Instruction schedulers employ code motion as a means of instruction reordering to enable scheduling of instructions at points where the resources required for their execution are available. In addition, driven by the profiling data, schedulers take advantage of predication and speculation for aggressive code motion across conditional branches. Optimization algorithms for partial dead code elimination (PDE) and partial redundancy elimination (PRE) employ code sinking and hoisting to enable optimization. However, unlike instruction scheduling, these optimization algorithms are unaware of resource availability and are incapable of exploiting profiling information, speculation, and predication. In this paper we develop data flow algorithms for performing the above optimizations with the following characteristics: (i) opportunities for PRE and PDE enabled by hoisting and sinking are exploited; (ii) hoisting and sinking of a code statement is driven by availability of functional unit resources; (iii) predication and speculation is incorporated to allow aggressive hoisting and sinking; and (iv) path profile information guides predication and speculation to enable optimization.

Traditional approach for generating loop transformation matrix, which is based upon the computation of distance vectors or direction vectors, does not work for those nested loops whose distance vectors are uncomputable and direction vectors contain no useful information. In this paper, we present a new technique for generating transformation matrix that is based upon identifying certain types of linear equations or inequalities of distance vectors. Two issues related to this technique are discussed in this paper: 1) Given a nested loop how to identify these linear equations or inequalities; 2) Given such a linear equation or inequality how to generate a legal and unimodular transformation matrix for the purpose of loop parallelization.

Parallel processing systems with cache or local memory in the memory hierarchies are considered. These systems have a local cache memory in each processor and usually employ a write-invalidate protocol for the cache coherence. In such systems, a problem called 'cache or local memory thrashing' can arise in executions of parallel programs, when the data unnecessarily moves back and forth between the caches or local memories in different processors. An approach to eliminate, or at least to reduce, such movement for nested parallel loops is presented. It is based on relations between array element accesses and enclosed loop indexes in the loops. The relations can be used to assign processors to execute the appropriate iterations for parallel loops in the loop nests with respect to the data in their caches or local memories. An algorithm for calculating the correct iteration of the parallel loop in terms of loop indexes of the previous iterations executed in the processor is presented. This method benefits parallel code with nested loop structures in a wide range of applications. The experimental results show that the technique can achieve speedups up to 2.>