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Scalable phylogeny reconstruction with disaggregated near-memory processing

Alachiotis Nikolaos, Skrimponis Panagiotis, Pissadakis Emmanouil, Pnevmatikatos Dionysios

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/55F291BA-07D1-4EDD-80CE-14BA2CC2F080-
Αναγνωριστικόhttps://doi.org/10.1145/3484983-
Αναγνωριστικόhttps://dl.acm.org/doi/10.1145/3484983-
Γλώσσαen-
Μέγεθος32 pagesen
ΤίτλοςScalable phylogeny reconstruction with disaggregated near-memory processingen
ΔημιουργόςAlachiotis Nikolaosen
ΔημιουργόςSkrimponis Panagiotisen
ΔημιουργόςPissadakis Emmanouilen
ΔημιουργόςΠισσαδακης Εμμανουηλel
ΔημιουργόςPnevmatikatos Dionysiosen
ΔημιουργόςΠνευματικατος Διονυσιοςel
ΕκδότηςAssociation for Computing Machinery (ACM)en
ΠερίληψηDisaggregated computer architectures eliminate resource fragmentation in next-generation datacenters by enabling virtual machines to employ resources such as CPUs, memory, and accelerators that are physically located on different servers. While this paves the way for highly compute- and/or memory-intensive applications to potentially deploy all CPUs and/or memory resources in a datacenter, it poses a major challenge to the efficient deployment of hardware accelerators: input/output data can reside on different servers than the ones hosting accelerator resources, thereby requiring time- and energy-consuming remote data transfers that diminish the gains of hardware acceleration. Targeting a disaggregated datacenter architecture similar to the IBM dReDBox disaggregated datacenter prototype, the present work explores the potential of deploying custom acceleration units adjacently to the disaggregated-memory controller on memory bricks (in dReDBox terminology), which is implemented on FPGA technology, to reduce data movement and improve performance and energy efficiency when reconstructing large phylogenies (evolutionary relationships among organisms). A fundamental computational kernel is the Phylogenetic Likelihood Function (PLF), which dominates the total execution time (up to 95%) of widely used maximum-likelihood methods. Numerous efforts to boost PLF performance over the years focused on accelerating computation; since the PLF is a data-intensive, memory-bound operation, performance remains limited by data movement, and memory disaggregation only exacerbates the problem. We describe two near-memory processing models, one that addresses the problem of workload distribution to memory bricks, which is particularly tailored toward larger genomes (e.g., plants and mammals), and one that reduces overall memory requirements through memory-side data interpolation transparently to the application, thereby allowing the phylogeny size to scale to a larger number of organisms without requiring additional memory.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by-nc-sa/4.0/en
Ημερομηνία2024-02-26-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαComputer systems organization → Architecturesen
Θεματική ΚατηγορίαComputer systems organization → Reconfigurable computingen
Θεματική ΚατηγορίαHardware → Integrated circuitsen
Θεματική ΚατηγορίαHardware → Reconfigurable logic and FPGAsen
Θεματική ΚατηγορίαHardware → Reconfigurable logic applicationsen
Θεματική ΚατηγορίαDisaggregated datacenteren
Θεματική ΚατηγορίαdReDBoxen
Θεματική ΚατηγορίαNear-memory processingen
Θεματική ΚατηγορίαPhylogeneticsen
Θεματική ΚατηγορίαRAxMLen
Βιβλιογραφική ΑναφοράN. Alachiotis, P. Skrimponis, M. Pissadakis and D. Pnevmatikatos, “Scalable phylogeny reconstruction with disaggregated near-memory processing,” ACM Trans. Reconfigurable Technol. Syst., vol. 15, no. 3, 2022, doi: 10.1145/3484983. en

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