Hi, I am Kaustubh, a Ph.D. candidate studying computer engineering in NUCAR lab at Northeastern University with my advisor David Kaeli. My research focuses on designing hardware accelerators for sparse graph workloads.
My expertise lies in:
- Computer Architecture Simulator Design
- Graph Neural Network Accelerators
- Sparse Matrix Accelerators
- Homomorphic Encryption Accelerators
- GPU Kernel Design
Contact: shivdikar.k [at] northeastern [dot] edu, mail [at] kaustubh [dot] us
- PhD - Computer Engineering, Northeastern University [Expected Fall 2023]
- MS - Electrical and Computer Engineering, Northeastern University [May 2021]
- BS - Electrical Engineering, Veermata Jijabai Technological Institute [May 2016]
- Summer-Fall 2020 Coop: Parallel Computing Lab @ Intel Labs with Fabrizio Petrini.
- Summer-Fall 2019 Coop: Parallel Computing Lab @ Intel Labs with Fabrizio Petrini.
- Summer-Fall 2018 Coop: Mobile Robotics @ Omron Adept with George Paul.
- June 2022: Mentored Lina Adkins for the GNN Acceleration project at REU-Pathways program
- May 2022: Served as Submission chair for HPCA 2023 conference.
- Jan 2020: Taught the GPU Programming Course at NEU
- April 2019: Graduate Innovator Award at the RISE 2019 Research Expo for our poster Pi-Tiles
- April 2018: Best Poster Award at the RISE 2018 Research Expo for our poster The Prime Hexagon
- Nov 2018: Mentored the NEU team for Student Cluster Contest at Super Computing Conference 2018
- Nov 2017: Joined the NEU Team for Student Cluster Contest at Super Computing Conference 2017
Accelerating Polynomial Multiplication for Homomorphic Encryption on GPUs
|Homomorphic Encryption (HE) enables users to securely outsource both the storage and computation of sensitive data to untrusted servers. Not only does FHE offer an attractive solution for security in cloud systems, but lattice-based FHE systems are also believed to be resistant to attacks by quantum computers. However, current FHE implementations suffer from prohibitively high latency. For lattice-based FHE to become viable for real-world systems, it is necessary for the key bottlenecks---particularly polynomial multiplication---to be highly efficient.
In this paper, we present a characterization of GPU-based implementations of polynomial multiplication. We begin with a survey of modular reduction techniques and analyze several variants of the widely-used Barrett modular reduction algorithm. We then propose a modular reduction variant optimized for 64-bit integer words on the GPU, obtaining a 1.8x speedup over the existing comparable implementations.
FHE protects against network insecurities in untrusted cloud services, enabling users to securely offload sensitive data
|Authors: Kaustubh Shivdikar, Gilbert Jonatan, Evelio Mora, Neal Livesay, Rashmi Agrawal, Ajay Joshi, José L. Abellán, John Kim, David Kaeli|
JAXED: Reverse Engineering DNN Architectures Leveraging JIT GEMM Libraries
|General matrix multiplication (GEMM) libraries on x86 architectures have recently adopted Just-in-time (JIT) based optimizations to dramatically reduce the execution time of small and medium-sized matrix multiplication. The exploitation of the latest CPU architectural extensions, such as the AVX2 and AVX-512 extensions, are the target for these optimizations. Although JIT compilers can provide impressive speedups to GEMM libraries, they expose a new attack surface through the built-in JIT code caches. These software-based caches allow an adversary to extract sensitive information through carefully designed timing attacks. The attack surface of such libraries has become more prominent due to their widespread integration into popular Machine Learning (ML) frameworks such as PyTorch and Tensorflow.
Attack Surface: After the victim’s execution, the victim leaves behind information about its model hyperparameters in the JIT code cache. The attacker probes this JIT code cache through the attacker’s ML model and observes timing information to determine the victim’s model hyperparameters.
|Authors: Malith Jayaweera, Kaustubh Shivdikar, Yanzhi Wang, David Kaeli|
GNNMark: A benchmark suite to characterize graph neural network training on GPUs
|Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning algorithms to train on non-euclidean data. GNNs are widely used in recommender systems, drug discovery, text understanding, and traffic forecasting. Due to the energy efficiency and high-performance capabilities of GPUs, GPUs are a natural choice for accelerating the training of GNNs. Thus, we want to better understand the architectural and system level implications of training GNNs on GPUs. Presently, there is no benchmark suite available designed to study GNN training workloads.
Graph Neural Network Analysis
|Authors: Trinayan Baruah, Kaustubh Shivdikar, Shi Dong, Yifan Sun, Saiful A Mojumder, Kihoon Jung, José L. Abellán, Yash Ukidave, Ajay Joshi, John Kim, David Kaeli|
SMASH: Sparse Matrix Atomic Scratchpad Hashing
Student cluster competition 2018, team northeastern university: Reproducing performance of a multi-physics simulations of the Tsunamigenic 2004 Sumatra Megathrust earthquake on the AMD EPYC 7551 architecture
Speeding up DNNs using HPL based Fine-grained Tiling for Distributed Multi-GPU Training
Video steganography using encrypted payload for satellite communication
Missing 'Middle Scenarios' Uncovering Nuanced Conditions in Latin America's Housing Crisis
Dynamic power allocation using Stackelberg game in a wireless sensor network
Automatic image annotation using a hybrid engine
What is KTB Wiki?
KTB Wiki, because the best way to store your knowledge is in an indexed SQL database.
This website was built on KTB Wiki. KTB wiki is my side project/attempt to consolidate knowledge gained during my Ph.D. journey. Though many other platforms provide similar service, the process of creating KTB Wiki was a learning experience since it taught me concepts of indexing, load balancing, and in-memory file systems. KTB Wiki was built using MediaWiki and is intended for research purposes only.