Transactional Memory

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See for example ea2eab : this fixes two such issues where we write an object field without first checking if we already did it. Note that Python is a complicated language; there are a number of less common cases that may cause conflict of any kind where we might not expect it at priori. In STM terms, this means blocks of code that are executed while guaranteeing that the transaction is not interrupted in the middle. This is experimental and may be removed in the future if Software lock elision is ever implemented.

In this example, we are sure that the item popped off one end of the list is appened again at the other end atomically. It means that another thread can run len lst1 or x in lst1 without any particular synchronization, and always see the same results, respectively 10 and True. It will never see the intermediate state where lst1 only contains 9 elements. Atomic sections are similar to re-entrant locks they can be nested , but additionally they protect against the concurrent execution of any code instead of just code that happens to be protected by the same lock in other threads.

This prevents all other threads from progressing at all. While it is not strictly true in pypy-stm , the exact rules for when other threads can progress or not are rather complicated; you have to consider it likely that such a piece of code will eventually block all other threads anyway.

Note that if you want to experiment with atomic , you may have to manually add a transaction break just before the atomic block. This is because the boundaries of the block are not guaranteed to be the boundaries of the transaction: the latter is at least as big as the block, but may be bigger. Therefore, if you run a big atomic block, it is a good idea to break the transaction just before. This can be done by calling transaction. This may be fixed at some point. There are also issues with the interaction of regular locks and atomic blocks. This can be seen if you write to files which have locks , including with a print to standard output.

If one thread tries to acquire a lock while running in an atomic block, and another thread has got the same lock at that point, then the former may fail with a thread. For now you can work around it by making sure that, say, all your prints are either in an atomic block or none of them are.

This kind of issue is theoretically hard to solve and may be the reason for atomic block support to eventually be removed. However, using them e. This means that in this case, the whole transaction will technically not cause any write into the lock object — it was unacquired before, and is still unacquired after the transaction.

If they each run a transaction that is long enough to contain the whole block, then all writes into the lock will be elided and the two transactions will not conflict with each other. As usual, they will be serialized in some order: one of the two will appear to run before the other. This approach can gracefully fail: unlike atomic sections, there is no guarantee that the transaction runs until the end of the block. It can enter the elision-mode anyway and tentatively execute the content of the block.

It is only at the end, when trying to commit, that the thread will pause. Note that this is all not implemented yet, but we expect it to work even if you acquire and release several locks. In the common case of acquiring several locks in nested order, they will all be elided by the same transaction. To repeat the basic idea, threads execute their code speculatively, and at known points e. Each duration of time between two commit-points is called a transaction. A conflict occurs when there is no consistent ordering.

The classical example is if two threads both tried to change the value of the same global variable. In that case, only one of them can be allowed to proceed, and the other one must be either paused or aborted restarting the transaction. If this occurs too often, parallelization fails. How much actual parallelization a multithreaded program can see is a bit subtle. Basically, a program not using transaction. However, if the program requires longer transactions, it comes with less obvious rules. The exact details may vary from version to version, too, until they are a bit more stabilized.

Here is an overview.

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Parallelization works as long as two principles are respected. The first one is that the transactions must not conflict with each other. The most obvious sources of conflicts are threads that all increment a global shared counter, or that all store the result of their computations into the same list — or, more subtly, that all pop the work to do from the same list, because that is also a mutation of the list.

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You can work around it with transaction. A conflict occurs as follows: when a transaction commits i. Conflicts do occur, of course, and it is pointless to try to avoid them all. For example they can be abundant during some warm-up phase. What is important is to keep them rare enough in total. For all the remaining execution time of the atomic block, they will impede parallel work. The best is to organize the code so that such operations are done completely outside atomic. This prevents other threads from committing if they also need this lock.

More about it when lock elision is implemented and tested. A separate position paper gives an overview of our position about STM in general. The core of the implementation is in a separate C library called stmgc , in the c7 subdirectory current version of pypy-stm and in the c8 subdirectory bleeding edge version. In particular, the notion of segment is discussed there. PyPy latest.

International Journal of Networking and Computing

What is PyPy? TransactionQueue Atomic sections Locks Miscellaneous functions More details about conflicts Implementation Technical reports Reference to implementation details See also. One typical example would look like this, where the function func typically invokes a large amount of code: for key , value in bigdict.

Here is a direct usage example: with transaction. This is the limit above which more threads will not be able to execute on more cores. Right now it is limited to 4 due to inter-segment overhead, but should be increased in the future. It should also be settable, and the default value should depend on the number of actual CPUs.

If STM is not available, this returns 1. By default, signals are only enabled in the main thread; a non-main thread will not receive signals this is like CPython. Enabling signals in non-main threads is useful for libraries where threads are hidden and the end user is not expecting his code to run elsewhere than in the main thread.

