Welcome! I'm a 5th year computer science PhD (and relapsing academic) at Stanford University, advised by Fredrik Kjolstad
I'm interested in high-performance computing, domain-specific languages, and GPUs. I'm grateful to be funded by an NSF Graduate Research Fellowship.
I graduated from Berkeley in 2015, where I double majored in Computer Science and Applied Mathematics.
Before Stanford I spent 4 years as a GPU and ML engineer:
first at Lawrence Livermore National Laboratory building parallel & scalable physics software for GPU-based supercomputers. My code was the computational heart of 15,000-GPU simulation of a human cell's lipid bilayer, and is still used by the National Cancer Instute.
After that I spent 2 years at Cruise as a senior engineer working on autonomous vehicles.
REPTILE: Performant Tiling of Recurrences
Muhammad Usman Tariq, Shiv Sundram, Fredrik Kjolstad
Proceedings of the ACM on Programming Languages, October 2025 (OOPSLA 2025)
[Paper]
Composing Distributed Computations Through Task and Kernel Fusion
Rohan Yadav, Shiv Sundram, Wonchan Lee, Michael Garland, Michael Bauer, Alex Aiken, Fredrik Kjolstad
International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2025. (ASPLOS 2025)
[Paper] Joint Work With Nvidia
Compiling Recurrences to Dense and Sparse Arrays
Shiv Sundram, Muhammmed Usman Tariq, Fredrik Kjolstad
Proceedings of the ACM on Programming Languages, October 2024 (OOPSLA 2024)
[Paper]
A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer
Francesco Di Natale, Harsh Bhatia, Timothy S Carpenter, Chris Neale, Sara Kokkila-Schumacher, Tomas Oppelstrup, Liam Stanton, Xiaohua Zhang, Shiv Sundram, Thomas RW Scogland, Gautham Dharuman, Michael P Surh, Yue Yang, Claudia Misale, Lars Schneidenbach, Carlos Costa, Changhoan Kim, Bruce D'Amora, Sandrasegaram Gnanakaran, Dwight V Nissley, Fred Streitz, Felice C Lightstone, Peer-Timo Bremer, James N Glosli, Helgi I Ingólfsson
ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), November 2019 (SC 19)
[Paper]
Best Paper Award, Supercomputing 2019
ddcMD: A Fully GPU-accelerated Molecular Dynamics Program for the Martini Force Field
Xiaohua Zhang, Shiv Sundram, Tomas Oppelstrup, Sara IL Kokkila-Schumacher, Timothy S Carpenter, Helgi I Ingólfsson, Frederick H Streitz, Felice C Lightstone, James N Glosli
The Journal of Chemical Physics, July 2020 [Paper]
Task Fusion in Distributed Runtimes
Shiv Sundram, Wonchan Lee, Alex Aiken
2022 IEEE/ACM 5th Annual Parallel Applications Workshop: Alternatives To MPI+ X (PAW-ATM 22)
[Paper]
Joint Work With Nvidia
Machine Learning–driven Multiscale Modeling Reveals Lipid-dependent Dynamics of RAS Signaling Proteins
Helgi I Ingólfsson, Chris Neale, Timothy S Carpenter, Rebika Shrestha, Cesar A López, Timothy H Tran, Tomas Oppelstrup, Harsh Bhatia, Liam G Stanton, Xiaohua Zhang, Shiv Sundram, Francesco Di Natale, Animesh Agarwal, Gautham Dharuman, Sara IL Kokkila Schumacher, Thomas Turbyville, Gulcin Gulten, Que N Van, Debanjan Goswami, Frantz Jean-Francois, Constance Agamasu, De Chen, Jeevapani J Hettige, Timothy Travers, Sumantra Sarkar, Michael P Surh, Yue Yang, Adam Moody, Shusen Liu, Brian C Van Essen, Arthur F Voter, Arvind Ramanathan, Nicolas W Hengartner, Dhirendra K Simanshu, Andrew G Stephen, Peer-Timo Bremer, S Gnanakaran, James N Glosli, Felice C Lightstone, Frank McCormick, Dwight V Nissley, Frederick H Streitz
Proceedings of the National Academy of Sciences, January 2022 [Paper]
Revet: A Language and Compiler for Dataflow Threads
Alexander Rucker, Shiv Sundram, Coleman Smith, Matthew Vilim, Raghu Prabhakar, Fredrik Kjolstad, Kunle Olukotun
High Performance Computer Architecture, March 2024 [Paper]
Machine Learning Algorithms for Automated NIF Capsule Mandrel Selection
K-J Boehm, Y Ayzman, R Blake, A Garcia, K Sequoia, S Sundram, W Sweet
Fusion Science and Technology, August 2020 [Paper]
Preparation and Optimization of a Diverse Workload for a Large-scale Heterogeneous System
Ian Karlin et al.
ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), November 2019 (SC 19)
[Paper]
Joint Work With Nvidia and IBM
Industry work
Cruise
Senior GPU Software Engineer
March 2019 - Aug 2020
Lawrence Livermore National Laboratory (LLNL)
Scientific GPU Software Developer & Researcher
Aug 2016 - Feb 2019
Microsoft
Software Engineer
Dec 2016 - Aug 2016
Qualcomm Research
Research Intern
Summer 2015
Other Presentations/work
Scaling molecular dynamics to 25,000 GPU’s on Sierra and Summit
Shiv Sundram, Tomas Oppelstrup
Nvidia GPU Technology Conference 2018, San Jose CA
[slides]
I did my quals report on computational genomics, and how to parallelize industry standard pipelines for aligning DNA, on both CPUs and GPUs:
Dependencies and Dataflow in Seed-Filter-Extend Pipelines [report]
Teaching/Mentorship
I have the pleasure of collaborating with three awesome undergraduate researchers
Usman Tariq
Usman works on accelerating linear algebra solvers with compilers. His code and techniques for direct solvers can outperform state-of-the-art libraries like ATLAS, OpenBLAS, and Intel MKL.
Bala Vinaithirthan
Bala works on compilers and new languages for DNA sequencing. His research focuses on accelerating the core computational algorithms in seed-filter-extend algorithms, the current standard for sequencing human DNA and analyzing it for mutations and genetic history. Joint project with Sneha Goenka (new Assistant Professor at Princeton).
Akhilesh Balasingam
Akhilesh works on simulators for evaluating and prototyping new dataflow-based computer architectures, and codesigning the programming models needed to make these proposed chips usable and versatile. Joint projects with professors Kunle Olukotun and Thierry Tambe
Personal Writing & Travel
When I'm not working on high-performance computing, I write narrative travelogues exploring food, solo-travel, and the people I meet along the way. You can read my ongoing series, Tales of the New Tamilakam Republic, [here].
Contact
For professional communications, you are welcome to use my current
academic address, shiv1@stanford.edu.
