Towards Jungle Computing with Ibis/Constellation Jason Maassen, - - PowerPoint PPT Presentation

Towards Jungle Computing with Ibis/Constellation

Jason Maassen, Niels Drost Henri Bal, Frank Seinstra

Department of Computer Science VU University, Amsterdam, The Netherlands

Introduction

• HPC is entering many domains
• Not just: physics / chemistry / climate modelling
• Also: semantic web / medical / multimedia analysis /

neuroinformatics / remote sensing / astronomy / ...

• HPC is becoming more complex
• Not just large SMP or clusters, instead:
• Clusters of SMPs / Grids / Clouds / Supers / ...
• Heterogenous machines using GPU / Cell / FPGA
• “It‟s a jungle out there“

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Example Domain

Computational Astrophysics (amusecode.org)

Jungle Computing

• Worst case computing ... as required by users
• Arbitrary combination of distributed, hierarchical,

and heterogenous computing

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Many Task Computing

According to Raicu, Foster, et al [SC‟08] “High-performance computations comprising multiple distinct activities, coupled via file system operations or message passing. Tasks may be small or large, uni-processor or multi-processor, compute-intensive or data-intensive. The set of tasks may be static or dynamic, homogeneous or heterogeneous, loosely coupled or tightly coupled. The aggregate number of tasks, quantity of computing, and volumes of data may be extremely large.”

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• Applications are dynamic and heterogeneous

workflows / DAGs of activities

MTC in the Jungle

• MTC has advantages for Jungle Computing
• Many distinct activities
• Can be implemented independently using the tools

and targeted to the HPC architecture, that best suit them

• Reduced programming complexity
• Complete applications are constructed using

sequences and combinations of activities

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Constellation

• MTC system for

Jungle Computing

• Model based on:

activities (tasks) executors (resources) contexts (matchmaking) events (communication)

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Constellation Model

Application

• Application: set of activities
• Distinct tasks
• Size and complexity may vary
• Targeted at specific HPC platform
• (Loosly) Coupled using events
• Often wrapper around existing code
• Similar to workflow or DAG of tasks
• Dynamic and unlimited in size

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Constellation Model

Hardware

• Hardware: set of executors
• Capable of running activities
• May represent anything from a single

core to an entire cluster, a GPU, etc.

• May be application specific
• Provides an application specific

heterogeneous resource pool

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Constellation Model

Context

• Both activities and executors

are tagged with a context

• Application defined label (+ rank)
• Used to defines relationship between

activites and executors, e.g.:

• Data dependencies, hardware

requirements, ...

• May combine contexts
• Executors may have preference

for label or rank

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Constellation Model

Matchmaking

• RTS performs load-balancing

and match-making

• Ensures activities are forwarded to

a suitable executor

• Tries to keep all executors busy
• Uses context-aware work-stealing
• RTS also performs event routing
• Based on unique activity identifier

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Constellation API

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Constellation API

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DACH 2008

Data Challenge in conjunction with IEEE Cluster/Grid 2008

• Supernova detection
• Analyse 1052 image pairs on 11 clusters (Intrigger)
• „Sequential‟ executable provided

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DACH 2008

Problem

• Main problems:
• Data distribution
• Heterogeneity of

work and hardware

• Load balancing

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DACH 2008

Workflow

• Winning approach in 2008:
• Parallelize workflow to improve hardware utilization
• Create hierarchical master worker framework
• Scheduling heuristics using data location and size

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Constellation Version

Option 1: Monolythic

• Wrap entire application in

a single activity

• One activity per image pair
• Wrap each machine in
• ne executor
• Multiple cores per executor
• Use context to influence
• rder and placement of

each of activities

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Evaluation

• Intrigger not available
• Instead we use DAS3+DAS4
• 5+6 clusters in the Netherlands
• Mix of 2/4/8/12/48 core machines
• Various types of GPUs
• Three Scenarios
• Data locality
• (Executor granularity)
• Heterogeneous processing

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Scenario 1

Data Locality

• Data distributed over 4 clusters of DAS3 + DAS4
• Use context to express data locality and preferred

processing order

• Adapt context to tune application
• No change in application

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Scenario 1

Results

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Activity Executor Effect “any” “any” Random

• rder

“any”,50 “any”, biggest Sorted by size “VU3”,”VU4”,50 “VU3”, biggest Local only Sorted by size “VU3”,”VU4”, ”any”,50 “VU3”, “any”, biggest Preference for local Fallback to any, Sorted by size

Constellation Version

Option 2: Workflow

• Wrap each stage in activity
• Wrap each core executor
• Use context to influence
• rder and placement of

each of the jobs

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Scenario 3:

Heterogeneous System

• 18 node GPU cluster
• 8 cores + 1 GPU per node
• Activity: single task
• Executor:

1 core (top) 1 core or GPU (bottom)

• Replaced activity 7.2

with GPU version.

• Label activities and

executors accordingly

• Significant performance

gain.

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Conclusions

• We think Jungle Computing is a neccesity for

some application areas.

• Constellation offers a suitable model (MTC) to

create such applications.

• Initial experiments show that Constellation works

well for a wide range of hardware configurations

• Easy to reconfigure applications to match resources
• Allows integration of specialized accellerator codes
• Suitable basis for a Jungle Computing model

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Future Work

• Application development
• AMUSE
• Remote Sensing
• Climate modelling
• Platform improvements
• Easier integration of existing codes
• Smart/automatic deployment/tuning of executors
• Improve data handling
• Better monitoring

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Questions ? jason@cs.vu.nl www.cs.vu.nl/ibis

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Scenario 2

Executor Granularity

• 30 largest images only
• Single 48 core machine
• Activity: entire application (a-c)

single task (d)

• Executor: [n]-cores
• No change in application

for experiment (a-c)

• Only change executor config.
• Completely ported

application in (d)

• Significant performance gain!

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