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) runs 3967 times, which I would expect would give a nice speedup and be well worth tracing.
Is the trace compile time linear in the length of the loop body, or can it grow faster (either asymptotically, or at thresholds or something)? Otherwise it suggests some kind of "don't trace 'giant loops'" element to the heuristics. It looks like the other loops in md5 are don't cares.
It then runs the new benchmark suite with -m, -j, and then -m -j, and outputs a chart describing how long each loop took, and which mode is best for that loop. Attached is the initial spew for Sun Spider.bz: That's a good question.
I am trying to find a good way to automatically correlate results with information like that, right now it's by hand. MCTS results Results for the four remaining interesting benchmarks, using a random search.
Speed of development is key, and Python was a faster way to code (and re-code) the programs that control this production pipeline.
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Conversely, it's probably OK to lose an opportunity to trace a loop here or there--the loss is smallish.Working on a new script with the ability to selectively enable tracing on any combination of loops in a script. I've no idea how accurate this is, but the results seem in line with the exhaustive searches on other tests (sometimes with more confidence).It performs a tree search to find a supposedly optimal result. Method JIT total: 107ms Tracing JIT total: 187ms Combined total: 151ms Estimated optimum: 105ms This seems to be saying that cube, raytrace, fannkuch, and aes do not trace well. I've been going over the SS data to try to make a start on simplifying the problem.The results are kind of hard to interpret at a glance so I'm still playing with it. bitops-bitwise-and: exhaustive search Initial script only does exhaustive searches, which doesn't work for a few SS tests, and all of v8. For now, attached is the exhaustive analysis where we can do it. My takeaway lesson from Sun Spider is that we should give up on loop nesting very quickly if it's not working out. Relative to the current JM TM setup, where we try to trace everything, the biggest wins we can get are by not tracing anything in the following benchmarks: access-fannkuch 32 ms 3d-raytrace 13 crypto-aes 9 bitops-nsieve-bits 6 date-formate-tofte 6 Features that jumped out at me as possibly being useful to distinguish these: * fannkuch - (Note: It's probably fine/good to trace the first loop, a simple array init) - triply-nested loop within one function - loops inside conditionals - the outer loop has a total of 13 control-flow constructs inside it: 8 loops and 5 ifs.* 3d-raytrace - doubly-nested loops within one function - loop containing a call to a method with the same name as the current function -- this suggests recursion inside a loop, or a callout to a method in the inheritance hierarchy that might loop - loop with 6 function calls and 2 ifs * crypto-aes - (Note: tracing the first 3 loops is fine) - doubly-nested loops within one function - (Note: there are some other loops that look like they should trace well to me, but the best times seem into indicate they should not trace; not sure what's going on here.) * bitops-nsieve-bits - doubly-nested loops within one function * date-format-tofte - loop containing a return statement - loop containing a call to eval - loop containing 4 method calls Note that an equally important analysis that I have not done yet is to identify the loops that are most important *to* trace and their characteristics.
Observations on the loops we really want to trace: * date-format-xparb - loop with a total of 3 operators, including loop test - loop identical to one in tofte that we don't want to trace: 4 function calls, fairly short overall * math-partial-sums - no control flow, no array access, only functions/props called are in Math.