ECE1747%20Parallel%20Programming

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ECE1747 Parallel Programming

• Shared Memory Multithreading Pthreads

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Shared Memory

• All threads access the same shared memory data
  space.

Shared Memory Address Space
proc1
proc2
proc3
procN
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Shared Memory (continued)

• Concretely, it means that a variable x, a pointer
  p, or an array a refer to the same object, no
  matter what processor the reference originates
  from.
• We have more or less implicitly assumed this to
  be the case in earlier examples.

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Shared Memory
a
proc1
proc2
proc3
procN
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Distributed Memory - Message Passing

• The alternative model to shared memory.

mem1
mem2
mem3
memN
a
a
a
a
proc1
proc2
proc3
procN
network
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Shared Memory vs. Message Passing

• Same terminology is used in distinguishing
  hardware.
• For us distinguish programming models, not
  hardware.

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Programming vs. Hardware

• One can implement
• a shared memory programming model
• on shared or distributed memory hardware
• (also in software or in hardware)
• One can implement
• a message passing programming model
• on shared or distributed memory hardware

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Portability of programming models
shared memory programming
message passing programming
distr. memory machine
shared memory machine
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Shared Memory Programming Important Point to
Remember

• No matter what the implementation, it
  conceptually looks like shared memory.
• There may be some (important) performance
  differences.

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Multithreading

• User has explicit control over thread.
• Good control can be used to performance benefit.
• Bad user has to deal with it.

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Pthreads

• POSIX standard shared-memory multithreading
  interface.
• Provides primitives for process management and
  synchronization.

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What does the user have to do?

• Decide how to decompose the computation into
  parallel parts.
• Create (and destroy) processes to support that
  decomposition.
• Add synchronization to make sure dependences are
  covered.

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General Thread Structure

• Typically, a thread is a concurrent execution of
  a function or a procedure.
• So, your program needs to be restructured such
  that parallel parts form separate procedures or
  functions.

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Example of Thread Creation (contd.)
main()
pthread_ create(func)
func()
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Thread Joining Example

• void func(void ) ..
• pthread_t id int X
• pthread_create(id, NULL, func, X)
• ..
• pthread_join(id, NULL)
• ..

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Example of Thread Creation (contd.)
main()
pthread_ create(func)
func()
pthread_ join(id)
pthread_ exit()
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Sequential SOR

• for some number of timesteps/iterations
• for (i0 iltn i )
• for( j1, jltn, j )
• tempij 0.25
• ( gridi-1j gridi1j
• gridij-1 gridij1 )
• for( i0 iltn i )
• for( j1 jltn j )
• gridij tempij

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Parallel SOR

• First (i,j) loop nest can be parallelized.
• Second (i,j) loop nest can be parallelized.
• Must wait to start second loop nest until all
  processors have finished first.
• Must wait to start first loop nest of next
  iteration until all processors have second loop
  nest of previous iteration.
• Give n/p rows to each processor.

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Pthreads SOR Parallel parts (1)

• void sor_1(void s)
• int slice (int) s
• int from (slicen)/p
• int to ((slice1)n)/p
• for( ifrom iltto i)
• for( j0 jltn j )
• tempij 0.25(gridi-1j gridi1j
• gridij-1 gridij1)

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Pthreads SOR Parallel parts (2)

• void sor_2(void s)
• int slice (int) s
• int from (slicen)/p
• int to ((slice1)n)/p
• for( ifrom iltto i)
• for( j0 jltn j )
• gridij tempij

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Pthreads SOR main

• for some number of timesteps
• for( i0 iltp i )
• pthread_create(thrdi, NULL, sor_1, (void
  )i)
• for( i0 iltp i )
• pthread_join(thrdi, NULL)
• for( i0 iltp i )
• pthread_create(thrdi, NULL, sor_2, (void
  )i)
• for( i0 iltp i )
• pthread_join(thrdi, NULL)

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Summary Thread Management

• pthread_create() creates a parallel thread
  executing a given function (and arguments),
  returns thread identifier.
• pthread_exit() terminates thread.
• pthread_join() waits for thread with particular
  thread identifier to terminate.

