By Mark S. Miller, Tom Van Cutsem, Bill Tulloh (auth.), Matthias Felleisen, Philippa Gardner (eds.)
This ebook constitutes the refereed complaints of the twenty second ecu Symposium on Programming, ESOP 2013, held as a part of the ecu Joint meetings on conception and perform of software program, ETAPS 2013, which came about in Rome, Italy, in March 2013. The 31 papers, awarded including a full-length invited speak, have been conscientiously reviewed and chosen from one hundred twenty complete submissions. The contributions were equipped based on ten topical sections on programming suggestions; programming instruments; separation common sense; sluggish typing; shared-memory concurrency and verification; technique calculi; taming concurrency; version checking and verification; weak-memory concurrency and verification; and kinds, inference, and analysis.
Read Online or Download Programming Languages and Systems: 22nd European Symposium on Programming, ESOP 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings PDF
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Extra info for Programming Languages and Systems: 22nd European Symposium on Programming, ESOP 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings
This compiler is in some respects more powerful than MapReduce, because (1) it can handle more complex queries, including chains of MapReduce computations and The Compiler Forest 35 (2) it parallelizes the computation across both cores and machines. With a tiny change we obtain a compiler that only parallelizes across machines: P CSEQ P CCluster COO ∗ . With a little more work one can also add the only important missing MapReduce optimization, namely early aggregation in the map stage. 5 Mathematical Foundations We now turn to a semantical account of partial compilers in terms of a category of tactics.
If both transfers become confident (before any cancellations win the race), then the overall decision is to proceed. 8 We do not feed the cancellation promises directly into the race, as Alice could then fulfill the cancellation promise, causing the race to signal a decision to proceed with the exchange, even though Alice’s money has not been escrowed, potentially giving Bob’s stock to Alice for free. Instead, once the cancellation promise has been either fulfilled 7 8 By ”promptly” we mean, once the relevant machines are up, processes running, and reachable to each other over the network.
We add one additional twist by including resource allocation and scheduling in the plan language. , ) by splitting the collection into many partitions resident on different cluster machines (each machine may have multiple partitions). The goal of the generated plan is to process the partitioned collections in an efficient way, ideally having each piece of data be processed by the machine where it is stored. In the following simple example we just use two machines. We define the operator application unary partial compiler P CCluster to be: Compiler S : OpAp.
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