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pro vyhledávání: '"cpu"'
Online LLM inference powers many exciting applications such as intelligent chatbots and autonomous agents. Modern LLM inference engines widely rely on request batching to improve inference throughput, aiming to make it cost-efficient when running on
Externí odkaz:
http://arxiv.org/abs/2411.01142
Autor:
Liu, Xiaoman
Forecasting CPU performance, which involves estimating performance scores based on hardware characteristics during operation, is crucial for computational system design and resource management. This research field currently faces two primary challeng
Externí odkaz:
http://arxiv.org/abs/2410.19297
The confidentiality of cryptographic keys is essential for the security of protection schemes used for communication, file encryption, and outsourced computation. Beyond cryptanalytic attacks, adversaries can steal keys from memory via software explo
Externí odkaz:
http://arxiv.org/abs/2410.01777
Autor:
Ichimura, Tsuyoshi, Fujita, Kohei, Hori, Muneo, Maddegedara, Lalith, Wells, Jack, Gray, Alan, Karlin, Ian, Linford, John
We propose a CPU-GPU heterogeneous computing method for solving time-evolution partial differential equation problems many times with guaranteed accuracy, in short time-to-solution and low energy-to-solution. On a single-GH200 node, the proposed meth
Externí odkaz:
http://arxiv.org/abs/2409.20380
Confidential Virtual Machines (CVMs) are a type of VMbased Trusted Execution Environments (TEEs) designed to enhance the security of cloud-based VMs, safeguarding them even from malicious hypervisors. Although CVMs have been widely adopted by major c
Externí odkaz:
http://arxiv.org/abs/2409.15542
Publikováno v:
35th Annual Workshop - Psychology of Programming Interest Group (PPIG), September 2024
This paper discusses further evaluations of the educational effectiveness of an existing CPU visual simulator (CPUVSIM). The CPUVSIM, as an Open Educational Resource, has been iteratively improved over a number of years following an Open Pedagogy app
Externí odkaz:
http://arxiv.org/abs/2411.05229
Autor:
Tian, Bing, Liu, Haikun, Tang, Yuhang, Xiao, Shihai, Duan, Zhuohui, Liao, Xiaofei, Zhang, Xuecang, Zhu, Junhua, Zhang, Yu
Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern
Externí odkaz:
http://arxiv.org/abs/2409.16576
Autor:
Hussein, Salam Ayad1 (AUTHOR) salamayad.77@uomustansiriyah.edu.iq, Kareem, Emad Issa Abdul1 (AUTHOR)
Publikováno v:
Ingénierie des Systèmes d'Information. Oct2024, Vol. 29 Issue 5, p1847-1858. 12p.
Design space exploration (DSE) enables architects to systematically evaluate various design options, guiding decisions on the most suitable configurations to meet specific objectives such as optimizing performance, power, and area. However, the growi
Externí odkaz:
http://arxiv.org/abs/2410.18368