Architecture of Computing Systems - ARCS 2019 32nd International Conference, Copenhagen, Denmark, May 20-23, 2019, Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Schoeberl, Martin (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Hochberger, Christian (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Uhrig, Sascha (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Brehm, Jürgen (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Pionteck, Thilo (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Theoretical Computer Science and General Issues ; 11479
• Θέματα: Computer communication systems.; Operating systems (Computers).; Logic design.; Architecture, Computer.; Input-output equipment (Computers).; Microprocessors.; Computer Communication Networks.; Operating Systems.; Logic Design.; Computer System Implementation.; Input/Output and Data Communications.; Processor Architectures.
• Φυσική περιγραφή: XIX, 335 p. 212 illus., 88 illus. in color. online resource.
• ISBN: 9783030186562
• DOI: 10.1007/978-3-030-18656-2

This book constitutes the proceedings of the 32nd International Conference on Architecture of Computing Systems, ARCS 2019, held in Copenhagen, Denmark, in May 2019. The 24 full papers presented in this volume were carefully reviewed and selected from 40 submissions. ARCS has always been a conference attracting leading-edge research outcomes in Computer Architecture and Operating Systems, including a wide spectrum of topics ranging from embedded and real-time systems all the way to large-scale and parallel systems. The selected papers are organized in the following topical sections: Dependable systems; real-time systems; special applications; architecture; memory hierarchy; FPGA; energy awareness; NoC/SoC. The chapter 'MEMPower: Data-Aware GPU Memory Power Model' is open access under a CC BY 4.0 license at link.springer.com.