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

今天，共享内存多处理器（smp）的高性能集群被用于处理科学应用的大型数据集。在这些SMP上，结合消息传递和共享内存的混合编程模型通常比纯消息传递效率低，尽管前者更适合SMP体系结构。这项工作的目的是确定smp的内存层次利用效率低下的来源，并优化内存行为。新颖之处在于对动态二进制重写的依赖，即，在应用程序执行时对其进行性能分析和调优。技术挑战是：1)开发动态二进制重写的框架，2)确定在并行性存在的情况下导致执行不确定顺序的一致性流量，3)识别内存瓶颈，4)确定二进制热点数据引用之间的数据依赖关系，5)研究执行二进制的内存改进程序转换的潜力，6)评估大规模基准优化的优点等等。关键的智力优势在于为长时间运行的应用程序提供了额外的动态优化。这项工作的更广泛的影响在于它通过充分利用软件优化来弥补处理器和主存储器速度之间日益增长的差距。

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.