• Observation:

– Information is shared in several specific modes in parallel programs

• Other models support only a limited sets of modes:– Shared memory: everything is shared: sledgehammer

approach

– Message passing: messages are the only method

• Charm++: identifies and supports several modes– Readonly / writeonce

– Tables (hash tables)

– accumulators

– Monotonic variables

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– Communication

– Threads





adjusting to speed



balancing


• Distributed strategies


• Delegation



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Seed Balancing• Applies (currently) to singleton chares

– Not chare array elements

– When a new chare is created, the system has freedom to assign it any processor

– The “seed” message (containing constructor parameters) may be moved around among procs until it takes root

• Use– Tree-structured computations

– State-space search, divide-conquer

– Early applications of Charm

– See papers

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Object Arrays• A collection of data-driven objects

– With a single global name for the collection

– Each member addressed by an index

• [sparse] 1D, 2D, 3D, tree, string, ...

– Mapping of element objects to procS handled by the system

A[0] A[1] A[2] A[3] A[..]

User’s view

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A[0] A[1] A[2] A[3] A[..]

A[3]A[0]

User’s view

System view

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A[0] A[1] A[2] A[3] A[..]

A[3]A[0]

User’s view

System view

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Migration support• Forwarding of messages

– Optimized by hop-counts:

– If a message took multiple hops, the receiver sends the current address to sender

– Array manager maintains a cache of known addresses

• Home processor for each element – Defined by a hash function on the index

• Reductions and broadcasts– Must work in presence of migrations!

• Also, in presence of deletions/insertions

– Communication time proportional to number of processors not objects

– Uses spanning tree based on processors, handling migrated “stragglers” separately

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– Communication

– Threads





adjusting to speed



balancing


• Distributed strategies


• Delegation



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Flexible mapping: using migratibility• Vacating workstations

– If the “owner” starts using it,• Migrate objects away

– Detection:

• Adjusting to speed:– Static:

• measure speeds at the beginning

• Use speed ratios in load balancing

– Dynamic:• In a time-shared

environment• Measure unix “load”• Migrate proportional set of

objects if machine is loaded

• Adaptive job scheduler– Tells jobs to change the sets of

processors used

– Each job maintains a bit-vector of processors they can use.

– Scheduler can change bit-vector

– Jobs obey by migrating objects

– Forwarding, reductions:

• Residual process is left behind

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Job Monitor

Job SubmissionFile UploadJob Specs

Bids

Job Specs

File Upload

Job Id

Job Id

Cluster

Cluster

Cluster

Faucets: Optimizing Utilization Within/across Clustershttp://charm.cs.uiuc.edu/research/faucets

http://charm.cs.uiuc.edu/research/faucets





http://www.psc.edu/machines/tcs/lemieux.html



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When you attach to a job:

• Live performance data (bottom)

• Application data – App. Supplied -- Live

Job Monitoring: Appspector

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Inefficient Utilization Within A Cluster

Job A 10 proce

ssors

Allocate A

Job B

8 processors

B QueuedConflict !16 Processor system

Job A

Job B

Current Job Schedulers can yield low system utilization..A competitive problem in Faucets-like systems

Parallel Servers are “profit centers” in faucets: need high utilization

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Two Adaptive Jobs

Job A Max_pe = 10

Min_pe = 1

A Expands !

Job B

Min_pe = 8Max_pe= 16

Shrink AAllocate B !16 Processor system

Job A

Job B

B FinishesAllocate A !

