Today we continue to discuss the WRL research report on Swift Java Compiler. We were discussing the loop peeling method of optimization. Part of the first iteration or more is peeled into code preceding the loop. It involves a search phase that starts from the highly nested loop for each loop. It evaluates the faulting operations to determine the candidates. There are a few other requirements as well. The procedure of loop peeling is straight forward. The blocks are copied to the new header. Control flow edges are redirected to the peel copy. Phi nodes are created in the new loop header or start and edges joined from the peel copy to the original. If the addition of phi nodes cascade down , the peeling is aborted. There is an alternative to estimate the number of blocks to add beforehand by running the phi node placement algorithm.
We now discuss pattern based peephole optimizer. Swift contains several passes that do pattern-based transformations of the SSA graph. A pattern matcher generator is one that takes a set of graph rewriting rules as input and produces a compiler pass which transforms the SSA graph according to those rules. Each rule consists of a pattern matcher that can match a piece of an SSA graph and some Java code which is executed when the pattern matches. The language used to describe the patterns allows matching of node operations, auxiliary operations and types, and allows nested matching on the inputs to a node. Matching of an operation, a list of operations, a particular operation, or any operations not in a list are alloed. Similar matching capabilities exist for type.Binding of a variable to a particular value in a pattern such that it can be used in the action part of the rule is permitted. The top level value is matched by the pattern is available in the action via the keyword self. Moreover the action can mutate the self value or any of its inputs.
#coding exercise
GetAlternateOddNumberRangeProductCubeRootEighthPower (Double [] A)
{
if (A == null) return 0;
Return A.AlternateOddNumberRangeProductCubeRootEighthPower();
}
#coding exercise
GetAlternateOddNumberRangeProductCubeRootSixthPower (Double [] A)
{
if (A == null) return 0;
Return A.AlternateOddNumberRangeProductCubeRootSixthPower();
}
We now discuss pattern based peephole optimizer. Swift contains several passes that do pattern-based transformations of the SSA graph. A pattern matcher generator is one that takes a set of graph rewriting rules as input and produces a compiler pass which transforms the SSA graph according to those rules. Each rule consists of a pattern matcher that can match a piece of an SSA graph and some Java code which is executed when the pattern matches. The language used to describe the patterns allows matching of node operations, auxiliary operations and types, and allows nested matching on the inputs to a node. Matching of an operation, a list of operations, a particular operation, or any operations not in a list are alloed. Similar matching capabilities exist for type.Binding of a variable to a particular value in a pattern such that it can be used in the action part of the rule is permitted. The top level value is matched by the pattern is available in the action via the keyword self. Moreover the action can mutate the self value or any of its inputs.
#coding exercise
GetAlternateOddNumberRangeProductCubeRootEighthPower (Double [] A)
{
if (A == null) return 0;
Return A.AlternateOddNumberRangeProductCubeRootEighthPower();
}
#coding exercise
GetAlternateOddNumberRangeProductCubeRootSixthPower (Double [] A)
{
if (A == null) return 0;
Return A.AlternateOddNumberRangeProductCubeRootSixthPower();
}
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