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Aditya Dhakal
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2020 – today
- 2024
- [j3]Pedro Bruel, Sai Rahul Chalamalasetti, Aditya Dhakal, Eitan Frachtenberg, Ninad Hogade, Rolando Pablo Hong Enriquez, Alok Mishra, Dejan S. Milojicic, Pavana Prakash, Gourav Rattihalli:
Predicting Heterogeneity and Serverless Principles of Converged High-Performance Computing, Artificial Intelligence, and Workflows. Computer 57(1): 136-144 (2024) - [j2]Rolando P. Hong Enriquez, Rosa M. Badia, Barbara M. Chapman, Kirk Bresniker, Scott Pakin, Alok Mishra, Pedro Bruel, Aditya Dhakal, Gourav Rattihalli, Ninad Hogade, Eitan Frachtenberg, Dejan S. Milojicic:
Quantum optimization algorithms: Energetic implications. Concurr. Comput. Pract. Exp. 36(16) (2024) - [c12]Yunming Xiao, Diman Zad Tootaghaj, Aditya Dhakal, Lianjie Cao, Puneet Sharma, Aleksandar Kuzmanovic:
Conspirator: SmartNIC-Aided Control Plane for Distributed ML Workloads. USENIX ATC 2024: 767-784 - 2023
- [j1]Jiasi Chen, K. K. Ramakrishnan, Aditya Dhakal, Xukan Ran:
Networked Architectures for Localization-Based Multi-User Augmented Reality. IEEE Commun. Mag. 61(12): 104-110 (2023) - [c11]Gourav Rattihalli, Ninad Hogade, Aditya Dhakal, Eitan Frachtenberg, Rolando P. Hong Enriquez, Pedro Bruel, Alok Mishra, Dejan S. Milojicic:
Fine-Grained Heterogeneous Execution Framework with Energy Aware Scheduling. CLOUD 2023: 35-44 - [c10]Tobias Pfandzelter, Aditya Dhakal, Eitan Frachtenberg, Sai Rahul Chalamalasetti, Darel Emmot, Ninad Hogade, Rolando Pablo Hong Enriquez, Gourav Rattihalli, David Bermbach, Dejan S. Milojicic:
Kernel-as-a-Service: A Serverless Programming Model for Heterogeneous Hardware Accelerators. Middleware 2023: 192-206 - [c9]Aditya Dhakal, Philipp Raith, Logan T. Ward, Rolando P. Hong Enriquez, Gourav Rattihalli, Kyle Chard, Ian T. Foster, Dejan S. Milojicic:
Fine-grained accelerator partitioning for Machine Learning and Scientific Computing in Function as a Service Platform. SC Workshops 2023: 1606-1613 - [i2]Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan:
D-STACK: High Throughput DNN Inference by Effective Multiplexing and Spatio-Temporal Scheduling of GPUs. CoRR abs/2304.13541 (2023) - 2022
- [c8]Aditya Dhakal, Xukan Ran, Yunshu Wang, Jiasi Chen, K. K. Ramakrishnan:
SLAM-share: visual simultaneous localization and mapping for real-time multi-user augmented reality. CoNEXT 2022: 293-306 - [c7]Aditya Dhakal, K. K. Ramakrishnan, Sameer G. Kulkarni, Puneet Sharma, Junguk Cho:
Slice-Tune: a system for high performance DNN autotuning. Middleware 2022: 228-240 - 2021
- [c6]Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan:
Primitives Enhancing GPU Runtime Support for Improved DNN Performance. CLOUD 2021: 53-64 - 2020
- [c5]Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan:
GSLICE: controlled spatial sharing of GPUs for a scalable inference platform. SoCC 2020: 492-506 - [c4]Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan:
ECML: Improving Efficiency of Machine Learning in Edge Clouds. CloudNet 2020: 1-6 - [c3]Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan:
Machine Learning at the Edge: Efficient Utilization of Limited CPU/GPU Resources by Multiplexing. ICNP 2020: 1-6 - [i1]Aditya Dhakal, Junguk Cho, Sameer G. Kulkarni, K. K. Ramakrishnan, Puneet Sharma:
Spatial Sharing of GPU for Autotuning DNN models. CoRR abs/2008.03602 (2020)
2010 – 2019
- 2019
- [c2]Aditya Dhakal, K. K. Ramakrishnan:
NetML: An NFV Platform with Efficient Support for Machine Learning Applications. NetSoft 2019: 396-404 - 2017
- [c1]Aditya Dhakal, K. K. Ramakrishnan:
Machine learning at the network edge for automated home intrusion monitoring. ICNP 2017: 1-6
Coauthor Index
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last updated on 2024-08-05 21:13 CEST by the dblp team
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