Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Oren, Gal"'
Autor:
Kadosh, Tal, Hasabnis, Niranjan, Soundararajan, Prema, Vo, Vy A., Capota, Mihai, Ahmed, Nesreen, Pinter, Yuval, Oren, Gal
Manual parallelization of code remains a significant challenge due to the complexities of modern software systems and the widespread adoption of multi-core architectures. This paper introduces OMPar, an AI-driven tool designed to automate the paralle
Externí odkaz:
http://arxiv.org/abs/2409.14771
Neutronic calculations for reactors are a daunting task when using Monte Carlo (MC) methods. As high-performance computing has advanced, the simulation of a reactor is nowadays more readily done, but design and optimization with multiple parameters i
Externí odkaz:
http://arxiv.org/abs/2403.14273
Monte Carlo (MC) simulations play a pivotal role in diverse scientific and engineering domains, with applications ranging from nuclear physics to materials science. Harnessing the computational power of high-performance computing (HPC) systems, espec
Externí odkaz:
http://arxiv.org/abs/2403.02735
Autor:
Schneider, Nadav, Hasabnis, Niranjan, Vo, Vy A., Kadosh, Tal, Krien, Neva, Capotă, Mihai, Tamir, Guy, Willke, Ted, Ahmed, Nesreen, Pinter, Yuval, Mattson, Timothy, Oren, Gal
The imperative need to scale computation across numerous nodes highlights the significance of efficient parallel computing, particularly in the realm of Message Passing Interface (MPI) integration. The challenging parallel programming task of generat
Externí odkaz:
http://arxiv.org/abs/2402.09126
Autor:
Chen, Le, Ahmed, Nesreen K., Dutta, Akash, Bhattacharjee, Arijit, Yu, Sixing, Mahmud, Quazi Ishtiaque, Abebe, Waqwoya, Phan, Hung, Sarkar, Aishwarya, Butler, Branden, Hasabnis, Niranjan, Oren, Gal, Vo, Vy A., Munoz, Juan Pablo, Willke, Theodore L., Mattson, Tim, Jannesari, Ali
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques have shown immense potential for natural language processing and code-ba
Externí odkaz:
http://arxiv.org/abs/2402.02018
Autor:
Chen, Le, Bhattacharjee, Arijit, Ahmed, Nesreen, Hasabnis, Niranjan, Oren, Gal, Vo, Vy, Jannesari, Ali
Large language models (LLMs)such as ChatGPT have significantly advanced the field of Natural Language Processing (NLP). This trend led to the development of code-based large language models such as StarCoder, WizardCoder, and CodeLlama, which are tra
Externí odkaz:
http://arxiv.org/abs/2401.16445
Autor:
Kadosh, Tal, Hasabnis, Niranjan, Vo, Vy A., Schneider, Nadav, Krien, Neva, Capota, Mihai, Wasay, Abdul, Ahmed, Nesreen, Willke, Ted, Tamir, Guy, Pinter, Yuval, Mattson, Timothy, Oren, Gal
With easier access to powerful compute resources, there is a growing trend in AI for software development to develop large language models (LLMs) to address a variety of programming tasks. Even LLMs applied to tasks from the high-performance computin
Externí odkaz:
http://arxiv.org/abs/2312.13322
Autor:
Chen, Le, Bhattacharjee, Arijit, Ahmed, Nesreen K., Hasabnis, Niranjan, Oren, Gal, Lei, Bin, Jannesari, Ali
Large language models (LLMs) have become increasingly prominent in academia and industry due to their remarkable performance in diverse applications. As these models evolve with increasing parameters, they excel in tasks like sentiment analysis and m
Externí odkaz:
http://arxiv.org/abs/2311.06505
In the landscape of High-Performance Computing (HPC), the quest for efficient and scalable memory solutions remains paramount. The advent of Compute Express Link (CXL) introduces a promising avenue with its potential to function as a Persistent Memor
Externí odkaz:
http://arxiv.org/abs/2308.10714
Autor:
Kadosh, Tal, Hasabnis, Niranjan, Vo, Vy A., Schneider, Nadav, Krien, Neva, Wasay, Abdul, Ahmed, Nesreen, Willke, Ted, Tamir, Guy, Pinter, Yuval, Mattson, Timothy, Oren, Gal
With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks. Even LLMs applied to tasks from the
Externí odkaz:
http://arxiv.org/abs/2308.09440