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pro vyhledávání: '"Torrellas A"'
Autor:
Ranawaka, Isuru, Hussain, Md Taufique, Block, Charles, Gerogiannis, Gerasimos, Torrellas, Josep, Azad, Ariful
We consider a sparse matrix-matrix multiplication (SpGEMM) setting where one matrix is square and the other is tall and skinny. This special variant, called TS-SpGEMM, has important applications in multi-source breadth-first search, influence maximiz
Externí odkaz:
http://arxiv.org/abs/2408.11988
The rapid evolution and widespread adoption of generative large language models (LLMs) have made them a pivotal workload in various applications. Today, LLM inference clusters receive a large number of queries with strict Service Level Objectives (SL
Externí odkaz:
http://arxiv.org/abs/2408.00741
Publikováno v:
29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2024), Volume 2, pages 582-600, La Jolla, CA, USA, May 2024
Last-level cache side-channel attacks have been mostly demonstrated in highly-controlled, quiescent local environments. Hence, it is unclear whether such attacks are feasible in a production cloud environment. In the cloud, side channels are flooded
Externí odkaz:
http://arxiv.org/abs/2405.12469
With the ubiquitous use of modern large language models (LLMs) across industries, the inference serving for these models is ever expanding. Given the high compute and memory requirements of modern LLMs, more and more top-of-the-line GPUs are being de
Externí odkaz:
http://arxiv.org/abs/2403.20306
Autor:
Lenadora, Damitha, Sathia, Vimarsh, Gerogiannis, Gerasimos, Yesil, Serif, Torrellas, Josep, Mendis, Charith
Over the years, many frameworks and optimization techniques have been proposed to accelerate graph neural networks (GNNs). Compared to the optimizations explored in these systems, we observe that different matrix re-associations of GNN computations l
Externí odkaz:
http://arxiv.org/abs/2306.15155
Side-channel attacks that use machine learning (ML) for signal analysis have become prominent threats to computer security, as ML models easily find patterns in signals. To address this problem, this paper explores using Adversarial Machine Learning
Externí odkaz:
http://arxiv.org/abs/2302.01474
Byte-addressable, non-volatile memory (NVM) is emerging as a promising technology. To facilitate its wide adoption, employing NVM in managed runtimes like JVM has proven to be an effective approach (i.e., managed NVM). However, such an approach is ru
Externí odkaz:
http://arxiv.org/abs/2205.06444
Autor:
Levites, Yona, Dammer, Eric B., Ran, Yong, Tsering, Wangchen, Duong, Duc, Abreha, Measho, Gadhavi, Joshna, Lolo, Kiara, Trejo-Lopez, Jorge, Phillips, Jennifer, Iturbe, Andrea, Erquizi, Aya, Moore, Brenda D., Ryu, Danny, Natu, Aditya, Dillon, Kristy, Torrellas, Jose, Moran, Corey, Ladd, Thomas, Afroz, Farhana, Islam, Tariful, Jagirdar, Jaishree, Funk, Cory C., Robinson, Max, Rangaraju, Srikant, Borchelt, David R., Ertekin-Taner, Nilüfer, Kelly, Jeffrey W., Heppner, Frank L., Johnson, Erik C.B., McFarland, Karen, Levey, Allan I., Prokop, Stefan, Seyfried, Nicholas T., Golde, Todd E.
Publikováno v:
In Cell Reports Medicine 20 August 2024 5(8)
Autor:
Blanco, Naiara Villalba, Vidal, Rafel Pérez, Fernández, Francisco José Vargas-Machuca, de Gracia García Ramírez, Mª, Castro, Ivan Javier, Serra, Natàlia Juan, Satorra, Rosa Maria Morera, Martorell, Sara Oduber, Huerta, Eduardo Sáez, Carrascosa, Montserrat, Soriano, Ludivina Ibañez, Smithson, Alex, Miserachs, Nuria, Blancas, David, Alonso-Tarrés, Carles, Ayuso, Elisabet Farré, González, Maria Priegue, de Ciriza Villacampa, Carmen Pérez, Fernández, María Dolores García, Martins, Marlene Àlvarez, del Río Pérez, Oscar, Riera, Ester Sanfeliu, Bertomeu, Manel Panisello, Flores, Angels García, González, Laura Linares, Pujol, Ester Comellas i, Matias, Guillem Vila, Adell, Claudia Miralles, Marcual, Jaume Llaberia, Sibat, Anna Martinez, Flotats, Elisenda, Roldan, Francina Riu, Martinez, Lorena Gaviria, de Gamarra Martínez, Edurne Fernández, Solchaga, Virginia Pomar, Luque, M. Fernanda Solano, Barque, M. Pilar Barrufet, Nicolas, Elisabeth Mauri, Fidalgo, Arantzazu Mera, Bertran, Nuria Torrellas, Gomila-Grange, Aina, Blasi, Oriol Gasch, Badia, Ester Dorca, Santamaria, Marta Andrés, Pérez-Moreno, Mar Olga, Pallares, Naya Bellaubi, González, Lidia Martín, Fernández, Magda Muelas, Padilla, Eduardo, Zorrilla, Silvia Gomez, Enguidanos, Maria Rosa Laplace, Jofre, Clara Sala, Arango, Mauricio Valencia, Pascua, Pilar Marcos, Chippiraz, Elisabet Lerma, Sureda, Teresa Falgueras, González, Melisa Barrantes, Saballs, Mireia, Taha, Mohamed Sufian Al-dirra, Guitart, Silvia Sancliment, Larrainzar-Coghen, Thais, Toboso, Sebastián Hernández, Rodriguez, Irene Sánchez, Fraile, Maria José, Torras, Sara Garcia, Guitard-Quer, Alba, Castellana-Perelló, Dolors, Sáenz, Alfredo Jover, Ramírez-Hidalgo, María, Pardo, Graciano García, Garriga, Imma Grau, Palau, Damaris Berbel, Moral, Alícia, Vilamala, Anna, Montal, Camil·la Valls, Navarro, Maria, Valls, Mariona Xercavins, Anaya, Gisela Cuadrado, Ochoa, Ivett Suárez, Matellanes, Julen Montoya, Trevisanello, Lucia, Segarra, Glòria Garcia, Prieto, Natacha Recio, Azcona, Ana Felisa Lopez, Iftimie, Simona Mihaella, Jaime, Laura Cabrera, Margall, Nuri Quer, Laporte, Júlia, González, Carlota Gudiol, Sanmartí, Montserrat, Diaz-Brito, Vicens, Belda, Alejandro Sanjuan, Sanz, Marta Milián, Giménez-Pérez, Montserrat, Hernández, Sergi, Padullés, Ariadna, Boix-Palop, Lucía, Grau, Santiago, Badia, Josep M., Ferrer, Ricard, Calbo, Esther, Limón, Enric, Pujol, Miquel, Horcajada, Juan P.
Publikováno v:
In International Journal of Antimicrobial Agents August 2024 64(2)
Autor:
Kokolis, Apostolos, Mantri, Namrata, Ganapathy, Shrikanth, Torrellas, Josep, Kalamatianos, John
The increased memory demands of workloads is putting high pressure on Last Level Caches (LLCs). Unfortunately, there is limited opportunity to increase the capacity of LLCs due to the area and power requirements of the underlying SRAM technology. Int
Externí odkaz:
http://arxiv.org/abs/2112.10632