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Probabilistic pointer analysis (PPA) is a compile-time analysis method that estimates the probability that a points-to relationship will hold at a particular program point. The results are useful for optimizing and parallelizing compilers, which need to quantitatively assess the profitability of transformations when performing aggressive optimizations and parallelization. This paper presents a PPA technique using the static single assignment (SSA) form. When computing the probabilistic points-to relationships of a specific pointer, a pointer relation graph (PRG) is first built to represent all of the possible points-to relationships of the pointer. The PRG is transformed by a sequence of reduction operations into a compact graph, from which the probabilistic points-to relationships of the pointer can be determined. In addition, PPA is further extended to interprocedural cases by considering function related statements. We have implemented our proposed scheme including static and profiling versions in the Open64 compiler, and performed experiments to obtain the accuracy and scalability. The static version estimates branch probabilities by assuming that every conditional is equally likely to be true or false, and that every loop executes 10 times before terminating. The profiling version measures branch probabilities dynamically from past program executions using a default workload provided with the benchmark. The average errors for selected benchmarks were 3.80 percent in the profiling version and 9.13 percent in the static version. Finally, SPEC CPU2006 is used to evaluate the scalability, and the result indicates that our scheme is sufficiently efficient in practical use. The average analysis time was 35.59 seconds for an average of 98,696 lines of code. [ABSTRACT FROM PUBLISHER]

Abstract: Effective address calculations for load and store instructions need to compete for ALU with other instructions and hence extra latencies might be incurred to data cache accesses. Fast address generation is an approach proposed to reduce cache access latencies. This paper presents a fast address generator that can eliminate most of the effective address computations by storing computed effective addresses of previous load/store instructions in a dummy register file. Experimental results show that this fast address generator can reduce effective address computations of load and store instructions by about 74% on average for SPECint2000 benchmarks and cut the execution times by 8.5%. Furthermore, when multiple dummy register files are deployed, this fast address generator eliminates over 90% of effective address computations of load and store instructions and improves the average execution times by 9.3%. [Copyright &y& Elsevier]

This paper proposes a simple yet efficient algorithm to distribute loads evenly on multiprocessor computers with hypercube interconnection networks. This algorithm was developed based upon the well-known dimension exchange method. However, the error accumulation suffered by other algorithms based on the dimension exchange method is avoided by exploiting the notion of regular distributions, which are commonly deployed for data distributions in parallel programming. This algorithm achieves a perfect load balance over P processors with an error of 1 and the worst-case time complexity of O(M log2P), where M is the maximum number of tasks initially assigned to each processor. Furthermore, perfect load balance is achieved over subcubes as well—once a hypercube is balanced, if the cube is decomposed into two subcubes by the lowest bit of node addresses, then the difference between the numbers of the total tasks of these subcubes is at most 1. [ABSTRACT FROM AUTHOR]