Lessons learned from a performance analysis and optimization of a multiscale cellular simulation

This work presents a comprehensive performance analysis and optimization of a multiscale agent-based cellular simulation. The optimizations applied are guided by detailed performance analysis and include memory management, load balance, and a locality-aware parallelization. The outcome of this paper is not only the speedup of 2.4x achieved by the optimized version with respect to the original PhysiCell code, but also the lessons learned and best practices when developing parallel HPC codes to obtain efficient and highly performant applications, especially in the computational biology field

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Acceleration of Axisymetric Ultrasound Simulations

Simulácia šírenia ultrazvuku prostredníctvom mäkkých biologických tkanív má širokú škálu praktických aplikácií. Patria sem dizajn prevodníkov pre diagnostický a terapeutický ultrazvuk, vývoj nových metód spracovania signálov a zobrazovacích techník, štúdium anomálií ultrazvukových lúčov v heterogénnych médiách, ultrazvuková klasifikácia tkanív, učenie rádiológov používať ultrazvukové zariadenia a interpretáciu ultrazvukových obrazov, modelové vrstvenie medicínskeho obrazu a plánovanie liečby pre ultrazvuk s vysokou intenzitou. Ultrazvuková simulácia však predstavuje výpočtovo zložitý problém, pretože simulačné domény sú veľmi veľké v porovnaní s akustickými vlnovými dĺžkami, ktoré sú predmetom záujmu. Ale ak je problém osovo symetrický, problém môže byť riešený v 2D.To umožňuje spúšťanie simulácií na mriežke s väčším počtom bodov, s menším využitím výpoč- tových zdrojov za kratšiu dobu. Táto práca modeluje a implementuje zrýchlenie vlnovej nelineárnej ultrazvukovej simulácie v axisymetrickom súradnicovom systéme realizovanom v Matlabe pomocou Mex súborov pre diskrétne sínové a kosínové transformácie. Axisymetrická simulácia bola implementovaná v C++ ako open source rozšírenie K-WAVE toolboxu. Kód je optimalizovaný na beh na jednom uzle superpočítaču Salomon (IT4Innovations, Ostrava, Česká republika) s dvoma dvanásť-jadrovými procesormi Intel Xeon E5-2680v3. Na maximalizáciu výpočtovej efektívnosti boli vykonané viaceré optimalizácie kódu. Po prvé, fourierové tramsformácie boli vypočítané pomocou real-to-complex FFT z knižnice FFTW. V porovnaní s complex-to-complex FFT to znížilo čas výpočtu a pamäť spojenú s výpočtom FFT o takmer 50%. Taktiež diskrétne sínové a kosínové transformácie sa počítali pomocou knižnice FFTW, ktoré v Matlab verzii museli byť vyvolané z dynamicky načítaných MEX súborov. Po druhé, aby sa znížilo zaťaženie priepustnosti pamäte, boli všetky operácie počítané jednoduchej presnosti pohyblivej rádovej čiarky. Po tretie, elementárne operá- cie boli paralelizované pomocou OpenMP a potom vektorizované pomocou rozšírení SIMD (SSE). Celkový výpočet C++ verzie je až do 34-násobne rýchlejší a využíva menej ako tretinu pamäte ako Matlab verzia simulácie. Simulácia ktorá by trvala takmer dva dni tak môže byť vypočítaná za jeden a pol hodinu. Toto všetko umožňuje počítať simuláciu na výpočetnej mriežke s veľkosťou 16384 × 8192 bodov v primeranom čase.The simulation of ultrasound propagation through soft biological tissue has a wide range of practical applications. These include the design of transducers for diagnostic and therapeutic ultrasound, the development of new signal processing and imaging techniques, studying the aberration of ultrasound beams in heterogeneous media, ultrasonic tissue classification, training ultrasonographers to use ultrasound equipment and interpret ultrasound images, model-based medical image registration, and treatment planning and dosimetry for high-intensity focused ultrasound. However, ultrasound simulation presents a computationally difficult problem, as simulation domains are very large compared with the acoustic wavelengths of interest. But if the problem is axisymmetric, the governing equations can also be solved in 2D. This allows running simulations with larger grid size, with less computational resources and in a shorter time. This paper model and implements an acceleration of the Full-wave Nonlinear Ultrasound Simulation in an Axisymmetric Coordinate System implemented in Matlab using Mex Files for FFTW DST and DCT transformations. The axisymmetric simulation was implemented in C++ as an extension to the open source K-WAVE toolbox. The codes were optimized to run using one node of Salomon supercomputer cluster (IT4Innovations, Ostrava, Czechia) with two twelve-core Intel Xeon E5-2680v3 processors. To maximize computational efficiency, several stages of code optimization were performed. First, the FFTs were computed using the real-to-complex FFT from the FFTW library. Compared to the complex-to-complex FFT, this reduced the compute time and memory associated with the FFT by nearly 50%. Also, real-to-real DCTs and DSTs were computed using FFTW library, which ones in Matlab version, had to be invoked from dynamically loaded MEX Files. Second, to save memory bandwidth, all operations were computed in single precision. Third, element-wise operations were parallelized using OpenMP and then optimized using streaming SIMD extensions (SSE). The overall computation of the C++ k-space model is up to 34-times faster and uses less than one-third of the memory than Matlab version. The simulation which would take nearly two days by Matlab implementation can be now computed in one and half hour. This all allows running the simulation on the computational grid with 16384 × 8192 grid points within a reasonable time.

Working With Incremental Spatial Data During Parallel (GPU) Computation

Central to many complex systems, spatial actors require an awareness of their local environment to enable behaviours such as communication and navigation. Complex system simulations represent this behaviour with Fixed Radius Near Neighbours (FRNN) search. This algorithm allows actors to store data at spatial locations and then query the data structure to find all data stored within a fixed radius of the search origin. The work within this thesis answers the question: What techniques can be used for improving the performance of FRNN searches during complex system simulations on Graphics Processing Units (GPUs)? It is generally agreed that Uniform Spatial Partitioning (USP) is the most suitable data structure for providing FRNN search on GPUs. However, due to the architectural complexities of GPUs, the performance is constrained such that FRNN search remains one of the most expensive common stages between complex systems models. Existing innovations to USP highlight a need to take advantage of recent GPU advances, reducing the levels of divergence and limiting redundant memory accesses as viable routes to improve the performance of FRNN search. This thesis addresses these with three separate optimisations that can be used simultaneously. Experiments have assessed the impact of optimisations to the general case of FRNN search found within complex system simulations and demonstrated their impact in practice when applied to full complex system models. Results presented show the performance of the construction and query stages of FRNN search can be improved by over 2x and 1.3x respectively. These improvements allow complex system simulations to be executed faster, enabling increases in scale and model complexity

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia

Proceedings, MSVSCC 2017

Proceedings of the 11th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 20, 2017 at VMASC in Suffolk, Virginia. 211 pp

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects