Download Compiler Construction: 20th International Conference, CC by Martin Odersky (auth.), Jens Knoop (eds.) PDF

By Martin Odersky (auth.), Jens Knoop (eds.)

This ebook constitutes the refereed lawsuits of the 20 th overseas convention on Compiler development, CC 2011, held in Saarbrücken, Germany, March 26—April three, 2011, as a part of ETAPS 2011, the ecu Joint meetings on conception and perform of software program.

The 15 revised complete papers offered including the summary of 1 invited speak have been conscientiously reviewed and chosen from fifty two submissions. The papers are geared up in topical sections on JIT compilation and code iteration, software research, reversible computing and interpreters, parallelism and high-performance computing, and activity and knowledge distribution.

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Example text

The table shows that CA generally outperforms QC and Naive. Copying large arrays affects execution performance and the results in Table 3 validate this claim. Where a significant number of bytes were copied by the naive implementation, for example, capr, crni and fdtd, CA performs better than both Naive and QC. In the three benchmarks that do not generate copies, the performance of CA is comparable to Naive and QC. This shows that the overhead of CA is low. It is therefore clear from the results of our experiments that the naive implementation generates significant overhead and is therefore unsuitable for an high-performance system.

That means if these copies remain at this location, the copies should be generated before the if-else statement. Loops. The main goal here is to identify copies that could be moved out of a loop. To place copies generated in a loop, we apply the rules for statement sequence and the if-else statement. 1. A disadvantage of propagating the copy outside of the loop is that if none of the loops that require copies is executed then we would have generated a useless copy. However, the execution is still correct.

Instructions that TraceMonkey generates for each benchmark. Figure 13 shows the results of our findings. We have produced this chart by plotting pairs (i, f (i)), which we define as follows: 1. let I be the list formed by the 100 most common SpiderMonkey bytecodes found in the Alexa benchmark. We order the bytecodes i according to a(i), the frequency of occurrences of i in the Alexa collection. 2. For each bytecode i ∈ I and benchmark B ∈ {Trace-Test, PeaceKeeper}, we let b(i) be i’s frequency in B, and we let f (i) = a(i) − b(i).

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