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Summary Program Structure

• Encapsulate parallel parts in functions.
• Use function arguments to parameterize what a
  particular thread does.
• Call pthread_create() with the function and
  arguments, save thread identifier returned.
• Call pthread_join() with that thread identifier.

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Pthreads Synchronization

• Create/exit/join
• provide some form of synchronization,
• at a very coarse level,
• requires thread creation/destruction.
• Need for finer-grain synchronization
• mutex locks,
• condition variables.

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Use of Mutex Locks

• To implement critical sections.
• Pthreads provides only exclusive locks.
• Some other systems allow shared-read,
  exclusive-write locks.

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Barrier Synchronization

• A wait at a barrier causes a thread to wait until
  all threads have performed a wait at the barrier.
• At that point, they all proceed.

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Implementing Barriers in Pthreads

• Count the number of arrivals at the barrier.
• Wait if this is not the last arrival.
• Make everyone unblock if this is the last
  arrival.
• Since the arrival count is a shared variable,
  enclose the whole operation in a mutex
  lock-unlock.

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Implementing Barriers in Pthreads

• void barrier()
• pthread_mutex_lock(mutex_arr)
• arrived
• if (arrivedltN)
• pthread_cond_wait(cond, mutex_arr)
• else
• pthread_cond_broadcast(cond)
• arrived0 / be prepared for next barrier
  /
• pthread_mutex_unlock(mutex_arr)

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Parallel SOR with Barriers (1 of 2)

• void sor (void arg)
• int slice (int)arg
• int from (slice (n-1))/p 1
• int to ((slice1) (n-1))/p 1
• for some number of iterations

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Parallel SOR with Barriers (2 of 2)

• for (ifrom iltto i)
• for (j1 jltn j)
• tempij 0.25 (gridi-1j
  gridi1j gridij-1 gridij1)
• barrier()
• for (ifrom iltto i)
• for (j1 jltn j)
• gridijtempij
• barrier()

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Parallel SOR with Barriers main

• int main(int argc, char argv)
• pthread_t thrdp
• / Initialize mutex and condition variables /
• for (i0 iltp i)
• pthread_create (thrdi, attr, sor,
  (void)i)
• for (i0 iltp i)
• pthread_join (thrdi, NULL)
• / Destroy mutex and condition variables /

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Note again

• Many shared memory programming systems (other
  than Pthreads) have barriers as basic primitive.
• If they do, you should use it, not construct it
  yourself.
• Implementation may be more efficient than what
  you can do yourself.

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Busy Waiting

• Not an explicit part of the API.
• Available in a general shared memory programming
  environment.

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Busy Waiting

• initially flag 0
• P1 produce data
• flag 1
• P2 while( !flag )
• consume data

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Use of Busy Waiting

• On the surface, simple and efficient.
• In general, not a recommended practice.
• Often leads to messy and unreadable code (blurs
  data/synchronization distinction).
• May be inefficient

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Private Data in Pthreads

• To make a variable private in Pthreads, you need
  to make an array out of it.
• Index the array by thread identifier, which you
  should keep track of .
• Not very elegant or efficient.

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Other Primitives in Pthreads

• Set the attributes of a thread.
• Set the attributes of a mutex lock.
• Set scheduling parameters.

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ECE 1747 Parallel Programming

• Machine-independent
• Performance Optimization Techniques

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Returning to Sequential vs. Parallel

• Sequential execution time t seconds.
• Startup overhead of parallel execution t_st
  seconds (depends on architecture)
• (Ideal) parallel execution time t/p t_st.
• If t/p t_st gt t, no gain.

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General Idea

• Parallelism limited by dependences.
• Restructure code to eliminate or reduce
  dependences.
• Sometimes possible by compiler, but good to know
  how to do it by hand.

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Optimizations Example 16

• for (i 0 i lt 100000 i) ai 1000 ai
  1

Cannot be parallelized as is. May be parallelized
by applying certain code transformations.
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Summary

• Reorganize code such that
• dependences are removed or reduced
• large pieces of parallel work emerge
• loop bounds become known
• Code can become messy there is a point of
  diminishing returns.