Adaptive Jobs can shrink or expand the number of processors they use, at runtime: by migrating virtual processors in Charm/AMPI

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AQS: Adaptive Queuing System

• AQS: A Scheduler for Clusters– Has the ability to manage adaptive jobs

• Currently, those implemented in Charm++ and AMPI– Handles regular (non-adaptive) MPI jobs– Experimental results on CSE Turing Cluster

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System Load (%)

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Utli

zatio

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Traditional Job

Adaptive Jobs

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Traditional Job Adaptive Jobs

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– Communication

– Threads





adjusting to speed



balancing




• Delegation



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IV: Principle of Persistence• Once the application is expressed in terms of

interacting objects:– Object communication patterns and computational loads

tend to persist over time

– In spite of dynamic behavior

• Abrupt and large,but infrequent changes (eg:AMR)

• Slow and small changes (eg: particle migration)

• Parallel analog of principle of locality– Heuristics, that holds for most CSE applications

– Learning / adaptive algorithms

– Adaptive Communication libraries

– Measurement based load balancing

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Measurement Based Load Balancing• Based on Principle of persistence• Runtime instrumentation

– Measures communication volume and computation time

• Measurement based load balancers– Use the instrumented data-base periodically to make new

decisions

– Many alternative strategies can use the database

• Centralized vs distributed

• Greedy improvements vs complete reassignments

• Taking communication into account

• Taking dependences into account (More complex)

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Load balancer in action

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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

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mb

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rati

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con

dAutomatic Load Balancing in Crack Propagation

1. ElementsAdded 3. Chunks

Migrated

2. Load Balancer Invoked

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Measurement based load balancing

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– Communication

– Threads





adjusting to speed



balancing




• Delegation



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Optimizing for Communication Patterns• The parallel-objects Runtime System can observe,

instrument, and measure communication patterns– Communication is from/to objects, not processors

– Load balancers can use this to optimize object placement

– Communication libraries can optimize

• By substituting most suitable algorithm for each operation

• Learning at runtime

V. Krishnan, MS Thesis, 1996

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Delegation mechanism• Messages to array elements

– Normally, an array message is handled by the BOC responsible for that array

– But, you can delegate message sending to another library (boc)

• All proxies support the following delegation routines. – void CProxy::ckDelegate(CkGroupID delMgr);

– This is useful for constructing communication libraries

• Suppose you want to combine many small messages

• Use: – At the beginning: proxy.delegate(libID),

– In loops: {lib.start(), proxy[I]->f(m1);…; lib.end()}

– Lib can maintain internal data structures for bugffering msgs

– User’s interface remains unchanged (except for the lib calls)

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• Messages to array elements– Normally, an array message is handled by the BOC

responsible for that array


• All proxies support the following delegation routines. – void CProxy::ckDelegate(CkGroupID delMgr);



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• Messages to array elements– Normally, an array message is handled by the BOC

responsible for that array


• All proxies support the following delegation routines.

– void CProxy::ckDelegate(CkGroupID delMgr);



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Communication Optimizations

• Persistence principle– Observing

communication behavior across iterations

– Communication patterns

• Who sends to whom

• Size of messages

• “Collective” Communication– Substitute optimal

algorithms

– Tune parameters

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Mesh Direct MPI

This approach makes a difference in applications

-- E.g. Transpose in NAMD

Time/step (ms)

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– Communication

– Threads





adjusting to speed



balancing




• Delegation



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– Communication

– Threads





adjusting to speed



balancing




• Delegation



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Object-based Parallelization

User View

System implementation

User is only concerned with interaction between objects

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Data driven execution

Scheduler Scheduler

Message Q Message Q

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Scaling to 64K/128K processors of BG/L• What issues will arise?

– Communication

• Bandwidth use more important than processor overhead

• Locality:

– Global Synchronizations

• Costly, but not because it takes longer

• Rather, small “jitters” have a large impact

• Sum of Max vs Max of Sum

– Load imbalance important, but low grainsize is crucial

– Critical paths gains importance

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Runtime Optimization Challenges

• Full exploitation of object decomposition– Load balance within individual phases– Automatic Identification of phases

• 10K-100K processors– Inadequate load balancing strategies– Need topology-aware balancing– Also fully-distributed decision making

• But based on “reasonable” global information

• Communication Optimizations– Identify patterns, develop algorithms suitable for each, and implement “learning”

strategies

• Fault tolerance and incremental checkpointing– Objects as the unit of checkpointing.– In-memory checkpoints– Tradeoff between forward-path overhead and fault-handling cost

How Charm works its magic

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