Christiaan Gribble
Principal Research Scientist

Applied Technology Operation
SURVICE Engineering Company

Mailing address:
     1051 Brinton Road, Suite 301
     Pittsburgh, PA  15221

Phone numbers:
     412.342.8219 (office)
     410.272.6763 (fax)

E-mail address:
    

 


[ home ]   [ research ]   [ vita ]

 

2020


     Implementing a Prototype System for 3D Reconstruction of Compressible Flow

Christiaan Gribble, Victor Eijkhout, and Paul Navratil

Practice and Experience in Advanced Research Computing, July 2020 (to appear)

PowerFlow3D is a prototype system for acquiring, reconstructing, and visualizing three-dimensional structure of complex flows around objects in wind tunnel test procedures. PowerFlow3D combines modern high-performance computing (HPC) with existing acquisition, reconstruction, and visualization methods to provide a foundational capability that helps to reveal critical information about the underlying structure of unknown flows. We describe the implementation of our system, focusing on tomographic reconstruction, in particular, and highlight the practical challenges encountered throughout our initial research and development (R&D) process. The resulting prototype achieves both reasonable performance and fidelity and provides opportunities for enhanced performance, fidelity, and scale. The results of this initial R&D effort thus enable continued progress toward a scalable HPC-accelerated system for guiding real-time decisions during wind tunnel tests.
     
     Effective Parallelization Strategies for Scalable, High-Performance Iterative Reconstruction

Christiaan Gribble

Eurographics Symposium on Parallel Graphics and Visualization, May 2020

Iterative reconstruction techniques in X-ray computed tomography converge to a result by successively refining increasingly accurate estimates. Compared to alternative approaches, iterative reconstruction imposes significant computational demand but generally leads to higher reconstruction quality and is more robust to inherently imperfect scan data. We explore several strategies for exploiting parallelism in iterative reconstruction and evaluate their scalability and performance on modern workstation-class systems. Results show that scalable, high performance iterative reconstruction is possible with careful attention to the expression of parallelism in both the projection and backprojection phases of computation.

Supplemental materials, including information about the source code for our prototype XCT reconstruction system and scaling results for TP1 and TP2, are also available.
     
     Using Robust Networks to Inform Lightweight Models in Semi-Supervised Learning for Object Detection

Jonathan Worobey, Shawn Recker, and Christiaan Gribble

Poster, GPU Technology Conference, March 2020

We propose a semi-supervised model training approach that temporarily utilizes the capacity of robust networks to efficiently train low latency models with limited hand-labeled data and a larger pool of unlabeled data. This approach results in more accurate lightweight models with minimal cost from hand-labeled data while also providing an efficient way of curating ground-truth datasets.

 
 
2019


     Using Robust Networks to Inform Lightweight Models in Semi-Supervised Learning for Object Detection

Jonathan Worobey, Shawn Recker, and Christiaan Gribble

Applied Imagery Pattern Recognition Workshop, October 2019

A common trade-off among object detection algorithms is accuracy-for-speed (or vice versa). To meet our application’s real-time requirement, we use a Single Shot MultiBox Detector (SSD) model. This architecture meets our latency requirements; however, a large amount of training data is required to achieve an acceptable accuracy level. While unusable for our end application, more robust network architectures, such as Regions with CNN features (R-CNN), provide an important advantage over SSD models—they can be more reliably trained on small datasets. By fine-tuning R-CNN models on a small number of hand-labeled examples, we create new, larger training datasets by running inference on the remaining unlabeled data. We show that these new, inferenced labels are beneficial to the training of lightweight models. These inferenced datasets are imperfect, and we explore various methods of dealing with the errors, including hand-labeling mislabeled data, discarding poor examples, and simply ignoring errors. Further, we explore the total cost, measured in human and computer time, required to execute this workflow compared to a hand-labeling baseline.
     
     Physical Simulation via Hardware-Optimized Ray Tracing Engines

Christiaan Gribble

Keynote presentation, Solar Ray Tracing Consortium, May 2019

We discuss two recent applications of hardware-optimized ray tracing engines to problems in physical simulation—or so-called non-optical rendering—at SURVICE Engineering. In the first, we leverage multi-hit ray tracing to compute ray/surface intervals and thus simulate phenomena governed by equations similar to the Beer-Lambert Law. In the second, we leverage fast ray marching to relate three-dimensional (3D) ray deflections to 3D refractive index gradients via line integrals and thus recover 3D refractive index distributions in gradient field tomography. In each case, modern high-performance ray tracing engines are used to not only visualize simulation results, but to effectively and efficiently solve the underlying problem at hand.
     
     Multi-Hit Ray Tracing in DXR

Christiaan Gribble

Book chapter, Ray Tracing Gems:  High-Quality and Real-Time Rendering with DXR and Other APIs, February 2019

Multi-hit ray traversal is a class of ray traversal algorithm that finds one or more, and possibly all, primitives intersected by a ray ordered by point of intersection. Multi-hit traversal generalizes traditional first-hit ray traversal and is useful in computer graphics and physics-based simulation. We present several possible multi-hit implementations using Microsoft DirectX Raytracing and explore the performance of these implementations in an example GPU ray tracer.

Additional resources, including source and binary distributions of the example multi-hit GPU ray tracing application highlighted in this chapter, as well as the full text and source code for Ray Tracing Gems, are also available.

 

[ 2001-2006 ]   [ 2007-2012 ]   [ 2013-2018 ]   [ 2019-present ]

 

Copyright © 2000-2020 Christiaan Gribble
All rights reserved