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 scientific applications. On these SMPs, hybrid programming models combing message passing and shared memory are often less efficient than pure message passing although the former fits SMP architectures more closely.

The objective of this work is to determine the sources of inefficiencies in utilizing memory hierarchies of SMPs and to optimize memory behavior. The novelty lies in the reliance on dynamic binary rewriting, i.e., performance analysis and tuning are performed on the application while it executes.

The technical challenges are to

  1. develop a framework for dynamic binary rewriting,
  2. determine coherence traffic resulting in misses for non-deterministic orders of executions in the presence of parallelism,
  3. identify memory bottlenecks,
  4. determine data dependencies between data references for hot-spots for binaries,
  5. study the potential for memory-improving program transformations on the executing binary and
  6. evaluate the merits of optimizations for large-scale benchmarks among others.

The key intellectual merit is in providing additional, dynamic optimizations for long-running applications. The broader impact of this work lies in its contribution to counter the increasing gap between processor and main memory speeds by fully exploiting software optimizations.

Another focus of this work is on concurrency services for middleware, specifically fully decentralized mutual exclusion in the presence of multi-mode locking. Theses:
"This material is based upon work supported by the National Science Foundation under Grant No. 0237570."

"Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation."