Hierarchical Model Validation of Symbolic Performance Models of Scientific Kernels
Hierarchical Model Validation of Symbolic Performance Models of Scientific Kernels
复制标题
科学内核符号性能模型的分层模型验证
DOI：
10.1007/11823285_8
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
J. Vetter
中科院分区：
文献类型：
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作者：
S. Alam;J. Vetter
科学应用的分层性能模型的多分辨率验证是至关重要的，主要有两个原因。首先，逐步验证确定科学模拟中所有重要组件或阶段的正确性。其次，在多个分辨率级别验证的模型是生成预测性能模型的第一步，不仅适用于现有系统，还适用于新兴系统和未来问题规模。我们提出了两个科学基准的分层性能模型的设计和验证，使用一种新的技术称为建模断言（MA）。我们的MA原型框架生成的符号性能模型，可以有效地通过在Octave和MATLAB中生成等效的模型表示进行评估。多分辨率建模和验证是在两个当代的，并行的系统，XT 3和蓝色基因/L系统。通过MPP平台上收集的实验数据证实了MA模型产生的工作量分布和增长率预测。此外，由MA模型生成的物理内存要求通过Blue Gene/L系统上的运行时间值进行验证，该系统在其两种独特的执行模式下具有512 MB和256 MB的物理内存容量。
Multi-resolution validation of hierarchical performance models of scientific applications is critical primarily for two reasons. First, the step-by-step validation determines the correctness of all essential components or phases in a science simulation. Second, a model that is validated at multiple resolution levels is the very first step to generate predictive performance models, for not only existing systems but also for emerging systems and future problem sizes. We present the design and validation of hierarchical performance models of two scientific benchmarks using a new technique called the modeling assertions (MA). Our MA prototype framework generates symbolic performance models that can be evaluated efficiently by generating the equivalent model representations in Octave and MATLAB. The multi-resolution modeling and validation is conducted on two contemporary, massively-parallel systems, XT3 and Blue Gene/L system. The workload distribution and the growth rates predictions generated by the MA models are confirmed by the experimental data collected on the MPP platforms. In addition, the physical memory requirements that are generated by the MA models are verified by the runtime values on the Blue Gene/L system, which has 512 MBytes and 256 MBytes physical memory capacity in its two unique execution modes.