Performance models help us to understand how HPC applications scale, which is crucial for efficiently utilizing HPC resources. They describe the performance (e.g., runtime) as a function of one or more execution parameters (e.g., problem size and the degree of parallelism). Creating one manually for a given program is challenging and time-consuming. Automatically learning a model from performance data is a viable alternative, but potentially resource-intensive. Extra-P is a tool that implements this approach. The user begins by selecting values for each parameter. Each combination of values defines a possible measurement point. The choice of measurement points affects the quality and cost of the resulting models, creating a complex optimization problem. A naive approach takes measurements for all possible measurement points, the number of which grows exponentially with the number of parameters. In our earlier work, we demonstrated that a quasi-linear number of points is sufficient and that prioritizing the least expensive points is a generic strategy with a good trade-off between cost and quality. Here, we present an improved selection strategy based on Gaussian process regression (GPR) that selects points individually for each modeling task. In our synthetic evaluation, which was based on tens of thousands of artificially generated functions, the naive approach achieved 66% accuracy with two model parameters and 5% artificial noise. At only 10% of the naïve approach's cost, the generic approach already achieved 47.3% accuracy, while the GPR-based approach achieved even 77.8% accuracy. Similar improvements were observed in experiments involving different numbers of model parameters and noise levels, as well as in case studies with realistic applications.

Empirical performance modeling is a proven instrument to analyze the scaling behavior of HPC applications. Using a set of smaller-scale experiments, it can provide important insights into application behavior at larger scales. Extra-P is an empirical modeling tool that applies linear regression to automatically generate human-readable performance models. Similar to other regression-based modeling techniques, the accuracy of the models created by Extra-P decreases as the amount of noise in the underlying data increases. This is why the performance variability observed in many contemporary systems can become a serious challenge. In this paper, we introduce a novel adaptive modeling approach that makes Extra-P more noise resilient, exploiting the ability of deep neural networks to discover the effects of numerical parameters, such as the number of processes or the problem size, on performance when dealing with noisy measurements. Using synthetic analysis and data from three different case studies, we demonstrate that our solution improves the model accuracy at high noise levels by up to 25% while increasing their predictive power by about 15%.

Identifying scalability bottlenecks in parallel applications is a vital but also laborious and expensive task. Empirical performance models have proven to be helpful to find such limitations, though they require a set of experiments in order to gain valuable insights. Therefore, the experiment design determines the quality and cost of the models. Extra-P is an empirical modeling tool that uses small-scale experiments to assess the scalability of applications. Its current version requires an exponential number of experiments per model parameter. This makes the creation of empirical performance models very expensive, and in some situations even impractical. In this paper, we propose a novel parameter-value selection heuristic, which functions as a guideline for the experiment design, leveraging sparse performance-modeling, a technique that only needs a polynomial number of experiments per model parameter. Using synthetic analysis and data from three different case studies, we show that our solution reduces the average modeling costs by about 85% while retaining 92% of the model accuracy.

The cost of maintaining an application code would significantly increase if the application code is branched into multiple versions, each of which is optimized for a different architecture. In this work, default and vector versions of a realworld application code are refactored to be a single version, and the differences between the versions are expressed as user-defined code transformations. As a result, application developers can maintain only the single version, and transform it to its vector version just before the compilation. Although code optimizations for a vector processor are sometimes different from those for other processors, application developers can enjoy the performance of the vector processor without increasing the code complexity. Evaluation results demonstrate that vectorization-aware loop optimization for a vector processor can be expressed as user-defined code transformation rules, and thereby significantly improve the performance of a vector processor without major code modifications.