Hybrid analysis of memory references and its application to automatic parallelization

Executing sequential code in parallel on a multithreaded machine has been an elusive goal of the academic and industrial research communities for many years. It has recently become more important due to the widespread introduction of multicores in PCs. Automatic multithreading has not been achieved because classic, static compiler analysis was not powerful enough and program behavior was found to be, in many cases, input dependent. Speculative thread level parallelization was a welcome avenue for advancing parallelization coverage but its performance was not always optimal due to the sometimes unnecessary overhead of checking every dynamic memory reference. In this dissertation we introduce a novel analysis technique, Hybrid Analysis, which unifies static and dynamic memory reference techniques into a seamless compiler framework which extracts almost maximum available parallelism from scientific codes and incurs close to the minimum necessary run time overhead. We present how to extract maximum information from the quantities that could not be sufficiently analyzed through static compiler methods, and how to generate sufficient conditions which, when evaluated dynamically, can validate optimizations. Our techniques have been fully implemented in the Polaris compiler and resulted in whole program speedups on a large number of industry standard benchmark applications

Characterizing the Memory Behavior of Compiler-Parallelized Applications

Compiler-parallelized applications are increasing in importance as moderate-scale multiprocessors become common. This paper evaluates how features of advanced memory systems (e.g., longer cache lines) impact memory system behavior for applications amenable to compiler parallelization. Using full-sized input data sets and applications taken from standard benchmark suites, we measure statistics such as speedups, synchronization and load imbalance, causes of cache misses, cache line utilization, data traffic and memory costs. This exploration allows us to draw several conclusions. First, we find that larger granularity parallelism often correlates with good memory system behavior, good overall performance, and high speedup in these applications. Second, we show that when long (512 byte) cache lines are used, many of these applications suffer from false sharing and low cache line utilization. Third, we identify some of the common artifacts in compiler-parallelized codes that can lead to fa..