ea540bc210
I went over the output of the following mess of a command:
`(ulimit -m 2000000; ulimit -v 2000000; git ls-files -z | parallel --xargs -0 cat | aspell list --mode=none --ignore-case | grep -E '^[A-Za-z][a-z]*$' | sort | uniq -c | sort -n | grep -vE '.{25}' | aspell pipe -W3 | grep : | cut -d' ' -f2 | less)`
and proceeded to spend a few days looking at it to find probable typos
and fixed a few hundred of them in all of the llvm project (note, the
ones I found are not anywhere near all of them, but it seems like a
good start).
Reviewed By: inclyc
Differential Revision: https://reviews.llvm.org/D131167
69 lines
3.3 KiB
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69 lines
3.3 KiB
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN"
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<html>
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<head> <META http-equiv="Content-Type" content="text/html; charset=ISO-8859-1">
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<title>Polly - Performance</title>
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<link type="text/css" rel="stylesheet" href="menu.css">
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<link type="text/css" rel="stylesheet" href="content.css">
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<body>
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<div id="box">
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<div id="content">
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<h1>Performance</h1>
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<p>To evaluate the performance benefits Polly currently provides we compiled the
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<a href="https://web.cse.ohio-state.edu/~pouchet.2/software/polybench/">Polybench
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2.0</a> benchmark suite. Each benchmark was run with double precision floating
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point values on an Intel Core Xeon X5670 CPU @ 2.93GHz (12 cores, 24 thread)
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system. We used <a href="https://sourceforge.net/projects/pocc/files/">PoCC</a> and the included <a
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href="http://pluto-compiler.sf.net">Pluto</a> transformations to optimize the
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code. The source code of Polly and LLVM/clang was checked out on
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25/03/2011.</p>
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<p>The results shown were created fully automatically without manual
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interaction. We did not yet spend any time to tune the results. Hence
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further improvements may be achieved by tuning the code generated by Polly, the
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heuristics used by Pluto or by investigating if more code could be optimized.
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As Pluto was never used at such a low level, its heuristics are probably
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far from perfect. Another area where we expect larger performance improvements
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is the SIMD vector code generation. At the moment, it rarely yields to
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performance improvements, as we did not yet include vectorization in our
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heuristics. By changing this we should be able to significantly increase the
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number of test cases that show improvements.</p>
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<p>The polybench test suite contains computation kernels from linear algebra
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routines, stencil computations, image processing and data mining. Polly
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recognizes the majority of them and is able to show good speedup. However,
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to show similar speedup on larger examples like the SPEC CPU benchmarks Polly
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still misses support for integer casts, variable-sized multi-dimensional arrays
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and probably several other constructs. This support is necessary as such
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constructs appear in larger programs, but not in our limited test suite.
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<h2> Sequential runs</h2>
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For the sequential runs we used Polly to create a program structure that is
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optimized for data-locality. One of the major optimizations performed is tiling.
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The speedups shown are without the use of any multi-core parallelism. No
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additional hardware is used, but the single available core is used more
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efficiently.
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<h3> Small data size</h3>
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<img src="images/performance/sequential-small.png" /><br />
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<h3> Large data size</h3>
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<img src="images/performance/sequential-large.png" />
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<h2> Parallel runs</h2>
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For the parallel runs we used Polly to expose parallelism and to add calls to an
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OpenMP runtime library. With OpenMP we can use all 12 hardware cores
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instead of the single core that was used before. We can see that in several
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cases we obtain more than linear speedup. This additional speedup is due to
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improved data-locality.
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<h3> Small data size</h3>
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<img src="images/performance/parallel-small.png" /><br />
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<h3> Large data size</h3>
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<img src="images/performance/parallel-large.png" />
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</div>
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</div>
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</body>
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</html>
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