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Writing portable programs that perform well on multiple platforms or for varying input sizes and types can be very difficult because performance is often sensitive to the system architecture, the runtime environment, and input data characteristics. This is even more challenging on parallel and distributed systems due to the wide variety of system architectures. One way to address this problem is to adaptively select the best parallel algorithm for the current input data and system from a set of functionally equivalent algorithmic options. Toward this goal, we have developed a general framework for adaptive algorithm selection for use in the Standard Template Adaptive Parallel Library (STAPL). Our framework uses machine learning techniques to analyze data collected by STAPL installation benchmarks and to determine tests that will select among algorithmic options at run-time. We apply a prototype implementation of our framework to two important parallel operations, sorting and matrix multiplication, on multiple platforms and show that the framework determines run-time tests that correctly select the best performing algorithm from among several competing algorithmic options in 86-100% of the cases studied, depending on the operation and the system.

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Speculative thread-level parallelization is a promising way to speed up codes that compilers fail to parallelize. While several speculative parallelization schemes have been proposed for different machine sizes and types of codes, the results so far show that it is hard to deliver scalable speedups. Often, the problem is not true dependence violations, but sub-optimal architectural design. Consequently, we attempt to identify and eliminate major architectural bottlenecks that limit the scalability of speculative parallelization. The solutions that we propose are: low-complexity commit in constant time to eliminate the task commit bottleneck, a memory-based overflow area to eliminate stall due to speculative buffer overflow, and exploiting high-level access patterns to minimize speculationinduced traffic. To show that the resulting system is truly scalable, we perform simulations with up to 128 processors. With our optimizations, the speedups for 128 and 64 processors reach 63 and 48, respectively. The average speedup for 64 processors is 32, nearly four times higher than without our optimizations.