Antonino Bonanni

dblp:252/4616 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 77% High-performance computing · 18% Storage systems · 5%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
memory-bound computation
0.412019
An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications · SC 2019
Memory systems
non-volatile memory
0.412019
An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications · SC 2019
Memory systems › non-volatile memory › persistent memory
optane persistent memory
0.412019
An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications · SC 2019
Memory systems › non-volatile memory
persistent memory
0.412019
An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications · SC 2019
High-performance computing
scientific computing systems
0.412019
An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications · SC 2019
Storage systems › object storage
distributed object store
0.112019
An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications · SC 2019
Memory systems › non-volatile memory
NVRAM
0.112019
An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications · SC 2019

Methods — techniques the papers use, named apart from their topics

performance evaluation · 0.4STREAM benchmark · 0.4
YearPublicationVenuePosition
2019 An early evaluation of Intel's optane DC persistent memory module and its impact on high-performance scientific applications
abstract
Memory and I/O performance bottlenecks in supercomputing simulations are two key challenges that must be addressed on the road to Exascale. The new byte-addressable persistent non-volatile memory technology from Intel, DCPMM, promises to be an exciting opportunity to break with the status quo, with unprecedented levels of capacity at near-DRAM speeds. Here, we explore the potential of DCPMM in the context of two high-performance scientific applications in terms of outright performance, efficiency and usability for both its Memory and App Direct modes. In Memory mode, we show equivalent performance and better efficiency for a CASTEP simulation that is limited by memory capacity on conventional DRAM-only systems without any changes to the application. For IFS, we demonstrate that a distributed object-store over NVRAM reduces the data contention created in weather forecasting data producer-consumer workflows. In addition, we also present the achievable memory bandwidth performance using STREAM.
Michèle Weiland, Holger Brunst, Tiago Quintino, Nick Johnson, Olivier Iffrig, Simon D. Smart, Christian Herold, Antonino Bonanni, Adrian Jackson, Mark Parsons 0001
SC8