Parallel Programming at TU München

Flashcards and summaries for Parallel Programming at the TU München

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Study with flashcards and summaries for the course Parallel Programming at the TU München

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

What are differences between kernel threads and user-level threads?

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

Which are the two concepts of synchronisation between threads in POSIX?

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

4 ways to distribute and coordinate work

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

drawbacks of work sharing?

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

coherency and consistency in memory models?

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

when system is coherent?

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

memory consistency is a contract between programmer and system. sequential consistency?

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

why seq consist is hard to achieve?

Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

types of races?    

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reducing synchro overhead?    

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false sharing?

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pragma nowait?

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Exemplary flashcards for Parallel Programming at the TU München on StudySmarter:

Parallel Programming

What are differences between kernel threads and user-level threads?
ul-threads... are scheduled by user cannot be preempted no kernel support or modifications needed switch between threads as needed

Parallel Programming

Which are the two concepts of synchronisation between threads in POSIX?
1) locks/mutual exclusion 2) condition variables

Parallel Programming

4 ways to distribute and coordinate work
  • Multiple concurrent executions aka. single program multiple data
  • master/worker
  • pipelining
  • arbitrary task dependencies

Parallel Programming

drawbacks of work sharing?

Drawback 1: possible imbalance caused by workload

Drawback 2: possible imbalance caused by machine


Parallel Programming

coherency and consistency in memory models?

Memory/Cache Coherency: reasoning about updates to one memory location

Memory Consistency: reasoning about updates to several memory locations

Parallel Programming

when system is coherent?

• Program order for loads/stores are preserved
• All stores eventually become visible
• All processors see the same order of writes

Parallel Programming

memory consistency is a contract between programmer and system. sequential consistency?

A multiprocessor system is sequentially consistent if the result of any
execution is the same as if the operations of all processors were executed in
some sequential order, and the operations of each individual processor appear
in this sequence in the order specified by the program”

Parallel Programming

why seq consist is hard to achieve?

Why is the program order requirement hard?
• Network can reorder
• Use of write-buffers
• Compiler optimizations
Why is the atomicity requirement hard?
• A write has to take place instantly with respect to all processors
• Can lead to a system-wide serialization of writes
We need to relax consistency requirements
• Necessary to achieve well performing systems
• Increase the level of concurrency
• Compensate with synchronization primitives
• Need them anyway for proper synchronization of contents
• Can implicitly execute consistency operations
Create the “illusion” of sequentially consistency

Parallel Programming

types of races?    
raw, war, waw
deal -- synchro

Parallel Programming

reducing synchro overhead?    
event synchro: point to point instead of barriers, hard to program
mutual exclusion: separate locks for separate data, smaller/less freq critical sections

Parallel Programming

false sharing?
degrades performance
- where distributed, coherent cache
- multiple threads access data
1. will never be altered by other threads
2. shares a cache line with data that is altered by other threads

caching protocol forces the first thread to reload the whole cache line

Parallel Programming

pragma nowait?

nowait
threads do not synchronize after the parallel loop

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