Zobrazeno 1 - 10
of 649
pro vyhledávání: '"Kandemir, Mahmut"'
Autor:
Jain, Rishabh, Bhasi, Vivek M., Jog, Adwait, Sivasubramaniam, Anand, Kandemir, Mahmut T., Das, Chita R.
Personalized recommendation is a ubiquitous application on the internet, with many industries and hyperscalers extensively leveraging Deep Learning Recommendation Models (DLRMs) for their personalization needs (like ad serving or movie suggestions).
Externí odkaz:
http://arxiv.org/abs/2410.22249
Autor:
Huo, Pingyi, Devulapally, Anusha, Maruf, Hasan Al, Park, Minseo, Nair, Krishnakumar, Arunachalam, Meena, Akbulut, Gulsum Gudukbay, Kandemir, Mahmut Taylan, Narayanan, Vijaykrishnan
Deep Learning Recommendation Models (DLRMs) have become increasingly popular and prevalent in today's datacenters, consuming most of the AI inference cycles. The performance of DLRMs is heavily influenced by available bandwidth due to their large vec
Externí odkaz:
http://arxiv.org/abs/2409.16633
Autor:
Sharma, Aakash, Bhasi, Vivek M., Singh, Sonali, Kesidis, George, Kandemir, Mahmut T., Das, Chita R.
We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the anticipated communication-network delays. Our scheduler consists of th
Externí odkaz:
http://arxiv.org/abs/2401.16492
The growing prevalence of near-term intermediate-scale quantum (NISQ) systems has brought forth a heightened focus on the issue of circuit reliability. Several quantum computing activities, such as circuit design and multi-qubit mapping, are focused
Externí odkaz:
http://arxiv.org/abs/2305.11919
Existing quantum systems provide very limited physical qubit counts, trying to execute a quantum algorithm/circuit on them that have a higher number of logical qubits than physically available lead to a compile-time error. Given that it is unrealisti
Externí odkaz:
http://arxiv.org/abs/2301.00720
Autor:
Sharma, Aakash, Bhasi, Vivek M., Singh, Sonali, Jain, Rishabh, Gunasekaran, Jashwant Raj, Mitra, Subrata, Kandemir, Mahmut Taylan, Kesidis, George, Das, Chita R.
We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud. We have implemented the profiler by exte
Externí odkaz:
http://arxiv.org/abs/2208.14344
There is an increasing demand for intelligent processing on emerging ultra-low-power internet of things (IoT) devices, and recent works have shown substantial efficiency boosts by executing inference tasks directly on the IoT device (node) rather tha
Externí odkaz:
http://arxiv.org/abs/2204.13106
Autor:
Ramezani, Morteza, Cong, Weilin, Mahdavi, Mehrdad, Kandemir, Mahmut T., Sivasubramaniam, Anand
Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependency between nodes in a graph, and the privacy concern due to the centra
Externí odkaz:
http://arxiv.org/abs/2111.08202
Autor:
Sarma, Anup, Singh, Sonali, Jiang, Huaipan, Pattnaik, Ashutosh, Mishra, Asit K, Narayanan, Vijaykrishnan, Kandemir, Mahmut T, Das, Chita R
Machine/deep-learning (ML/DL) based techniques are emerging as a driving force behind many cutting-edge technologies, achieving high accuracy on computer vision workloads such as image classification and object detection. However, training these mode
Externí odkaz:
http://arxiv.org/abs/2109.07710
Autor:
Zhang, Jie, Kwon, Miryeong, Gouk, Donghyun, Koh, Sungjoon, Kim, Nam Sung, Kandemir, Mahmut Taylan, Jung, Myoungsoo
Large persistent memories such as NVDIMM have been perceived as a disruptive memory technology, because they can maintain the state of a system even after a power failure and allow the system to recover quickly. However, overheads incurred by a heavy
Externí odkaz:
http://arxiv.org/abs/2106.14241