17 citations · 44 across the 8 of their papers we have counts for
9 papers
HyperJump: Accelerating HyperBand via Risk Modelling
Pedro Mendes, Maria Casimiro, Paolo Romano +1
In the literature on hyper-parameter tuning, a number of recent solutions rely on low-fidelity observations (e.g., training with sub-sampled datasets) in order to efficiently ident…
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling
Pedro Mendes, Maria Casimiro, Paolo Romano +1
This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process…
Stretching the capacity of Hardware Transactional Memory in IBM POWER architectures
Ricardo Filipe, Shady Issa, Paolo Romano +1
The hardware transactional memory (HTM) implementations in commercially available processors are significantly hindered by their tight capacity constraints. In practice, this rende…
Bandwidth-Aware Page Placement in NUMA
David Gureya, João Neto, Reza Karimi +6
Page placement is a critical problem for memoryintensive applications running on a shared-memory multiprocessor with a non-uniform memory access (NUMA) architecture. State-of-the-a…
Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs
Maria Casimiro, Diego Didona, Paolo Romano +3
Modern data analytic and machine learning jobs find in the cloud a natural deployment platform to satisfy their notoriously large resource requirements. Yet, to achieve cost effici…
HeTM: Transactional Memory for Heterogeneous Systems
Daniel Castro, Paolo Romano, Aleksandar Ilic +1
Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak perf…
A Flexible Framework for Accurate Simulation of Cloud In-Memory Data Stores
Pierangelo Di Sanzo, Francesco Quaglia, Bruno Ciciani +5
In-memory (transactional) data stores are recognized as a first-class data management technology for cloud platforms, thanks to their ability to match the elasticity requirements i…
On Bootstrapping Machine Learning Performance Predictors via Analytical Models
Diego Didona, Paolo Romano
Performance modeling typically relies on two antithetic methodologies: white box models, which exploit knowledge on system's internals and capture its dynamics using analytical app…
Exploiting Locality in Lease-Based Replicated Transactional Memory via Task Migration
Danny Hendler, Alex Naiman, Sebastiano Peluso +3
We present Lilac-TM, the first locality-aware Distributed Software Transactional Memory (DSTM) implementation. Lilac-TM is a fully decentralized lease-based replicated DSTM. It emp…