8 citations · 35 across the 10 of their papers we have counts for
5 papers · 1 filter
Towards a General Framework for ML-based Self-tuning Databases
Thomas Schmied, Diego Didona, Andreas Döring +2
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimizatio…
Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs
Diego Didona, Nikolas Ioannou, Radu Stoica +1
Solid-state drives (SSDs) are extensively used to deploy persistent data stores, as they provide low latency random access, high write throughput, high data density, and low cost.…
SnapBoost: A Heterogeneous Boosting Machine
Thomas Parnell, Andreea Anghel, Malgorzata Lazuka +5
Modern gradient boosting software frameworks, such as XGBoost and LightGBM, implement Newton descent in a functional space. At each boosting iteration, their goal is to find the ba…
Compiling Neural Networks for a Computational Memory Accelerator
Kornilios Kourtis, Martino Dazzi, Nikolas Ioannou +3
Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computation…
Safe and Efficient Remote Application Code Execution on Disaggregated NVM Storage with eBPF
Kornilios Kourtis, Animesh Trivedi, Nikolas Ioannou
With rapid improvements in NVM storage devices, the performance bottleneck is gradually shifting to the network, thus giving rise to the notion of "data movement wall". To reduce t…