Data interpretation and experiment planning in performance tools
Allen D. Malony · ACM SIGMETRICS Performance Evaluation Review · 1995
The parallel scientific computing community is placing increasing emphasis on portability and scalability of programs, languages, and architectures. This creates new challenges for developers of parallel performance analysis tools, who will have to deal with increasing volumes of performance data drawn from diverse platforms. One way to meet this challenge is to incorporate sophisticated facilities for data interpretation and experiment planning within the tools themselves, giving them increased flexibility and autonomy in gathering and selecting performance data. This panel discussion brings together four research groups that have made advances in this direction.