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// See if the letter is in the player's hand. Tile^ tile = gcnew Tile(letter); Tile^ tileToRemove = nullptr; bool tileFound = false; for each (Tile^ t in workingTiles) { if (t->LetterValue == tile->LetterValue) { tileToRemove = t; tileFound = true; break; } } if ( tileFound == true) { workingTiles->Remove( tileToRemove ); bag->Add(tile); } else // The letter was not found. { Console::WriteLine("You do not have enough {0}s to pass back.", letter); Console::WriteLine("Press any key to continue..."); Console::ReadLine(); return false; } } while (code != safe_cast<int>('\n')); // if bag->Count == 0

5,030 2,779 1,852 1,389 1,111 926 794 695

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Ignore, for the moment, the last two columns, which are reporting the results with CPU costing enabled, and focus on the first three sets of costs. You will notice that the values for 9i and 10g don t quite agree with each other, but at least they are reasonably close to (serial cost / degree of parallelism). But the most obvious feature is that the cost in 8i never changes, no matter what the degree of parallelism. Essentially, 8i costs and optimizes your query for the best serial path, and then runs it in parallel. On the other hand, 9i assumes that it can execute a completely collision-free 100% parallel run, effectively optimizing for a set of data downsized by a factor of (degree of parallelism). Allowing for small rounding errors, the numbers from 10g suggest that it has introduced a parallel efficiency factor of 90% in the arithmetic. The actual figures suggest the following version-dependent formulae for a parallel tablescan:

Follow the same steps you did earlier when you opened the word processing document. Sheet to Go View Mode:

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8i 9i 10g Cost at degree N = serial cost Cost at degree N = ceil(serial cost / N ) Cost at degree N = ceil(serial cost / (0.9 * N))

Console::Write("Are you sure you want to pass (Y/N) "); String^ response = Console::ReadLine(); if (response->StartsWith( "Y") || response->StartsWith("y")) { if (bag->Count > 0) { Console::Write("{0} draws tiles: ", players[playerNum]->Name); // Copy the working tiles to the player tiles. players[playerNum]->tiles = workingTiles; while ( players[playerNum]->tiles->Count < MAX_TILES_IN_HAND) { Tile^ tile = DrawTile(true); if (tile != nullptr) { players[playerNum]->tiles->Add(tile); Console::Write(" {0} ", tile->ToString()); }

Opened with Sheet to Go (you can view formulas and edit).

This leaves you with three issues to consider. First, 8i is not optimizing parallel queries properly. Second, as you upgrade, costs of parallel execution can change dramatically, and you may not be able to predict the side effects. Third, the arithmetic used by 9i assumes that there will be absolutely no interference between parallel execution slaves and that s not going to be very realistic in most cases. The change between 8i and 9i is controlled by parameter optimizer_percent_parallel (hidden from 9i onwards as _optimizer_percent_parallel). In 8i, the default value was zero in other words, calculate as serial. In 9i the default value is 101 which causes Oracle to perform its calculations on the basis of 100% parallelism. You can, in theory, set the parameter to any value between 0 and 101. If you do, then the optimizer does a straight-line interpolation between the serial cost (the Resc figure in a

Sheet to Go Edit Mode:

Note An important detail that is often overlooked when people are thinking about nested loops is that we have picked some columns from the first table before we start our search in the second table, so we may be able to use more precise access paths into the second table. (There was a time when Oracle used to advise prefixing of local indexes on partitioned table for exactly this reason but that ceased to be an automatic concomitant of partitioning a long time ago.)

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