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...
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...
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...
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...
