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Josue V. Quiroga

dblp:201/6246 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-1840-2855ORCID · corroborated

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

Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author

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
Processor architecture and microarchitecture · 74% Hardware accelerators and domain-specific architectures · 20% High-performance computing · 6%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Processor architecture and microarchitecture
instruction set architecture
0.712023
Vitruvius+: An Area-Efficient RISC-V Decoupled Vector Coprocessor for High Performance Computing Applications · ACM Trans. Archit. Code Optim. 2023
Processor architecture and microarchitecture › instruction set architecture
RISC-V
0.712023
Vitruvius+: An Area-Efficient RISC-V Decoupled Vector Coprocessor for High Performance Computing Applications · ACM Trans. Archit. Code Optim. 2023
Processor architecture and microarchitecture › instruction set architecture
vector extension
0.712023
Vitruvius+: An Area-Efficient RISC-V Decoupled Vector Coprocessor for High Performance Computing Applications · ACM Trans. Archit. Code Optim. 2023
Processor architecture and microarchitecture
out-of-order execution
0.212023
Vitruvius+: An Area-Efficient RISC-V Decoupled Vector Coprocessor for High Performance Computing Applications · ACM Trans. Archit. Code Optim. 2023
Processor architecture and microarchitecture › out-of-order execution
register renaming
0.212023
Vitruvius+: An Area-Efficient RISC-V Decoupled Vector Coprocessor for High Performance Computing Applications · ACM Trans. Archit. Code Optim. 2023
YearPublicationVenuePosition
2023 Vitruvius+: An Area-Efficient RISC-V Decoupled Vector Coprocessor for High Performance Computing Applications
abstract
The maturity level of RISC-V and the availability of domain-specific instruction set extensions, like vector processing, make RISC-V a good candidate for supporting the integration of specialized hardware in processor cores for the High Performance Computing (HPC) application domain. In this article, 1 we present Vitruvius+, the vector processing acceleration engine that represents the core of vector instruction execution in the HPC challenge that comes within the EuroHPC initiative. It implements the RISC-V vector extension (RVV) 0.7.1 and can be easily connected to a scalar core using the Open Vector Interface standard. Vitruvius+ natively supports long vectors: 256 double precision floating-point elements in a single vector register. It is composed of a set of identical vector pipelines (lanes), each containing a slice of the Vector Register File and functional units (one integer, one floating point). The vector instruction execution scheme is hybrid in-order/out-of-order and is supported by register renaming and arithmetic/memory instruction decoupling. On a stand-alone synthesis, Vitruvius+ reaches a maximum frequency of 1.4 GHz in typical conditions (TT/0.80V/25°C) using GlobalFoundries 22FDX FD-SOI. The silicon implementation has a total area of 1.3 mm 2 and maximum estimated power of ∼920 mW for one instance of Vitruvius+ equipped with eight vector lanes.
Francesco Minervini, Oscar Palomar, Osman S. Unsal, Enrico Reggiani, Josue V. Quiroga, Joan Marimon, Carlos Rojas 0001, Roger Figueras, Abraham Ruiz, Alberto González 0004, Jonnatan Mendoza, Iván Vargas 0001, César Hernández, Joan Cabre, Lina Khoirunisya, Mustapha Bouhali, Julian Pavon, Francesc Moll, Mauro Olivieri, Mario Kovac, Mate Kovac, Leon Dragic, Mateo Valero, Adrián Cristal
ACM Trans. Archit. Code Optim.5
2019 Evaluation of a Rack-Scale Disaggregated Memory Prototype for Cloud Data Centers
abstract
Disaggregated data centers propose a modular architecture where memory and compute resources are utilized in a finer granularity, aiming for a better optimized capacity and power consumption. In contrast to a classic compute node in a data center, memory is not required to be co-located with the processor, disaggregation can enhance hardware elasticity, improve virtual machine (VM) migration, and reduce the total cost of ownership (TCO), when compared to current data center solutions.
Josue V. Quiroga, Martí Torrents, Nehir Sönmez, Dimitris Theodoropoulos 0001, Ferad Zyulkyarov, Mario Nemirovsky
RSP1