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Weightlifting

5 minute read

Published:

I’m pretty passionate about weightlifting, and try to make it a part of my daily lifestyle. Over the years there’s a lot I’ve learned, and I hope this blog post might help others starting out. Read more

publications

ScoRD: A Scoped Race Detector for GPUs

Aditya K Kamath*, Alvin A George*, and Arkaprava Basu
(*Authors contributed equally to this work)

Published in ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), 2020

This paper proposed a hardware race detector for GPUs. Our hardware was able to efficiently support detection of scoped races in GPU programs. Read more

iGUARD: In-GPU Advanced Race Detection

Aditya K Kamath and Arkaprava Basu

Published in ACM SIGOPS 28th Symposium on Operating Systems Principles (SOSP), 2021

This paper proposed an in-GPU software race detector. The race detector made use of NVBit, a binary instrumentation tool. Using this, we were able to detect races due to improper synchronization, scopes, or ITS. We even found races in 3 NVIDIA-supported libaries (cuML, CUB, Cooperative Groups). Read more

GPM: Leveraging Persistent Memory from a GPU

Shweta Pandey*, Aditya K Kamath*, and Arkaprava Basu
(*Authors contributed equally to this work)

Published in ACM 27th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2022

This paper explored how to utilise commercially available NVM on a GPU using real hardware. Through this process we came up with a benchmark suite (GPMBench) consisting of GPU applications that benefit from both GPU parallelism as well as NVM persistence. We also provide a GPU-optimised library (libGPM) that simplifies access to NVM from a GPU. Read more

Scoped Buffered Persistency Model for GPUs

Shweta Pandey*, Aditya K Kamath*, and Arkaprava Basu
(*Authors contributed equally to this work)

Published in ACM 28th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2023

This paper explores persistency models for GPUs, analyzing whether CPU persistency models are suitable for GPU architecture and the needs of GPU applications (spoiler: they aren’t). We investigate how to express persistency models for intra-thread and inter-thread persist memory order (PMO) for GPU programs. We then look at how to design the hardware architecture necessary to implement these operations efficiently. Read more

talks

GPM: Leveraging Persistent Memory from a GPU

Aditya K Kamath*, Shweta Pandey*, and Arkaprava Basu
(*Authors contributed equally to this work)

Presented at 13th Annual Non-volatile Memories Workshop

This is a workshop presentation of ‘GPM’ published in ASPLOS ‘23. This paper explored how to utilise commercially available NVM on a GPU using real hardware. Through this process we came up with a benchmark suite (GPMBench) consisting of GPU applications that benefit from both GPU parallelism as well as NVM persistence. We also provide a GPU-optimised library (libGPM) that simplifies access to NVM from a GPU. Read more

Herding LLaMaS: Using LLMs as an OS Module

Aditya K Kamath* and Sujay Yadalam*
(*Authors contributed equally to this work)

Presented at ASPLOS 2023, Wild and Crazy Ideas Session

Sujay and I found ourselves chatting about the future of research one day. It was around this time that an explosion of interest in Large Language Models (LLMs) had started, sparked by the release of ChatGPT by OpenAI. Our discussion inevitably veered towards this topic, and we became curious about how LLMs could revolutionize the field of operating systems. Read more