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Factors that Determine Speedup

• Characteristics of parallel code
• granularity
• load balance
• locality
• communication and synchronization

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Granularity

• Granularity size of the program unit that is
  executed by a single processor.
• May be a single loop iteration, a set of loop
  iterations, etc.
• Fine granularity leads to
• (positive) ability to use lots of processors
• (positive) finer-grain load balancing
• (negative) increased overhead

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Granularity and Critical Sections

• Small granularity gt more processors gt more
  critical section accesses gt more contention.

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Issues in Performance of Parallel Parts

• Granularity.
• Load balance.
• Locality.
• Synchronization and communication.

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Load Balance

• Load imbalance different in execution time
  between processors between barriers.
• Execution time may not be predictable.
• Regular data parallel yes.
• Irregular data parallel or pipeline perhaps.
• Task queue no.

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Static vs. Dynamic

• Static done once, by the programmer
• block, cyclic, etc.
• fine for regular data parallel
• Dynamic done at runtime
• task queue
• fine for unpredictable execution times
• usually high overhead
• Semi-static done once, at run-time

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Choice is not inherent

• MM or SOR could be done using task queues put
  all iterations in a queue.
• In heterogeneous environment.
• In multitasked environment.

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Static Load Balancing

• Block
• best locality
• possibly poor load balance
• Cyclic
• better load balance
• worse locality
• Block-cyclic
• load balancing advantages of cyclic (mostly)
• better locality

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Dynamic Load Balancing (1 of 2)

• Centralized single task queue.
• Easy to program
• Excellent load balance
• Distributed task queue per processor.
• Less communication/synchronization

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Dynamic Load Balancing (2 of 2)

• Task stealing
• Processes normally remove and insert tasks from
  their own queue.
• When queue is empty, remove task(s) from other
  queues.
• Extra overhead and programming difficulty.
• Better load balancing.

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Semi-static Load Balancing

• Measure the cost of program parts.
• Use measurement to partition computation.
• Done once, done every iteration, done every n
  iterations.

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Molecular Dynamics (MD)

• Simulation of a set of bodies under the influence
  of physical laws.
• Atoms, molecules, celestial bodies, ...
• Have same basic structure.

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Molecular Dynamics (Skeleton)

• for some number of timesteps
• for all molecules i
• for all other molecules j
• forcei f( loci, locj )
• for all molecules i
• loci g( loci, forcei )

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Molecular Dynamics

• To reduce amount of computation, account for
  interaction only with nearby molecules.

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Molecular Dynamics (continued)

• for some number of timesteps
• for all molecules i
• for all nearby molecules j
• forcei f( loci, locj )
• for all molecules i
• loci g( loci, forcei )

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Molecular Dynamics (continued)

• for each molecule i
• number of nearby molecules counti
• array of indices of nearby molecules indexj
• ( 0 lt j lt counti)

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Molecular Dynamics (continued)

• for some number of timesteps
• for( i0 iltnum_mol i )
• for( j0 jltcounti j )
• forcei f(loci,locindexj)
• for( i0 iltnum_mol i )
• loci g( loci, forcei )

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Molecular Dynamics (simple)

• for some number of timesteps
• parallel for
• for( i0 iltnum_mol i )
• for( j0 jltcounti j )
• forcei f(loci,locindexj)
• parallel for
• for( i0 iltnum_mol i )
• loci g( loci, forcei )

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Molecular Dynamics (simple)

• Simple to program.
• Possibly poor load balance
• block distribution of i iterations (molecules)
• could lead to uneven neighbor distribution
• cyclic does not help

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Better Load Balance

• Assign iterations such that each processor has
  the same number of neighbors.
• Array of assign records
• size number of processors
• two elements
• beginning i value (molecule)
• ending i value (molecule)
• Recompute partition periodically

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Frequency of Balancing

• Every time neighbor list is recomputed.
• once during initialization.
• every iteration.
• every n iterations.
• Extra overhead vs. better approximation and
  better load balance.

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Summary

• Parallel code optimization
• Critical section accesses.
• Granularity.
• Load balance.