ڞ༗ετϨʔδ ఏҊख๏ w ఆ௨Γɼ࠷ετϨʔδσόΠεͱͯ͠ͷੑೳ͕ߴ͍44%͕࠷ߴ w ఏҊख๏ڞ༗ετϨʔδΑΓߴͳ݁Ռ͕ಘΒΕɼϓϦϑΣονͷޮՌ͕֬ ೝͰ͖Δ w ڞ༗ετϨʔδͱఏҊख๏Λൺֱ͢ΔͱɼఏҊख๏ͷํ͕ΑΓগͳ͍ϓϩηε Ͱڞ༗ετϨʔδΑΓߴ͍SFBE*0Λୡ͍ͯ͠Δ 16
ʢ113ݻఆʣ w Ϟσϧ3FT/FUͱ͍͏ΈࠐΈχϡʔϥϧωοτϫʔΫͷҰछΛ༻ɼ༻ ͢ΔσʔλɼϛχόοναΠζධՁ࣮ݧ̍ͱಉ͡ 17 w ˞༧ߘʹܝࡌͨ͠ʮFQPDIσʔλฒྻ܇࿅ʯͷධՁ݁ՌͱҎԼͷ͕ҟͳΓ·͢ w ॲཧ։࢝લʹESPQDBDIFTΛ༻͍ͯ1BHF$BDIFΛআ w ܇࿅͢ΔFQPDIΛ͔ΒʹมߋʢFQPDIҎ߱ͷ݁ՌʹมԽ͕ͳ͔ͬͨͨΊʣ w ίʔυͷ࠷దԽʢQSJOUσόοάʹΑΔΦʔόʔϔουΛۃྗআʣ
Y., Chowdhury, F.,et al. “Entropy-Aware I/O Pipelining for Large-Scale Deep Learning on HPC Systems”, MASCOTS.2018.00023, pp.145-146 (2018) [2] X. Lu, H. Shi, M. H. Javed,et al. “Characterizing Deep Learning over Big Data (DLoBD) Stacks on RDMA-Capable Networks," 2017 IEEE 25th Annual Symposium on High- Performance Interconnects (HOTI), pp. 87-94 (2017) 25