CAREER:Exploiting Binary Rewriting to Analyze and Alleviate Memory Bottlenecks for Scientific Applications

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Today, high-performance clusters of shared-memory multiprocessors (SMPs)are employed to cope with large data sets for scientificapplications. On these SMPs, hybrid programming models combingmessage passing and shared memory are often less efficient than puremessage passing although the former fits SMP architectures moreclosely.The objective of this work is to determine the sources ofinefficiencies in utilizing memory hierarchies of SMPs and to optimizememory behavior. The novelty lies in the reliance ondynamic binary rewriting, i.e., performance analysis and tuning areperformed on the application while it executes.The technical challenges are to1) develop a framework for dynamic binary rewriting,2) determine coherence traffic resulting in misses fornon-deterministic orders of executions in the presence of parallelism,3) identify memory bottlenecks,4) determine data dependencies between data references forhot-spots for binaries,5) study the potential for memory-improving program transformationson the executing binary and6) evaluate the merits of optimizations for large-scale benchmarksamong others.The key intellectual merit is in providing additional, dynamicoptimizations for long-running applications. The broader impact ofthis work lies in its contribution to counter the increasing gapbetween processor and main memory speeds by fullyexploiting software optimizations.