SUJIN PHILIP

LinkedIn: linkedin.com/in/sujinphilip
Webpage: https://philip-sujin.github.io/webpage

PROFILE

• MS. Computer Science with specialization in Graphics and Visualization
• Skilled in modern C++ programming
• 13+ years experience in the industry, mainly specialized in HPC, computer graphics and visualization

ACADEMIC QUALIFICATION

• MS. Computing – Graphics and Visualization, University of Utah, GPA 3.9, 2014
• BE. Computer, University of Pune, First Class, 2006

TECHNICAL SKILLS

Proficient: C++, C, Python, Cuda, MPI, Multithreading, CMake
Competent: OpenGL, TBB, Thrust, Shell Scripting

WORK EXPERIENCE

Amazon

Software Development Engineer
Sep 2024 - Present

• Research and development of DFM (Design For Manufacturing) checks for Amazon’s internal manufacturing operations. Primarily focused on SLS 3D printing
• Lead developer of software packages that implement algorithms for performing DFM checks and other processing on 3D parts models
  • The algorithms are implemented in C++ (standard version 2017) for high performance
  • Cloud development kit (CDK) is used to package the algorithms inside docker containers and deploy them as Lambda functions, and for CI/CD

Kitware Inc.

Staff R&D Engineer
Nov 2014 – Aug 2024

Member of the Scientific Computing Team. Contributed to leading open-source visualization software: VTK, VTK-m, and ParaView.

• VTK-m (DOE Exascale Computing Project)
  • Developed VTK-m filters such as Clip and ExternalFaces.
  • Implemented VTK file readers in VTK-m.
  • Implemented virtual function simulation in CUDA for abstract array types.
  • Enabled VTK-m object serialization to support the DIY parallelization library (MPI-based).
  • Developed the Kokkos-based device backend for VTK-m, deployed on Frontier (AMD GPUs) and Aurora (Intel GPUs).
  • Improved CUDA (Thrust-based) and OpenMP backends by optimizing performance and fixing core issues.
• VTK-m Integration with VTK and ParaView
  • Led the integration of VTK-m with VTK and ParaView for accelerated data processing.
  • Implemented VTK-m powered filters within VTK and optimized data transfers between CPU and GPU.
  • Developed factory overrides for seamless switching to VTK-m filters when available.
  • Optimized field range computation directly on the GPU, minimizing CPU-GPU data transfers.
• IARPA Haystac Project
  • Developed a parallel city traffic simulator using SUMO, MPI, and Python.
  • Created an Airflow DAG-based workflow for simulation deployment on AWS (EC2, Kubernetes, S3, Docker).
• Other Notable Contributions
  • Developed parallel filters for resampling multi-partition datasets into structured datasets for faster volume rendering in ParaView.
  • Built a proof-of-concept for CUDA-OpenGL interoperability, enabling direct GPU-GPU data transfers.
  • Contributed to CMake and commercial projects.

Persistent Systems Limited

Software Developer
Sep 2006 – Jul 2009

Member of the project group that handled PSL’s client, Netezza Corporation.

• Development and maintenance of software for the Snippet Processing Unit (SPU)―the processing nodes of Netezza’s massively parallel database appliance systems (NPS)
• Key projects:
  • Developed a tool to track heap memory allocations on the snippet processing units to help analysis of memory leaks
  • Investigated techniques to enable automatic leak detection on the SPUs. Implemented prototypes based on Mark and Sweep algorithm and methods based on tagging allocations with their intended scope and lifetime

RESEARCH ASSISTANTSHIP AND INTERNSHIPS

Scientific Computing and Imaging Institute

Research Assistant
Jan 2010 – Aug 2014

Worked on heterogeneous parallel computing for scientific computing and visualization applications.

• Developed a scalable, parallel, out-of-core solver for gradient domain processing of large, gigapixel sized images using multithreading, GPGPU and MPI
• Developed a scalable, parallel, out-of-core technique for computing seams for gigapixel sized panoramas using multithreading and MPI

Lawrence Livermore National Laboratory

Graduate Intern
Summer 2012

Collaborated on the project to develop a scalable, parallel, out-of-core technique to compute seams for gigapixel sized panoramas

Los Alamos National Laboratory

Graduate Intern
Summer 2011

Developed a parallel ray casting based volume renderer using multithreading, GPUs and MPI.

Lawrence Livermore National Laboratory

Graduate Intern
Summer 2010

Collaborated on the project to develop a scalable, parallel, out-of-core solver for gradient domain processing of massive images

PUBLICATIONS

• Parallel Gradient Domain Processing of Massive Images. S. Philip, B. Summa, P.-T. Bremer, and V. Pascucci, in Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization 2011, pp. 11-19
• Hybrid CPU-GPU Solver for Gradient Domain Processing of Massive Images. S. Philip, B. Summa, P.-T Bremer and V. Pascucci, in Proceedings of the International Conference on Parallel and Distributed Systems 2011, pp. 244-251
• Scalable Seams for Gigapixel Panoramas. S. Philip, B. Summa, J. Tierny, P.-T. Bremer, and V. Pascucci, in Proceedings of Eurographics Symposium on Parallel Graphics and Visualization 2013, pp. 25-32 (Selected as Best Paper)
• Distributed Seams for Gigapixel Panoramas. S. Philip, B. Summa, J. Tierny, P.-T. Bremer, and V. Pascucci, in IEEE Transactions on Visualization and Computer Graphics, Volume 21, Issue 3, March 2015, pp 350-362
• Visualization at Exascale: Making It All Work with VTK-m. K. Moreland, et. al. in International Journal of High Performance Computing Applications, Volume 38, Issue 5, September 2024, pp 508-526
• Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation. S. Son, L. Zheng, B. Clipp, C. Greenwell, S. Philip, M C Lin. 2025
• The ViSUS Visualization Framework. V. Pascucci, G. Scorzelli, B. Summa, P.-T. Bremer, A. Gyulassy, C. Christensen, S. Philip and S. Kumar. in High Performance Visualization: Enabling Extreme-Scale Scientific Insight, Chapman and Hall/CRC Computational Science, Ch. 19, Edited by E. Wes Bethel and Hank Childs (LBNL) and Charles Hansen (UofU), Chapman and Hall/CRC, 2012.
• In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned M. Kim, J. Kress, J. Choi, N. Podhorszki, S. Klasky, M. Wolf, K. Mehta, K. Huck, B. Geveci, S. Phillip, R. Maynard, H. Guo, T. Peterka, K. Moreland, C. Chang, J. Dominski, M. Churchill, and D. Pugmire, in High Performance Computing. Springer International Publishing, 2018, pp. 230-242.