Multilingual Debugging Support for Datadriven Parallel Languages

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Multilingual Debugging Support for Data-driven
Parallel Languages

• Parthasarathy Ramachandran
• Laxmikant Kale
• Parallel Programming Laboratory
• Dept. of Computer Science
• University of Illinois at Urbana Champaign

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Data driven execution motivation

• Latency
• openMP remote access
• MPI a blocking receive
• Response delay remote processor may be busy
• Latency Tolerance
• Basic idea have multiple items on your agenda
• Overlap wait for data with useful computation

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Data driven systems

• Data flow machines
• Functional and Logic languages (1983-)
• Rediflow (Keller/Lindstrom), ..
• Prolog Conery, Kale, ..
• Actors
• Charm/Chare Kernel 1987
• first implementation on parallel machines
• Multipol, Cid, Chant, Tulip, ..

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What is data driven execution

• Multiple entities per processor
• Objects, threads, handlers,
• Scheduled based on availability of data

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Data driven execution
Scheduler
Scheduler
Message Q
Message Q
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Data driven execution

• Existence of scheduler DDE pre-requisite
• Implicit control transfer
• e.g. when a thread blocks for a recv, control
  transfers to a module of systems choice

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Data driven execution

• Advantages
• Adaptive overlap of useful computation with idle
  time
• Multiple modules efficiency and modularity
• overlap idle time in one module with useful
  computation from another
• MPI/PVM modules cant do this (as well), without
  sacrificing modularity

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Debugging problem

• Implicit control transfer leads to debugging
  difficulties
• Unpredictable jumps
• Need to observe schedulers queue
• Need to use scheduling quanta
• I.e. step to the next event
• Here
• A general purpose framework for debugging
  data-driven programs

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Converse

• Many data-driven languages/paradigms
• Which one is right?
• Acceptance barriers
• Coexistence with monsters (MPI/openMP)
• Our solution interoperability
• Not the same as C/Fortran interoperabilty
• Ability to integrate modules written in different
  paradigms in a single program
• E.g. MPI, Charm, Chant

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Converse

• Supports multi-paradigm programming
• Provides portability
• Makes it easy to implement RTS for new paradigms
• Several languages/libraries
• Charm, Java, threaded MPI, PVM, md-perl, pc,
  Nexus, Path, Tempo, Cid, CC,..
• More info http//charm.cs.uiuc.edu

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Example application
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Debugging Multi-paradigm Multi-lingual Programs

• New problems
• Scheduler entities belong to different languages
• Need ability to set language specific breakpoints
• Also
• Integration with normal debugging methods
• Source level debugging e.g. gdb
• Need to freeze execution properly

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Inadequacy of current methods

• Can we use gdb? (for example)
• Scheduler code inaccessible
• Cannot focus on specific breakpoints
• If we set a breakpoint in the scheduler
• Scheduler entities belong to different languages
• How to view them uniformly?
• Need support for language implementors

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Converse debugging framework

• Freeze and Thaw
• External intervention
• Freeze schedulers (on one or all processors)
• Ensure bookkeeping tasks continue
• Scheduler processes debugging commands only
• Scheduler level language-specific breakpoints
• Language-specific viewing
• of scheduler entries (e.g. method invocation,
  msg)
• of resident entities (e.g. objects, threads)

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Architecture

• Uses converse client-server interface
• Can inject messages into running programs
• Can send responses via TCP sockets
• Used for other purposes as well
• E.g. Web based submission/monitoring

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Architecture

• Debugger client
• Java based, GUI/communication
• Debugger runtime library
• A module in Converse runtime
• Provides hooks for languages/libraries
• GDB interface
• Integrates source level debugging
• Can customize for other debuggers

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Viewing multilingual entities

• Schedulers view
• Each entry
• The header contains a handler index
• Handlers are registered by languages
• Language runtimes register callback functions
• showHeader returns a short string
• showContent returns a long string with newLines
• On freeze scheduler sends list of headers
• On demand, sends the result of showContent

header
gibberish
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Non scheduler entities

• Objects and other data structures
• Not in the schedulers queue
• But still important to view
• Same problem multiple languages
• Solution
• Each language registers each of its objects
• For C object, use virtual inheritance
• For others, registered callbacks

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Breakpoints

• Are language specific
• Charm list of remotely invokable methods
• Multipol handler function names, Chant Code
• Each language supplies
• A list of breakpoints (names and indices)
• RTS maintains list of set breakpoints
• note a handler may represent multiple
  breakpoints
• Given a scheduler entry,
• language RTS identifies breakpoint number

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Optimizations

• Data transfer between the runtime and client
• Chunking send only a viewable screenful of
  information at a time
• Caching
• use idle time while user inspects data to collect
  and prepare information

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Summary

• Debugger is in operational use on
• Workstation clusters
• Parallel machines (Origin 2000, T3E)
• Complements source level debuggers
• Provides a useful functionality for all
  data-driven languages/libraries
• If you use Converse for your RTS, you will get
  this functionality for free

http//charm.cs.uiuc.edu