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Programming Paradigms for C oncurrency

Programming Paradigms for C oncurrency. Pavol Černý , Vasu Singh, Thomas Wies. Programming Paradigms for Concurrency. Three parts covering three major paradigms. Classical shared memory programming Pavol Černý Programming with transactional memories Vasu Singh

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Programming Paradigms for C oncurrency

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  1. Programming Paradigms for Concurrency PavolČerný, Vasu Singh, Thomas Wies Art of Multiprocessor Programming

  2. Programming Paradigms for Concurrency • Three parts covering three major paradigms. • Classical shared memory programming • PavolČerný • Programming with transactional memories • Vasu Singh • Message-passing programming • Thomas Wies

  3. Administrivia • Course webpage • http://pub.ist.ac.at/courses/ppc10/ • Feel free to contact the instructors • firstname.lastname@ist.ac.at • Current plan is 6 homework assignements • two per course part • Class project (much more on this today) • Grades: 60% course project, 40% homework • Register at gradschool@ist.ac.at

  4. Programming Paradigms for ConcurrencyPart I: Shared Memory Programming PavolČerný Art of Multiprocessor Programming

  5. Mutual Exclusion Accessing a shared resource: Flag is raised means “I am going to usea shared resource” no access Flag is lowered means “I am not using a shared resource” access

  6. Mutual Exclusion Alice Bob flag[0] ←0 flag[1] ←0 no access no access flag[1] = 0 flag[0] = 0 flag[0] ← 0 flag[0] ← 0 test test flag[0] ← 1 flag[1] ← 1 Boom! access access

  7. Mutual Exclusion: Attempt 2 Alice Bob flag[0] ←0 flag[1] ←0 no access no access flag[0] ← 1 flag[1] ← 1 flag[0] ← 0 flag[0] ← 0 request request flag[0] = 0 flag[1] = 0 Now what?! access access

  8. Mutual Exclusion: Attempt 3 Alice Bob flag[0] ←0 flag[1] ←0 no access no access flag[0] ← 1 turn ← 1 flag[1] ← 1 turn ← 0 flag[0] ← 0 flag[0] ← 0 request request flag[0] = 0 or turn = 1 flag[1] = 0 or turn = 0 OK, works! access access

  9. Mutual exclusion • Questions to ponder: • Can we make do with two shared bits (instead of three)? • How can one extend this idea to n processes? • Does the algorithm work in Java? • Run it and see. What is the problem? • Where is the fault in our proof?

  10. Schedule

  11. How many of you have seen…? Have you programmed concurrent programs? In Java? In pthreads? … Bakery algorithm? Queue locks? Linearizability? Sequential consistency? compareAndSet? Concurrent Hashtables?

  12. Projects • Topic: • good: choose from among our suggestions • better: define your own project • On your own or in groups of two • Pick a project by: before Christmas • Progress report 1: January 15th (2 pages) • Presentation and final report: January 27th and February 3rd (final report: 4 pages)

  13. Project 1: Irregular data parallelism Cavity Example: Delaunay mesh refinement Effects of updates are local, but not bounded statically (“irregular”). Can we still exploit locality for parallelism?

  14. Project 1: Irregular data parallelism Locality of effects: Mesh retriangulation http://iss.ices.utexas.edu/lonestar/index.html

  15. Project 1: Irregular data parallelism Lonestar benchmark suite: http://iss.ices.utexas.edu/lonestar/index.html Barnes-Hut N-body Simulation Delaunay Mesh refinement Focused communities Delaunay triangulation … Project: 1. pick one of these applications, 2. find a good (possibly novel) way of parallelizing it, 3. implement it (by modifying the sequential implementation provided in Lonestar benchmarks), 4. confirm improvement in running time by experimentation.

  16. Project 2: Deductive verification: proving a concurrent data structure correct Pick an implementation of a concurrent data structure: a stack, a queue, a set, .. Pick a theorem prover or a verification tool: for example: PVS or QED Prove that the implementation is linearizable P1: remove(7) 7 3 9 5 P2: remove(5)

  17. Project 2: Deductive verification: proving a concurrent data structure correct P1: remove(7) 7 3 9 5 References: R. Colvin, L. Groves, V. Luchangco, M. Moir: Formal Verification of a Lazy Concurrent List-Based Set Algorithm. CAV 2006 T. Elmas, S. Qadeer, A. Sezgin, O. Subasi, S. Tasiran: Simplifying Linearizability Proofs with Reduction and Abstraction. TACAS 2010. P2: remove(5)

  18. Project 3:Performance measurement/ performance model for concurrent programs • Pick a problem with at least three-four different solutions • Lock implementations • Data structures: queues, stacks, sets… • Examine the performance of the solutions in different settings: • small number of threads vs large number of threads • 2 cores, small amount of memory (laptop) vs 8 cores, large memory/cache (server) • different usage models • input that generates little vs input that generates lots of contention • 3a. Find a hybrid solution that works well in a particular setting • or • 3b. Find a performance model that explains the data

  19. Project 4: Your own!

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