I am a PhD student in the Electrical and Computer Engineering Department at Carnegie Mellon University. I am advised by Prof. Onur Mutlu and Prof. Phil Gibbons. My research interests lie in the general area of computer architecture and systems. My current research focus is on high-performance, energy efficient and easy-to-program architectures for throughput-oriented systems such as modern GPUs.
- Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds
Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B. Gibbons, Onur Mutlu
SoftMC: A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies
Hasan Hassan, Nandita Vijaykumar, Samira Khan, Saugata Ghose, Kevin Chang, Gennady Pekhimenko, Donghyuk Lee, Oguz Ergin, Onur Mutlu
Zorua: A Holistic Approach to Resource Virtualization in GPUs
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko, Samira Khan, Ashish Shrestha, Saugata Ghose, Adwait Jog, Phillip B. Gibbons, Onur Mutlu
Accelerating Pointer Chasing in 3D-Stacked Memory: Challenges, Mechanisms, Evaluation
Kevin Hsieh, Samira Khan, Nandita Vijaykumar, Kevin K. Chang, Amirali Boroumand, Saugata Ghose, Onur Mutlu
Transparent Offloading and Mapping (TOM): Enabling Programmer-Transparent Near-Data Processing in GPU Systems
Kevin Hsieh, Eiman Ebrahimi, Gwangsun Kim, Niladrish Chatterjee, Mike O'Connor, Nandita Vijaykumar, Onur Mutlu, Stephen Keckler
ChargeCache: Reducing DRAM Latency by Exploiting Row Access Locality
Hasan Hassan, Gennady Pekhimenko, Nandita Vijaykumar, Vivek Seshadri, Donghyuk Lee, Oguz Ergin, Onur Mutlu
A Case for Toggle-Aware Compression for GPU Systems
Gennady Pekhimenko, Evgeny Bolotin, Nandita Vijaykumar, Onur Mutlu, Todd C. Mowry, Stephen W. Keckler
A Framework for Accelerating Bottlenecks in GPU Execution with Assist Warps
Nandita Vijaykumar, Gennady Pekhimenko, Adwait Jog, Abhishek Bhowmick, Rachata Ausavarungnirun, Chita Das, Mahmut Kandemir, Todd C. Mowry, Onur Mutlu
Invited Book Chapter in Advances in GPU Research and Practice, Elsevier, 2016.
arXiv.org version, February 2016.
A Case for Core-Assisted Bottleneck Acceleration in GPUs: Enabling Flexible Data Compression with Assist Warps
Nandita Vijaykumar, Gennady Pekhimenko, Adwait Jog, Abhishek Bhowmick, Rachata Ausavarungnirun, Chita Das, Mahmut Kandemir, Todd C. Mowry, and Onur Mutlu