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3 <!-- Material used from: HTML 4.01 specs: http://www.w3.org/TR/html401/ -->
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4 <html>
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5 <head> <META http-equiv="Content-Type" content="text/html; charset=ISO-8859-1">
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6 <title>Polly - Performance</title>
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7 <link type="text/css" rel="stylesheet" href="menu.css">
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8 <link type="text/css" rel="stylesheet" href="content.css">
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9 </head>
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10 <body>
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11 <div id="box">
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12 <!--#include virtual="menu.html.incl"-->
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13 <div id="content">
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14 <h1>Performance</h1>
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15
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16 <p>To evaluate the performance benefits Polly currently provides we compiled the
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17 <a href="https://web.cse.ohio-state.edu/~pouchet.2/software/polybench/">Polybench
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18 2.0</a> benchmark suite. Each benchmark was run with double precision floating
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19 point values on an Intel Core Xeon X5670 CPU @ 2.93GHz (12 cores, 24 thread)
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20 system. We used <a href="https://sourceforge.net/projects/pocc/files/">PoCC</a> and the included <a
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21 href="http://pluto-compiler.sf.net">Pluto</a> transformations to optimize the
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22 code. The source code of Polly and LLVM/clang was checked out on
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23 25/03/2011.</p>
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24
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25 <p>The results shown were created fully automatically without manual
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26 interaction. We did not yet spend any time to tune the results. Hence
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27 further improvements may be achieved by tuning the code generated by Polly, the
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28 heuristics used by Pluto or by investigating if more code could be optimized.
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29 As Pluto was never used at such a low level, its heuristics are probably
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30 far from perfect. Another area where we expect larger performance improvements
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31 is the SIMD vector code generation. At the moment, it rarely yields to
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32 performance improvements, as we did not yet include vectorization in our
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33 heuristics. By changing this we should be able to significantly increase the
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34 number of test cases that show improvements.</p>
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35
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36 <p>The polybench test suite contains computation kernels from linear algebra
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37 routines, stencil computations, image processing and data mining. Polly
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38 recognizes the majority of them and is able to show good speedup. However,
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39 to show similar speedup on larger examples like the SPEC CPU benchmarks Polly
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40 still misses support for integer casts, variable-sized multi-dimensional arrays
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41 and probably several other constructs. This support is necessary as such
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42 constructs appear in larger programs, but not in our limited test suite.
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43
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44 <h2> Sequential runs</h2>
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45
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46 For the sequential runs we used Polly to create a program structure that is
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47 optimized for data-locality. One of the major optimizations performed is tiling.
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48 The speedups shown are without the use of any multi-core parallelism. No
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49 additional hardware is used, but the single available core is used more
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50 efficiently.
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51 <h3> Small data size</h3>
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52 <img src="images/performance/sequential-small.png" /><br />
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53 <h3> Large data size</h3>
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54 <img src="images/performance/sequential-large.png" />
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55 <h2> Parallel runs</h2>
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56 For the parallel runs we used Polly to expose parallelism and to add calls to an
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57 OpenMP runtime library. With OpenMP we can use all 12 hardware cores
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58 instead of the single core that was used before. We can see that in several
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59 cases we obtain more than linear speedup. This additional speedup is due to
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60 improved data-locality.
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61 <h3> Small data size</h3>
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62 <img src="images/performance/parallel-small.png" /><br />
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63 <h3> Large data size</h3>
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64 <img src="images/performance/parallel-large.png" />
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65 </div>
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66 </div>
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67 </body>
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68 </html>
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