Engineering topics
Peterka, T.
Publications and source records attributed to Peterka, T..
Decaf: Decoupled Dataflows for In Situ High-Performance Workflows
Decaf is a dataflow system for the parallel communication of coupled tasks in an HPC workflow. The dataflow can perform arbitrary data transformations ranging from simply forwarding data to complex data redistribution. Decaf does this by allowing the user to allocate resources and execute custom code in the dataflow. All communication through the dataflow is efficient parallel message passing over MPI. The runtime for calling tasks is entirely message-driven; Decaf executes a task when all messages for the task have been received. Such a messagedriven runtime allows cyclic task dependencies in the workflow graph, for example, to enact computational steering based on the result of downstream tasks. Decaf includes a simple Python API for describing the workflow graph. This allows Decaf to stand alone as a complete workflow system, but Decaf can also be used as the dataflow layer by one or more other workflow systems to form a heterogeneous task-based computing environment. In one experiment, we couple a molecular dynamics code with a visualization tool using the FlowVR and Damaris workflow systems and Decaf for the dataflow. In another experiment, we test the coupling of a cosmology code with Voronoi tessellation and density estimation codes using MPI for the simulation, the DIY programming model for the two analysis codes, and Decaf for the dataflow. Such workflows consisting of heterogeneous software infrastructures exist because components are developed separately with different programming models and runtimes, and this is the first time that such heterogeneous coupling of diverse components was demonstrated in situ on HPC systems.
Computing Just What you Need: Online Data Analysis and Reduction at Extreme Scales
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Simultaneously Synchrotron X-ray Fluorescence and Ptychographic Imaging of Frozen Biological Single Cells
Bionanoprobe (BNP), a hard x-ray fluorescence sample-scanning nanoprobe at the Advanced Photon Source of Argonne National Laboratory, has been used to quantitatively study elemental distributions in biological cells with sub-100 nm spatial resolution and high sensitivity. Cryogenic conditions enable biological samples to be studied in their frozen-hydrated state with both ultrastructure and elemental distributions more faithfully preserved compared to conventional chemical fixation or dehydration methods. Furthermore, radiation damage is reduced in two ways: the diffusion rate of free radicals is decreased at low temperatures; and the sample is embedded in vitrified ice, which reduces mass loss.
Advances and challenges in cryo ptychography at the Advanced Photon Source
Ptychography has emerged as a nondestructive tool to quantitatively study extended samples at a high spatial resolution. In this manuscript, we report on recent developments from our team. We have combined cryo ptychography and fluorescence microscopy to provide simultaneous views of ultrastructure and elemental composition, we have developed multi-GPU parallel computation to speed up ptychographic reconstructions, and we have implemented fly-scan ptychography to allow for faster data acquisition. We conclude with a discussion of future challenges in high-resolution 3D ptychography.