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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Weightlifting
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 BasuPublished 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...
Scalable, Programmable and Dense: The HammerBlade Open-Source RISC-V Manycore
Dai C Jung, Max Ruttenberg, Paul Gao, Scott Davidson, Daniel Petrisko, Kangli Li, Aditya K Kamath et al.Published in ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA), 2024
In this paper, we explore HammerBlade, which simultaneously achieves scalability, programmability and density. HammerBlade is a fully open-source RISC-V manycore architecture, which has been silicon-validated with a 2048-core ASIC implementation using a 14/16nm process. Read more...
(MC)2: Lazy MemCopy at the Memory Controller
Aditya K Kamath, Simon PeterPublished in ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA), 2024
In this paper we propose (MC)2, a new lazy memory copy mechanism that provides performance improvement to applications that sparsely access the data that they copy. We show that (MC)2 provides benefits across a variety of microbenchmarks and workloads, including Google’s Protobuf, where (MC)2 provides a 43% speedup and Linux huge page copy-on-write faults, where (MC)2 provides 250x lower latency. Read more...
POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference
Distinguished Artifact AwardAditya K Kamath, Ramya Prabhu, Jayashree Mohan, Simon Peter, Ramachandran Ramjee, Ashish Panwar
Published in ACM 30th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2025
In this paper, we present POD-Attention — the first GPU kernel that efficiently computes attention for hybrid-batch LLM inference. POD-Attention maximizes the utilization of both compute and memory bandwidth by carefully allocating GPU resources such that prefill and decode operations happen concurrently on the same multiprocessor.POD-Attention speeds up attention computation by up to $59\%$ (mean $28\%$), enabling higher throughput and lower latency LLM inference. 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...