HOEFLER, Torsten, BONOTO, Tommaso, DE SENSI, Daniele, DI GIROLAMO, Salvatore, LI, Shigang, HEDDES, Marco, GOEL, Deepak, CASTRO, Miguel und SCOTT, Steve, 2024. Hamming Mesh: A Network Topology for Large-Scale Deep Learning. Communications of the ACM. 1 Dezember 2024. Vol. 67, no. 12, p. 97-105. DOI 10.1145/3623490.
Elsevier - Harvard (with titles)Hoefler, T., Bonoto, T., De Sensi, D., Di Girolamo, S., Li, S., Heddes, M., Goel, D., Castro, M., Scott, S., 2024. Hamming Mesh: A Network Topology for Large-Scale Deep Learning. Communications of the ACM 67, 97-105. https://doi.org/10.1145/3623490
American Psychological Association 7th editionHoefler, T., Bonoto, T., De Sensi, D., Di Girolamo, S., Li, S., Heddes, M., Goel, D., Castro, M., & Scott, S. (2024). Hamming Mesh: A Network Topology for Large-Scale Deep Learning. Communications of the ACM, 67(12), 97-105. https://doi.org/10.1145/3623490
Springer - Basic (author-date)Hoefler T, Bonoto T, De Sensi D, Di Girolamo S, Li S, Heddes M, Goel D, Castro M, Scott S (2024) Hamming Mesh: A Network Topology for Large-Scale Deep Learning.. Communications of the ACM 67:97-105. https://doi.org/10.1145/3623490
Juristische Zitierweise (Stüber) (Deutsch)Hoefler, Torsten/ Bonoto, Tommaso/ De Sensi, Daniele/ Di Girolamo, Salvatore/ Li, Shigang/ Heddes, Marco/ Goel, Deepak/ Castro, Miguel/ Scott, Steve, Hamming Mesh: A Network Topology for Large-Scale Deep Learning., Communications of the ACM 2024, 97-105.