Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Hasabnis, Niranjan"'
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
Domain-specific languages (DSLs) are integral to various software workflows. Such languages offer domain-specific optimizations and abstractions that improve code readability and maintainability. However, leveraging these languages requires developer
Externí odkaz:
http://arxiv.org/abs/2406.03003
Tensor processing infrastructures such as deep learning frameworks and specialized hardware accelerators have revolutionized how computationally intensive code from domains such as deep learning and image processing is executed and optimized. These i
Externí odkaz:
http://arxiv.org/abs/2404.18249
Autor:
Golin, Renato, Chelini, Lorenzo, Siemieniuk, Adam, Madhu, Kavitha, Hasabnis, Niranjan, Pabst, Hans, Georganas, Evangelos, Heinecke, Alexander
This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance from a generic linear algebra high-level abstraction. We demonstrate this flow with a proof-of-concept MLIR project that uses in
Externí odkaz:
http://arxiv.org/abs/2404.15204
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
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