Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Capota, Mihai"'
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
Autor:
Duan, Shukai, Ping, Heng, Kanakaris, Nikos, Xiao, Xiongye, Zhang, Peiyu, Kyriakis, Panagiotis, Ahmed, Nesreen K., Ma, Guixiang, Capota, Mihai, Nazarian, Shahin, Willke, Theodore L., Bogdan, Paul
Existing approaches for device placement ignore the topological features of computation graphs and rely mostly on heuristic methods for graph partitioning. At the same time, they either follow a grouper-placer or an encoder-placer architecture, which
Externí odkaz:
http://arxiv.org/abs/2405.14185
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:
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:
Duan, Shukai, Kanakaris, Nikos, Xiao, Xiongye, Ping, Heng, Zhou, Chenyu, Ahmed, Nesreen K., Ma, Guixiang, Capota, Mihai, Willke, Theodore L., Nazarian, Shahin, Bogdan, Paul
Code optimization is a daunting task that requires a significant level of expertise from experienced programmers. This level of expertise is not sufficient when compared to the rapid development of new hardware architectures. Towards advancing the wh
Externí odkaz:
http://arxiv.org/abs/2312.05657
Autor:
Xiao, Yao, Ma, Guixiang, Ahmed, Nesreen K., Capota, Mihai, Willke, Theodore, Nazarian, Shahin, Bogdan, Paul
To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that is capable of mining the complexity of high-level p
Externí odkaz:
http://arxiv.org/abs/2204.11981
Autor:
Iosup, Alexandru, Musaafir, Ahmed, Uta, Alexandru, Pérez, Arnau Prat, Szárnyas, Gábor, Chafi, Hassan, Tănase, Ilie Gabriel, Nai, Lifeng, Anderson, Michael, Capotă, Mihai, Sundaram, Narayanan, Boncz, Peter, Depner, Siegfried, Heldens, Stijn, Manhardt, Thomas, Hegeman, Tim, Ngai, Wing Lung, Xia, Yinglong
In this document, we describe LDBC Graphalytics, an industrial-grade benchmark for graph analysis platforms. The main goal of Graphalytics is to enable the fair and objective comparison of graph analysis platforms. Due to the diversity of bottlenecks
Externí odkaz:
http://arxiv.org/abs/2011.15028
Autor:
Turek, Javier S., Jain, Shailee, Vo, Vy, Capota, Mihai, Huth, Alexander G., Willke, Theodore L.
Recent work has shown that topological enhancements to recurrent neural networks (RNNs) can increase their expressiveness and representational capacity. Two popular enhancements are stacked RNNs, which increases the capacity for learning non-linear f
Externí odkaz:
http://arxiv.org/abs/1909.00021
Autor:
Anderson, Michael J., Capotă, Mihai, Turek, Javier S., Zhu, Xia, Willke, Theodore L., Wang, Yida, Chen, Po-Hsuan, Manning, Jeremy R., Ramadge, Peter J., Norman, Kenneth A.
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has le
Externí odkaz:
http://arxiv.org/abs/1608.04647
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