Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Swany Martin"'
Autor:
Chandio, Bibrak Qamar, Srivastava, Prateek, Brodowicz, Maciej, Swany, Martin, Sterling, Thomas
The paper provides a unified co-design of 1) a programming and execution model that allows spawning tasks from within the vertex data at runtime, 2) language constructs for \textit{actions} that send work to where the data resides, combining parallel
Externí odkaz:
http://arxiv.org/abs/2402.06086
Autor:
Tyagi, Sahil, Swany, Martin
Publikováno v:
2023 IEEE International Conference on Big Data (BigData), 925-935
Gradient compression alleviates expensive communication in distributed deep learning by sending fewer values and its corresponding indices, typically via Allgather (AG). Training with high compression ratio (CR) achieves high accuracy like DenseSGD,
Externí odkaz:
http://arxiv.org/abs/2312.02493
Autor:
Tyagi, Sahil, Swany, Martin
Publikováno v:
Tyagi, S., & Swany, M. (2023). Accelerating Distributed ML Training via Selective Synchronization. 2023 IEEE International Conference on Cluster Computing (CLUSTER), 1-12
In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high comm
Externí odkaz:
http://arxiv.org/abs/2307.07950
Autor:
Tyagi, Sahil, Swany, Martin
Publikováno v:
Tyagi, S., & Swany, M. (2023). GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training. 2023 IEEE 16th International Conference on Cloud Computing (CLOUD), 319-329
Distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model. The periodic synchronization at each iteration incurs considerabl
Externí odkaz:
http://arxiv.org/abs/2305.12201
Autor:
Musser Jeremy, Kissel Ezra, Swany Martin, Breen Joe, Stidd Jason, McKee Shawn, Meekhof Benjeman
Publikováno v:
EPJ Web of Conferences, Vol 245, p 07055 (2020)
The Network Management Abstraction Layer (NMAL) extends perfSONAR capabilities to include automated network topology discovery and tracking in the Unified Network Information Service (UNIS), and incorporate Software Defined Networking (SDN) into over
Externí odkaz:
https://doaj.org/article/696851210f8c4773ab43c7ab33ebc42d
Autor:
Zhang, Boyuan, Tian, Jiannan, Di, Sheng, Yu, Xiaodong, Swany, Martin, Tao, Dingwen, Cappello, Franck
Today's graphics processing unit (GPU) applications produce vast volumes of data, which are challenging to store and transfer efficiently. Thus, data compression is becoming a critical technique to mitigate the storage burden and communication cost.
Externí odkaz:
http://arxiv.org/abs/2304.07342
Publikováno v:
ICASSP2023
We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by few quantum gates inserted into the variational quantum circuit of the victim QNN. QTrojan is much stealthie
Externí odkaz:
http://arxiv.org/abs/2302.08090
Publikováno v:
Robotics: Science and Systems, 2023
Autonomous mobility is emerging as a new disruptive mode of urban transportation for moving cargo and passengers. However, designing scalable autonomous fleet coordination schemes to accommodate fast-growing mobility systems is challenging primarily
Externí odkaz:
http://arxiv.org/abs/2302.07337
Autor:
Tyagi, Sahil, Swany, Martin
Publikováno v:
Tyagi, S., & Swany, M. (2022). ScaDLES: Scalable Deep Learning over Streaming data at the Edge. 2022 IEEE International Conference on Big Data (Big Data), 2113-2122
Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, as well as independent and identically distributed
Externí odkaz:
http://arxiv.org/abs/2301.08897
In this work, we propose IQGAN, a quantum Generative Adversarial Network (GAN) framework for multiqubit image synthesis that can be efficiently implemented on Noisy Intermediate Scale Quantum (NISQ) devices. We investigate the reasons for the inferio
Externí odkaz:
http://arxiv.org/abs/2210.16857