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copied!<p>For profiling you can add quite a few options to the app when you run it. Firstly you can add "+RTS -sstderr". This gives you the broad stats that you described so I guess you already know this!</p> <pre><code>1,835,837,744 bytes allocated in the heap 328,944,448 bytes copied during GC 2,908,728 bytes maximum residency (25 sample(s)) 142,056 bytes maximum slop 9 MB total memory in use (1 MB lost due to fragmentation) Generation 0: 3483 collections, 0 parallel, 1.54s, 1.54s elapsed Generation 1: 25 collections, 0 parallel, 0.09s, 0.07s elapsed INIT time 0.00s ( 0.00s elapsed) MUT time 3.04s ( 3.13s elapsed) GC time 1.63s ( 1.61s elapsed) RP time 0.00s ( 0.00s elapsed) PROF time 0.00s ( 0.00s elapsed) EXIT time 0.00s ( 0.00s elapsed) Total time 4.67s ( 4.74s elapsed) %GC time 34.9% (34.0% elapsed) Alloc rate 603,893,994 bytes per MUT second Productivity 65.1% of total user, 64.2% of total elapsed </code></pre> <p>As you can see from the example output above, I write some pretty bad Haskell. You can delve into this more with some additional options and improve things.</p> <ol> <li>-prof - Enables profiling</li> <li>-caf-all - Constant Applicative form for all top-level items (constant costs, one for each module.)</li> <li>-auto-all - Cost-centre analysis for every top-level function</li> </ol> <p>Once you get the profiling information out, you can print out pretty pictures like the one below (<a href="http://www.haskell.org/ghc/docs/6.12.2/html/users_guide/hp2ps.html" rel="nofollow noreferrer">hp2ps</a>). This shows allocation rates. As you can see from below it's pretty simple to find the function that's doing too much work. </p> <p><img src="https://i.stack.imgur.com/80aUg.png" alt="Picture of the allocation rate of various functions"></p> <p>Whilst this doesn't give per type information, it does help isolate what's happening. I found <a href="http://book.realworldhaskell.org/read/profiling-and-optimization.html" rel="nofollow noreferrer">Real World Haskell</a> to be tremendously useful.</p>

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