Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Md, Vasimuddin"'
Autor:
Md, Vasimuddin, Misra, Sanchit, Ma, Guixiang, Mohanty, Ramanarayan, Georganas, Evangelos, Heinecke, Alexander, Kalamkar, Dhiraj, Ahmed, Nesreen K., Avancha, Sasikanth
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is challenging due to large memory capacity and bandwidth requirements
Externí odkaz:
http://arxiv.org/abs/2104.06700
Autor:
Georganas, Evangelos, Kalamkar, Dhiraj, Avancha, Sasikanth, Adelman, Menachem, Aggarwal, Deepti, Anderson, Cristina, Breuer, Alexander, Bruestle, Jeremy, Chaudhary, Narendra, Kundu, Abhisek, Kutnick, Denise, Laub, Frank, Md, Vasimuddin, Misra, Sanchit, Mohanty, Ramanarayan, Pabst, Hans, Retford, Brian, Ziv, Barukh, Heinecke, Alexander
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and hardware ecosystems, the programming methodology of DL systems is stagnan
Externí odkaz:
http://arxiv.org/abs/2104.05755
The Deep Graph Library (DGL) was designed as a tool to enable structure learning from graphs, by supporting a core abstraction for graphs, including the popular Graph Neural Networks (GNN). DGL contains implementations of all core graph operations fo
Externí odkaz:
http://arxiv.org/abs/2007.06354
Autor:
Ho, Darryl, Ding, Jialin, Misra, Sanchit, Tatbul, Nesime, Nathan, Vikram, Md, Vasimuddin, Kraska, Tim
Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale requires overcoming performanc
Externí odkaz:
http://arxiv.org/abs/1910.04728
Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. Large sequencing centers typically employ hundreds of such systems. Such high-throughput and low-cost generation of data
Externí odkaz:
http://arxiv.org/abs/1907.12931
Autor:
Yufeng Gu, Arun Subramaniyan, Tim Dunn, Alireza Khadem, Kuan-Yu Chen, Somnath Paul, Md Vasimuddin, Sanchit Misra, David Blaauw, Satish Narayanasamy, Reetuparna Das
This artifact contains the code and instructions for paper: "GenDP: A Framework of Dynamic Programming Acceleration for Genome Sequencing Analysis" inInternational Symposium on Computer Architecture (ISCA), 2023.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b0f87c974dd3819b5b747e13596677e
https://zenodo.org/record/7792032
https://zenodo.org/record/7792032
Publikováno v:
Nature Computational Science. 2:78-83
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63387b866e2661fb970c872a109fdd4c
https://doi.org/10.1038/s43588-022-00201-8
https://doi.org/10.1038/s43588-022-00201-8
Autor:
Georganas, Evangelos, Kalamkar, Dhiraj, Avancha, Sasikanth, Adelman, Menachem, Aggarwal, Deepti, Anderson, Cristina, Breuer, Alexander, Bruestle, Jeremy, Chaudhary, Narendra, Kundu, Abhisek, Kutnick, Denise, Laub, Frank, Md, Vasimuddin, Misra, Sanchit, Mohanty, Ramanarayan, Pabst, Hans, Retford, Brian, Ziv, Barukh, Heinecke, Alexander
Publikováno v:
SC
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and hardware ecosystems, the programming methodology of DL systems is stagnan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f03112e161221e0bc2c2ef064fdad8f
https://doi.org/10.1145/3458817.3476206
https://doi.org/10.1145/3458817.3476206
Autor:
Md. Vasimuddin, Srinivas Aluru
Publikováno v:
HiPC
Learning the structure of Bayesian networks, even in the static case, is NP-hard, compelling much of the research to focus on heuristic-based approaches. However, there are instances where exact solutions are desirable especially for small network si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27e14997aadfaecb3c9b5c331caf171c
https://doi.org/10.1109/hipc.2017.00015
https://doi.org/10.1109/hipc.2017.00015
Autor:
Yong Dong, Maneesha Aluru, Srinivas Aluru, Kiran Pamnany, Min Xie, Sriram P. Chockalingam, Sanchit Misra, Md. Vasimuddin
Publikováno v:
SC
Learning Bayesian networks is NP-hard. Even with recent progress in heuristic and parallel algorithms, modeling capabilities still fall short of the scale of the problems encountered. In this paper, we present a massively parallel method for Bayesian
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d833449761d0eac45d8e2a7f1c859c64
https://doi.org/10.1109/sc.2014.43
https://doi.org/10.1109/sc.2014.43