As next-generation experimental and observational instruments for scientific research are being deployed with higher resolutions and faster data capture rates, the fundamental demands of producing high-quality scientific throughput require portability and performance to meet the high productivity goals. Understanding such a workflow’s end-to-end performance on HPC systems is formidable work. In this paper, we address this challenge by introducing a Workflow Roofline model, which ties a workflow’s end-to-end performance with peak node- and system-performance constraints. We analyze four workflows: LCLS, a time-sensitive workflow that is bound by system external bandwidth; BerkeleyGW, a traditional HPC workflow that is bound by node-local performance; CosmoFlow, an AI workflow that is bound by the CPU preprocessing; and GPTune, an auto tuner that is bound by the data control flow. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on workflows and systems and motivate workflow optimizations.

X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data-collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min - sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS-NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Completing data analysis within 10 mins is a first for XFEL experiments and an important milestone if we are to keep up with data-collection trends.

Summary The upcoming exascale computing systems Frontier and Aurora will draw much of their computing power from GPU accelerators. The hardware for these systems will be provided by AMD and Intel, respectively, each supporting their own GPU programming model. The challenge for applications that harness one of these exascale systems will be to avoid lock‐in and to preserve performance portability. We report here on our results of using Kokkos to accelerate a real‐world application on NERSC's Perlmutter Phase 1 (using NVIDIA A100 accelerators) and Crusher, the testbed system for OLCF's Frontier (using AMD MI250X). By porting to Kokkos, we successfully ran the same X‐ray tracing code on both systems and achieved speed‐ups between 13 % and 66 % compared to the original CUDA code. These results are a highly encouraging demonstration of using Kokkos to accelerate production science code.

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.