4 papers
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…
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…