Thicket is an open-source Python toolkit for Exploratory Data Analysis (EDA) of multi-run performance experiments. It enables an understanding of optimal performance configuration for large-scale application codes. Most performance tools focus on a single execution (e.g., single platform, single measurement tool, single scale). Thicket bridges the gap to convenient analysis in multi-dimensional, multi-scale, multi-architecture, and multi-tool performance datasets by providing an interface for interacting with the performance data. Thicket has a modular structure composed of three components. The first component is a data structure for multi-dimensional performance data, which is composed automatically on the portable basis of call trees, and accommodates any subset of dimensions present in the dataset. The second is the metadata, enabling distinction and sub-selection of dimensions in performance data. The third is a dimensionality reduction mechanism, enabling analysis such as computing aggregated statistics on a given data dimension. Extensible mechanisms are available for applying analyses (e.g., top-down on Intel CPUs), data science techniques (e.g., K-means clustering from scikit-learn), modeling performance (e.g., Extra-P), and interactive visualization. We demonstrate the power and flexibility of Thicket through two case studies, first with the open-source RAJA Performance Suite on CPU and GPU clusters and another with a large physics simulation run on both a traditional HPC cluster and an AWS Parallel Cluster instance.

As computational science applications benefit from larger-scale, more heterogeneous high performance computing (HPC) systems, the process of studying their performance becomes increasingly complex. The performance data analysis library Hatchet provides some insights into this complexity, but is currently limited in its analysis capabilities. Missing capabilities include the handling of relational caller-callee data captured by HPC profilers. To address this shortcoming, we augment Hatchet with a Call Path Query Language that leverages relational data in the performance analysis of scientific applications. Specifically, our Query Language enables data reduction using call path pattern matching. We demonstrate the effectiveness of our Query Language in identifying performance bottlenecks and enhancing Hatchet's analysis capabilities through three case studies. In the first case study, we compare the performance of sequential and multi-threaded versions of the graph alignment application Fido. In doing so, we identify the existence of large memory inefficiencies in both versions. In the second case study, we examine the performance of MPI calls in the linear algebra mini-application AMG2013 when using MVAPICH and Spectrum-MPI. In doing so, we identify hidden performance losses in specific MPI functions. In the third case study, we illustrate the use of our Query Language in Hatchet's interactive visualization. In doing so, we show that our Query Language enables a simple and intuitive way to massively reduce profiling data.

Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks.