Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Buluç, Aydın"'
Autor:
Jananthan, Hayden, Jones, Michael, Arcand, William, Bestor, David, Bergeron, William, Burrill, Daniel, Buluc, Aydin, Byun, Chansup, Davis, Timothy, Gadepally, Vijay, Grant, Daniel, Houle, Michael, Hubbell, Matthew, Luszczek, Piotr, Michaleas, Peter, Milechin, Lauren, Milner, Chasen, Morales, Guillermo, Morris, Andrew, Mullen, Julie, Patel, Ritesh, Pentland, Alex, Pisharody, Sandeep, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Wachman, Gabriel, Yee, Charles, Kepner, Jeremy
The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold i
Externí odkaz:
http://arxiv.org/abs/2409.08115
Autor:
Kepner, Jeremy, Jananthan, Hayden, Jones, Michael, Arcand, William, Bestor, David, Bergeron, William, Burrill, Daniel, Buluc, Aydin, Byun, Chansup, Davis, Timothy, Gadepally, Vijay, Grant, Daniel, Houle, Michael, Hubbell, Matthew, Luszczek, Piotr, Milechin, Lauren, Milner, Chasen, Morales, Guillermo, Morris, Andrew, Mullen, Julie, Patel, Ritesh, Pentland, Alex, Pisharody, Sandeep, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Wachman, Gabriel, Yee, Charles, Michaleas, Peter
Understanding what is normal is a key aspect of protecting a domain. Other domains invest heavily in observational science to develop models of normal behavior to better detect anomalies. Recent advances in high performance graph libraries, such as t
Externí odkaz:
http://arxiv.org/abs/2409.03111
Autor:
Hong, Yuxi, Buluc, Aydin
Multiplying two sparse matrices (SpGEMM) is a common computational primitive used in many areas including graph algorithms, bioinformatics, algebraic multigrid solvers, and randomized sketching. Distributed-memory parallel algorithms for SpGEMM have
Externí odkaz:
http://arxiv.org/abs/2408.14558
Publikováno v:
Proceedings of the 38th ACM International Conference on Supercomputing (ICS 2024) 225-235
Sparse matrix multiplication is an important kernel for large-scale graph processing and other data-intensive applications. In this paper, we implement various asynchronous, RDMA-based sparse times dense (SpMM) and sparse times sparse (SpGEMM) algori
Externí odkaz:
http://arxiv.org/abs/2311.18141
Graph Neural Networks (GNNs) offer a compact and computationally efficient way to learn embeddings and classifications on graph data. GNN models are frequently large, making distributed minibatch training necessary. The primary contribution of this p
Externí odkaz:
http://arxiv.org/abs/2311.02909
This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly ran
Externí odkaz:
http://arxiv.org/abs/2310.15419
This paper introduces the batch-parallel Compressed Packed Memory Array (CPMA), a compressed, dynamic, ordered set data structure based on the Packed Memory Array (PMA). Traditionally, batch-parallel sets are built on pointer-based data structures su
Externí odkaz:
http://arxiv.org/abs/2305.05055
Dedicated accelerator hardware has become essential for processing AI-based workloads, leading to the rise of novel accelerator architectures. Furthermore, fundamental differences in memory architecture and parallelism have made these accelerators ta
Externí odkaz:
http://arxiv.org/abs/2304.08662
Autor:
Selvitopi, Oguz, Ekanayake, Saliya, Guidi, Giulia, Awan, Muaaz G., Pavlopoulos, Georgios A., Azad, Ariful, Kyrpides, Nikos, Oliker, Leonid, Yelick, Katherine, Buluç, Aydın
Publikováno v:
SC'22: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2022
Similarity search is one of the most fundamental computations that are regularly performed on ever-increasing protein datasets. Scalability is of paramount importance for uncovering novel phenomena that occur at very large scales. We unleash the powe
Externí odkaz:
http://arxiv.org/abs/2303.01845
Autor:
Bharadwaj, Vivek, Malik, Osman Asif, Murray, Riley, Grigori, Laura, Buluc, Aydin, Demmel, James
We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler draws each row in time logarithmic in the height of the Khatri-Rao pr
Externí odkaz:
http://arxiv.org/abs/2301.12584