Transactional Memory

This works without generating conflicts. The returned integers are only roughly in increasing order; this should not be relied upon. The future research will focus on how to trade off between performance and semantics correctness. Transactional memory provides a flexible and easy mechanism for parallel programming in multicore processors.

This paper firstly presented three key research areas for all implementation schemes according the following aspects: version management, conflict detection and contention management. Then we descript their exclusive research hot topic from different implementation schemes. From the analysis above, we get a research trend that the future transactional memory systems will be more likely combined with flexibility of software implementation and high performance of hardware implementation, the hybrid will become the next hot research field. Ananian, C.

Rinard, Efficient object-based software transactions. Asanovic, B. Kuszmaul, C. Leiserson and S. Lie, Unbounded transactional memory. Blundell, C. Lewis and M. Martin, Deconstructing transactional semantics: The subtleties of atomicity. Devietti, E. Making the fast case common and the uncommon case simple in unbounded transactional memory.

News, Goyal, M. Hill, M. Swift and D. Wood, TokenTM: Efficient execution of large transactions with hardware transactional memory. Ceze, L. Tuck, J. Torrellas and C. Cascaval, Bulk disambiguation of speculative threads in multiprocessors. Liu et al. Narayanasamy, G. Venkatesh, J. Sampson and M. Van Biesbrouck et al. Unbounded page-based transactional memory. Chung, J. Minh, A. McDonald, T. Skare and H. Chafi Tradeoffs in transactional memory virtualization. Dice, D. Shavit, What Really Makes Transactions Faster?.

Understanding tradeoffs in software transactional memory.

Verifiably Correct Transactional Memory.

Ennals, R. Software transactional memory should not be obstruction-free. Intel Research Cambridge Tech Report. Report No. Fraser, K. Practical lock-freedom. University of Cambridge, Report No. Fu, C. Wu, X. Wang and X. Yang, A review of software transactional memory in multicore processors.

Maurice and P. Bastian, Polymorphic contention management.

Guerraoui, R. Herlihy and B. Pochon, Toward a theory of transactional contention managers. Hammond, L. Wong, C. Mike, D. Brian and D. John et al. Transactional memory coherence and consistency. Harris, T. Fraser, Language support for lightweight transactions. Plesko, A. Shinnar and D. Tarditi, Optimizing memory transactions. Marlow, H. Maurice and S. Peyton-Jones, Composable memory transactions. Herlihy, M. Moss, Transactional memory: Architectural support for lock-free data structures.

Wait-free synchronization. ACM Trans. Languages Syst. Luchangco and M. Moir, Obstruction-free synchronization: Double-ended queues as an example. Luchangco, M.

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Moir and W. Scherer, Software transactional memory for dynamic-sized data structures. Hicks, M. Foster and P. Polyvios, Lock inference for atomic sections. Larus, J. Rajwar, Transactional Memory. Lev, Y. Maessen, Split hardware transactions: True nesting of transactions using best-effort hardware transactional memory.

Submission history

Li, K. Improving the performance of checkpointing scheme with task duplication. Chinese J. Marathe, V. Spear, H. Christopher, A. Athul, E. David, N. William III and L. Michael, Lowering the overhead of nonblocking software transactional memory. Schere-III and S. Adaptive software transactional memory. McCloskey B. Zhou, G. David and B. Eric, Autolocker: Synchronization inference for atomic sections. McDonald, A. Chung, B. Carlstrom, C. Minh, H. Chafi, C. Kozyrakis and K. Olukotun, Architectural semantics for practical transactional memory.

Transactional Memory, 2nd edition

Bobba, M. Moravan, M. Hill and D. LogTM: Log-based transactional memory. Bobba, K. Moore L. Yen and M. Hill et al. Supporting nested transactional memory in logTM. Moss, J. Hosking, Nested transactional memory: Model and architecture sketches. Sonmez, S. Srdjan, U. Osman, C. Adrian, V. Mateo and H. Tim, Plakal, M. Sorin, A. Condon and M. Hill, Lamport clocks: Verifying a directory cache-coherence protocol.

Puerto Vallarta, Mexico, Jun. Rajwar, R. Goodman, Transactional lock-free execution of lock-based programs. Herlihy and K. Lai, Virtualizing transactional memory. Saha, B. Adl-Tabatabai, R. Hudson, C. Minh and B. Hertzberg, Schere III, W. Scott, Contention management in dynamic software transactional memory.

Scherer III, W. Advanced contention management for dynamic software transactional memory. Shavit, N. Touitou, Software transactional memory. Shpeisman, T. Menon, A. Adl-Tabatabai, S. Balensiefer and D. Grossman et al.

Enforcing isolation and ordering in STM. Sorin, D. Plakal, A. Martin, D. Specifying and verifying a broadcast and a multicast snooping cache coherence protocol. IEEE Trans. Parallel Distrib. Lewis, V.

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