Zobrazeno 1 - 10
of 433
pro vyhledávání: '"Nelson, Jacob"'
Autor:
Lim, Katie, Giordano, Matthew, Stavrinos, Theano, Zhang, Irene, Nelson, Jacob, Kasikci, Baris, Anderson, Tom
Direct-attached accelerators, where application accelerators are directly connected to the datacenter network via a hardware network stack, offer substantial benefits in terms of reduced latency, CPU overhead, and energy use. However, a key challenge
Externí odkaz:
http://arxiv.org/abs/2403.14770
Autor:
Nelson, Jacob S., Baczewski, Andrew D.
We compare several quantum phase estimation (QPE) protocols intended for early fault-tolerant quantum computers (EFTQCs) in the context of models of their implementations on a surface code architecture. We estimate the logical and physical resources
Externí odkaz:
http://arxiv.org/abs/2403.00077
Autor:
Patel, Pratyush, Lim, Katie, Jhunjhunwalla, Kushal, Martinez, Ashlie, Demoulin, Max, Nelson, Jacob, Zhang, Irene, Anderson, Thomas
Field-Programmable Gate Arrays (FPGAs) are more energy efficient and cost effective than CPUs for a wide variety of datacenter applications. Yet, for latency-sensitive and bursty workloads, this advantage can be difficult to harness due to high FPGA
Externí odkaz:
http://arxiv.org/abs/2304.04488
Autor:
Yuan, Yifan, Huang, Jinghan, Sun, Yan, Wang, Tianchen, Nelson, Jacob, Ports, Dan R. K., Wang, Yipeng, Wang, Ren, Tai, Charlie, Kim, Nam Sung
Responding to the "datacenter tax" and "killer microseconds" problems for datacenter applications, diverse solutions including Smart NIC-based ones have been proposed. Nonetheless, they often suffer from high overhead of communications over network a
Externí odkaz:
http://arxiv.org/abs/2203.08906
Publikováno v:
In Agricultural and Forest Meteorology 15 November 2024 358
Autor:
Yuan, Yifan, Alama, Omar, Sapio, Amedeo, Fei, Jiawei, Nelson, Jacob, Ports, Dan R. K., Canini, Marco, Kim, Nam Sung
The advent of switches with programmable dataplanes has enabled the rapid development of new network functionality, as well as providing a platform for acceleration of a broad range of application-level functionality. However, existing switch hardwar
Externí odkaz:
http://arxiv.org/abs/2112.06095
Autor:
Shah, Aashaka, Chidambaram, Vijay, Cowan, Meghan, Maleki, Saeed, Musuvathi, Madan, Mytkowicz, Todd, Nelson, Jacob, Saarikivi, Olli, Singh, Rachee
Machine learning models are increasingly being trained across multiple GPUs and servers. In this setting, data is transferred between GPUs using communication collectives such as AlltoAll and AllReduce, which can become a significant bottleneck in tr
Externí odkaz:
http://arxiv.org/abs/2111.04867
Publikováno v:
In Journal of Environmental Management April 2024 356
ML workloads are becoming increasingly popular in the cloud. Good cloud training performance is contingent on efficient parameter exchange among VMs. We find that Collectives, the widely used distributed communication algorithms, cannot perform optim
Externí odkaz:
http://arxiv.org/abs/2105.14088