Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Houle, Michael"'
Autor:
Byun, Chansup, Mullen, Julia, Reuther, Albert, Arcand, William, Bergeron, William, Bestor, David, Burrill, Daniel, Gadepally, Vijay, Houle, Michael, Hubbell, Matthew, Jananthan, Hayden, Jones, Michael, Michaleas, Peter, Morales, Guillermo, Prout, Andrew, Rosa, Antonio, Yee, Charles, Kepner, Jeremy, Milechin, Lauren
One of the more complex tasks for researchers using HPC systems is performance monitoring and tuning of their applications. Developing a practice of continuous performance improvement, both for speed-up and efficient use of resources is essential to
Externí odkaz:
http://arxiv.org/abs/2407.01481
Autor:
Huang, Hanxun, Campello, Ricardo J. G. B., Erfani, Sarah Monazam, Ma, Xingjun, Houle, Michael E., Bailey, James
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.
Externí odkaz:
http://arxiv.org/abs/2401.10474
Autor:
Anderberg, Alastair, Bailey, James, Campello, Ricardo J. G. B., Houle, Michael E., Marques, Henrique O., Radovanović, Miloš, Zimek, Arthur
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset. Using the theory of Local Intrinsic Dimensionality (LID), our 'dimensionality-aware' outlier detection
Externí odkaz:
http://arxiv.org/abs/2401.05453
Autor:
Jananthan, Hayden, Kepner, Jeremy, Jones, Michael, Arcand, William, Bestor, David, Bergeron, William, Byun, Chansup, Davis, Timothy, Gadepally, Vijay, Grant, Daniel, Houle, Michael, Hubbell, Matthew, Klein, Anna, Milechin, Lauren, Morales, Guillermo, Morris, Andrew, Mullen, Julie, Patel, Ritesh, Pentland, Alex, Pisharody, Sandeep, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Trigg, Tyler, Wachman, Gabriel, Yee, Charles, Michaleas, Peter
Expanding the scientific tools available to protect computer networks can be aided by a deeper understanding of the underlying statistical distributions of network traffic and their potential geometric interpretations. Analyses of large scale network
Externí odkaz:
http://arxiv.org/abs/2310.00522
Autor:
Byun, Chansup, Arcand, William, Bestor, David, Bergeron, Bill, Gadepally, Vijay, Houle, Michael, Hubbell, Matthew, Jananthan, Hayden, Jones, Michael, Klein, Anna, Michaleas, Peter, Milechin, Lauren, Morales, Guillermo, Mullen, Julie, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Yee, Charles, Kepner, Jeremy
pPython seeks to provide a parallel capability that provides good speed-up without sacrificing the ease of programming in Python by implementing partitioned global array semantics (PGAS) on top of a simple file-based messaging library (PythonMPI) in
Externí odkaz:
http://arxiv.org/abs/2309.03931
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis
Autor:
Amsaleg, Laurent, Chelly, Oussama, Houle, Michael E., Kawarabayashi, Ken-ichi, Radovanović, Miloš, Treeratanajaru, Weeris
Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection, similarity search and subspace clustering. However, since their conve
Externí odkaz:
http://arxiv.org/abs/2209.14475
Autor:
Jananthan, Hayden, Milechin, Lauren, Jones, Michael, Arcand, William, Bergeron, William, Bestor, David, Byun, Chansup, Houle, Michael, Hubbell, Matthew, Gadepally, Vijay, Klein, Anna, Michaleas, Peter, Morales, Guillermo, Mullen, Julie, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Yee, Charles, Kepner, Jeremy
Python has become a standard scientific computing language with fast-growing support of machine learning and data analysis modules, as well as an increasing usage of big data. The Dynamic Distributed Dimensional Data Model (D4M) offers a highly compo
Externí odkaz:
http://arxiv.org/abs/2209.00602
Autor:
Byun, Chansup, Arcand, William, Bestor, David, Bergeron, Bill, Gadepally, Vijay, Houle, Michael, Hubbell, Matthew, Jananthan, Hayden, Jones, Michael, Keville, Kurt, Klein, Anna, Michaleas, Peter, Milechin, Lauren, Morales, Guillermo, Mullen, Julie, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Yee, Charles, Kepner, Jeremy
pPython seeks to provide a parallel capability that provides good speed-up without sacrificing the ease of programming in Python by implementing partitioned global array semantics (PGAS) on top of a simple file-based messaging library (PythonMPI) in
Externí odkaz:
http://arxiv.org/abs/2208.14908
Autor:
Kepner, Jeremy, Cho, Kenjiro, Claffy, KC, Gadepally, Vijay, McGuire, Sarah, Milechin, Lauren, Arcand, William, Bestor, David, Bergeron, William, Byun, Chansup, Hubbell, Matthew, Houle, Michael, Jones, Michael, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Yee, Charles, Michaleas, Peter
The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest publicly available Internet traffic data sets. An analysis of 50 billion packets using 10,000 processo
Externí odkaz:
http://arxiv.org/abs/2201.06096
Autor:
Bergeron, Bill, Hubbell, Matthew, Sequeira, Dylan, Williams, Winter, Arcand, William, Bestor, David, Chansup, Byun, Gadepally, Vijay, Houle, Michael, Jones, Michael, Klien, Anna, Michaleas, Peter, Milechin, Lauren, Prout, Julie Mullen Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Yee, Charles, Kepner, Jeremy
Supercomputers are complex systems producing vast quantities of performance data from multiple sources and of varying types. Performance data from each of the thousands of nodes in a supercomputer tracks multiple forms of storage, memory, networks, p
Externí odkaz:
http://arxiv.org/abs/2109.04532